�ݺ�ߣshows by User: JanEiteBullema

�ݺ�ߣshows by User: JanEiteBullema / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: JanEiteBullema / Fri, 14 May 2021 09:24:32 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: JanEiteBullema 2018 Example of a Digital Twin for 3 D printing /slideshow/2018-example-of-a-digital-twin-for-3-d-printing/248312164 2018exampleofadigitaltwinfor3dprinting-210514092432
For the WATIFY seminar 20 april 2018 I presented this first builfd of a Digital Twin for a 3D Printer. Advanced manufacturing is the use of innovative technology to improve products or processes. An important innovative technology is additive manufacturing or 3D printing. In this webinar some practical examples are given how digitization is used to improve 3D printing: 1) e-supply chain tools for additive manufacturing, 2) automated root cause analyses of printing defects, 3) use of deep learning towards Zero Defects. ]]>
For the WATIFY seminar 20 april 2018 I presented this first builfd of a Digital Twin for a 3D Printer. Advanced manufacturing is the use of innovative technology to improve products or processes. An important innovative technology is additive manufacturing or 3D printing. In this webinar some practical examples are given how digitization is used to improve 3D printing: 1) e-supply chain tools for additive manufacturing, 2) automated root cause analyses of printing defects, 3) use of deep learning towards Zero Defects. ]]> Fri, 14 May 2021 09:24:32 GMT /slideshow/2018-example-of-a-digital-twin-for-3-d-printing/248312164 JanEiteBullema@slideshare.net(JanEiteBullema) 2018 Example of a Digital Twin for 3 D printing JanEiteBullema For the WATIFY seminar 20 april 2018 I presented this first builfd of a Digital Twin for a 3D Printer. Advanced manufacturing is the use of innovative technology to improve products or processes. An important innovative technology is additive manufacturing or 3D printing. In this webinar some practical examples are given how digitization is used to improve 3D printing: 1) e-supply chain tools for additive manufacturing, 2) automated root cause analyses of printing defects, 3) use of deep learning towards Zero Defects. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2018exampleofadigitaltwinfor3dprinting-210514092432-thumbnail.jpg?width=120&height=120&fit=bounds" /> For the WATIFY seminar 20 april 2018 I presented this first builfd of a Digital Twin for a 3D Printer. Advanced manufacturing is the use of innovative technology to improve products or processes. An important innovative technology is additive manufacturing or 3D printing. In this webinar some practical examples are given how digitization is used to improve 3D printing: 1) e-supply chain tools for additive manufacturing, 2) automated root cause analyses of printing defects, 3) use of deep learning towards Zero Defects.

2018 Example of a Digital Twin for 3 D printing from Jan Eite Bullema

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0 2017 Electrical interconnects in miro fluidics /slideshow/2017-electrical-interconnects-in-miro-fluidics/231693224 mfmanufacturingworkshopelectricalinterconnectsinmirofluidicsimec2017-200409074653
The last few years microfluidics stopped being a niche technology,with a user base predominantly consisting of engineers. Most of the microfluidic companies now are growing and the install base of instruments based on microfluidics is growing fast. Still, the situation is far from ideal. Designs are unnecessary complicated, there is little to no reuse of build-up expertise or developed components. Similar to the early computerindustry,amajor reason for the low popularity is the complicated character of microfluidic devices, specifically in terms of fabrication, and thusmaking theminaccessible to a larger population.[1]I n the ECSEL MFM project first steps have been made towards developingstandards for microfluidic devices. Standards for basic design features like geometrical outlines and port locations have been proposed inwhite papers[2]and where adopted by ISO in an ISO IWA process.[3]One of the complications of microfluidic products is the challenge of providing electrical connections. The average microfluidic engineer lacks electronicpackaging knowledge. Furthermore, the incompatibility of microfluidics and electronics combined with space constrains, limits the technology choices.]]>
The last few years microfluidics stopped being a niche technology,with a user base predominantly consisting of engineers. Most of the microfluidic companies now are growing and the install base of instruments based on microfluidics is growing fast. Still, the situation is far from ideal. Designs are unnecessary complicated, there is little to no reuse of build-up expertise or developed components. Similar to the early computerindustry,amajor reason for the low popularity is the complicated character of microfluidic devices, specifically in terms of fabrication, and thusmaking theminaccessible to a larger population.[1]I n the ECSEL MFM project first steps have been made towards developingstandards for microfluidic devices. Standards for basic design features like geometrical outlines and port locations have been proposed inwhite papers[2]and where adopted by ISO in an ISO IWA process.[3]One of the complications of microfluidic products is the challenge of providing electrical connections. The average microfluidic engineer lacks electronicpackaging knowledge. Furthermore, the incompatibility of microfluidics and electronics combined with space constrains, limits the technology choices.]]> Thu, 09 Apr 2020 07:46:53 GMT /slideshow/2017-electrical-interconnects-in-miro-fluidics/231693224 JanEiteBullema@slideshare.net(JanEiteBullema) 2017 Electrical interconnects in miro fluidics JanEiteBullema The last few years microfluidics stopped being a niche technology,with a user base predominantly consisting of engineers. Most of the microfluidic companies now are growing and the install base of instruments based on microfluidics is growing fast. Still, the situation is far from ideal. Designs are unnecessary complicated, there is little to no reuse of build-up expertise or developed components. Similar to the early computerindustry,amajor reason for the low popularity is the complicated character of microfluidic devices, specifically in terms of fabrication, and thusmaking theminaccessible to a larger population.[1]I n the ECSEL MFM project first steps have been made towards developingstandards for microfluidic devices. Standards for basic design features like geometrical outlines and port locations have been proposed inwhite papers[2]and where adopted by ISO in an ISO IWA process.[3]One of the complications of microfluidic products is the challenge of providing electrical connections. The average microfluidic engineer lacks electronicpackaging knowledge. Furthermore, the incompatibility of microfluidics and electronics combined with space constrains, limits the technology choices. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mfmanufacturingworkshopelectricalinterconnectsinmirofluidicsimec2017-200409074653-thumbnail.jpg?width=120&height=120&fit=bounds" /> The last few years microfluidics stopped being a niche technology,with a user base predominantly consisting of engineers. Most of the microfluidic companies now are growing and the install base of instruments based on microfluidics is growing fast. Still, the situation is far from ideal. Designs are unnecessary complicated, there is little to no reuse of build-up expertise or developed components. Similar to the early computerindustry,amajor reason for the low popularity is the complicated character of microfluidic devices, specifically in terms of fabrication, and thusmaking theminaccessible to a larger population.[1]I n the ECSEL MFM project first steps have been made towards developingstandards for microfluidic devices. Standards for basic design features like geometrical outlines and port locations have been proposed inwhite papers[2]and where adopted by ISO in an ISO IWA process.[3]One of the complications of microfluidic products is the challenge of providing electrical connections. The average microfluidic engineer lacks electronicpackaging knowledge. Furthermore, the incompatibility of microfluidics and electronics combined with space constrains, limits the technology choices.

2017 Electrical interconnects in miro fluidics from Jan Eite Bullema

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0 2012 Biocompatibele MEMS / Microsystems Packaging /slideshow/2012-biocompatibele-mems-microsystems-packaging/169829220 2012biocompatibelememspackaging-190907123701
This presentaion is a short introduction into the fascinating subject of biocompatible packaging of MEMS / micro systems. I gave this presentation for a technology cluster of Dutch micro systems companies]]>
This presentaion is a short introduction into the fascinating subject of biocompatible packaging of MEMS / micro systems. I gave this presentation for a technology cluster of Dutch micro systems companies]]> Sat, 07 Sep 2019 12:37:01 GMT /slideshow/2012-biocompatibele-mems-microsystems-packaging/169829220 JanEiteBullema@slideshare.net(JanEiteBullema) 2012 Biocompatibele MEMS / Microsystems Packaging JanEiteBullema This presentaion is a short introduction into the fascinating subject of biocompatible packaging of MEMS / micro systems. I gave this presentation for a technology cluster of Dutch micro systems companies <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2012biocompatibelememspackaging-190907123701-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentaion is a short introduction into the fascinating subject of biocompatible packaging of MEMS / micro systems. I gave this presentation for a technology cluster of Dutch micro systems companies

2012 Biocompatibele MEMS / Microsystems Packaging from Jan Eite Bullema

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0 2018 Reliability in the age of big data /slideshow/2018-bullema-reliability-in-the-age-of-big-data-template-plot-conferentie/124472246 2018-bullema-reliabilityintheageofbigdatatemplateplotconferentie-181130090303
Reliability in the Age of Big Data Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. The issue at hand is how to link typical new data elements of big data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. New methods like deep learning, text mining and multivariate degradation models are currently explored to use big data for reliability applications. These new methods can be the basis for new reliability propositions like use based insurance. Basis for this presentation is a paper by William Meeker and coworkers, were new reliability methods for using Big Data are introduced. At TNO we are currently working on Digital Twins for Smart Manufacturing, a topic closely related to use of big data for reliability in industrial environments ]]>
Reliability in the Age of Big Data Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. The issue at hand is how to link typical new data elements of big data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. New methods like deep learning, text mining and multivariate degradation models are currently explored to use big data for reliability applications. These new methods can be the basis for new reliability propositions like use based insurance. Basis for this presentation is a paper by William Meeker and coworkers, were new reliability methods for using Big Data are introduced. At TNO we are currently working on Digital Twins for Smart Manufacturing, a topic closely related to use of big data for reliability in industrial environments ]]> Fri, 30 Nov 2018 09:03:03 GMT /slideshow/2018-bullema-reliability-in-the-age-of-big-data-template-plot-conferentie/124472246 JanEiteBullema@slideshare.net(JanEiteBullema) 2018 Reliability in the age of big data JanEiteBullema Reliability in the Age of Big Data Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. The issue at hand is how to link typical new data elements of big data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. New methods like deep learning, text mining and multivariate degradation models are currently explored to use big data for reliability applications. These new methods can be the basis for new reliability propositions like use based insurance. Basis for this presentation is a paper by William Meeker and coworkers, were new reliability methods for using Big Data are introduced. At TNO we are currently working on Digital Twins for Smart Manufacturing, a topic closely related to use of big data for reliability in industrial environments <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2018-bullema-reliabilityintheageofbigdatatemplateplotconferentie-181130090303-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reliability in the Age of Big Data Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. The issue at hand is how to link typical new data elements of big data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. New methods like deep learning, text mining and multivariate degradation models are currently explored to use big data for reliability applications. These new methods can be the basis for new reliability propositions like use based insurance. Basis for this presentation is a paper by William Meeker and coworkers, were new reliability methods for using Big Data are introduced. At TNO we are currently working on Digital Twins for Smart Manufacturing, a topic closely related to use of big data for reliability in industrial environments

2018 Reliability in the age of big data from Jan Eite Bullema

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0 2016 Bayesian networks to analyse led reliability /JanEiteBullema/2016-bayesian-networks-to-analyse-led-reliability 2016-bullema-bayesiannetworkstoanalyseledreliabilitycorrectedfinal-180823071714
Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase. Use of Bayesian Networks make it possible to do root cause analysis. The Bayesian Network is build from FMEA. ]]>
Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase. Use of Bayesian Networks make it possible to do root cause analysis. The Bayesian Network is build from FMEA. ]]> Thu, 23 Aug 2018 07:17:14 GMT /JanEiteBullema/2016-bayesian-networks-to-analyse-led-reliability JanEiteBullema@slideshare.net(JanEiteBullema) 2016 Bayesian networks to analyse led reliability JanEiteBullema Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase. Use of Bayesian Networks make it possible to do root cause analysis. The Bayesian Network is build from FMEA. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2016-bullema-bayesiannetworkstoanalyseledreliabilitycorrectedfinal-180823071714-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase. Use of Bayesian Networks make it possible to do root cause analysis. The Bayesian Network is build from FMEA.

2016 Bayesian networks to analyse led reliability from Jan Eite Bullema

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0 2017 3D Printing: stop prototyping, start producing! /slideshow/2017-3d-printing-stop-prototyping-start-producing/93418735 2017-bullema-tnoamsystemcenter3dprintinginpractisefinal-180410074540
3D Printing: stop prototyping, start producing! Jan Eite Bullema, Senior Scientist, TNO 3D printing is transforming from a prototyping technology into a manufacturing technology. Two important roadblocks in this transformation are (1) the difficulty of designing products suitable for 3D printing and (2) production costs. In my presentation I will show how the issue of product design for 3D printing is addressed using big data and machine learning. To lower production costs faster 3D printing technologies have been developed. In the presentation I will show examples of innovative equipment that TNO has developed to increase the production speed of 3D printing. ]]>
3D Printing: stop prototyping, start producing! Jan Eite Bullema, Senior Scientist, TNO 3D printing is transforming from a prototyping technology into a manufacturing technology. Two important roadblocks in this transformation are (1) the difficulty of designing products suitable for 3D printing and (2) production costs. In my presentation I will show how the issue of product design for 3D printing is addressed using big data and machine learning. To lower production costs faster 3D printing technologies have been developed. In the presentation I will show examples of innovative equipment that TNO has developed to increase the production speed of 3D printing. ]]> Tue, 10 Apr 2018 07:45:39 GMT /slideshow/2017-3d-printing-stop-prototyping-start-producing/93418735 JanEiteBullema@slideshare.net(JanEiteBullema) 2017 3D Printing: stop prototyping, start producing! JanEiteBullema 3D Printing: stop prototyping, start producing! Jan Eite Bullema, Senior Scientist, TNO 3D printing is transforming from a prototyping technology into a manufacturing technology. Two important roadblocks in this transformation are (1) the difficulty of designing products suitable for 3D printing and (2) production costs. In my presentation I will show how the issue of product design for 3D printing is addressed using big data and machine learning. To lower production costs faster 3D printing technologies have been developed. In the presentation I will show examples of innovative equipment that TNO has developed to increase the production speed of 3D printing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017-bullema-tnoamsystemcenter3dprintinginpractisefinal-180410074540-thumbnail.jpg?width=120&height=120&fit=bounds" /> 3D Printing: stop prototyping, start producing! Jan Eite Bullema, Senior Scientist, TNO 3D printing is transforming from a prototyping technology into a manufacturing technology. Two important roadblocks in this transformation are (1) the difficulty of designing products suitable for 3D printing and (2) production costs. In my presentation I will show how the issue of product design for 3D printing is addressed using big data and machine learning. To lower production costs faster 3D printing technologies have been developed. In the presentation I will show examples of innovative equipment that TNO has developed to increase the production speed of 3D printing.

2017 3D Printing: stop prototyping, start producing! from Jan Eite Bullema

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0 2011 Introduction micro and nanotechnology /slideshow/2011-introduction-micro-and-nanotechnology/88095532 mc2011introductionmicroandnanotechnology-180216081854
These are the slides I made for the Micro Systems and Nano technology course that I gave for Mikro centrum for some years, a little old but not outdated i think. Already the current converge of hardware technology, software technology and biology becomes visible. ]]>
These are the slides I made for the Micro Systems and Nano technology course that I gave for Mikro centrum for some years, a little old but not outdated i think. Already the current converge of hardware technology, software technology and biology becomes visible. ]]> Fri, 16 Feb 2018 08:18:54 GMT /slideshow/2011-introduction-micro-and-nanotechnology/88095532 JanEiteBullema@slideshare.net(JanEiteBullema) 2011 Introduction micro and nanotechnology JanEiteBullema These are the slides I made for the Micro Systems and Nano technology course that I gave for Mikro centrum for some years, a little old but not outdated i think. Already the current converge of hardware technology, software technology and biology becomes visible. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mc2011introductionmicroandnanotechnology-180216081854-thumbnail.jpg?width=120&height=120&fit=bounds" /> These are the slides I made for the Micro Systems and Nano technology course that I gave for Mikro centrum for some years, a little old but not outdated i think. Already the current converge of hardware technology, software technology and biology becomes visible.

2011 Introduction micro and nanotechnology from Jan Eite Bullema

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0 2017 Accelerated Testing: ALT, HALT and MEOST /slideshow/2017-accelerated-testing-alt-halt-and-meost/81847327 2017-bullema-acceleratedtestingforicd-171110101056
Accelerated Life Testing (ALT) is a lifetime prediction methodology commonly used by the industry in the past decades. This method , however, is reaching its limitations with the development of products within emerging technologies requiring long-term reliability. At TNO we work on technology development with long expected lifetimes , e.g. solar cells and LED lighting. New methodologies are required to predict long term reliability for these type of products. Methods to predict long term reliability by extending ALT methods, like HALT (Highly Accelerated Life Testing) and MEOST (Multiple Environmental Stress Testing) will be discussed in the presentation. A problem in application of these methods is definition of adequate stress profiles. It is our experience that to gain benefit from accelerated testing, insight in the Physic of Failure of a product is essential. ]]>
Accelerated Life Testing (ALT) is a lifetime prediction methodology commonly used by the industry in the past decades. This method , however, is reaching its limitations with the development of products within emerging technologies requiring long-term reliability. At TNO we work on technology development with long expected lifetimes , e.g. solar cells and LED lighting. New methodologies are required to predict long term reliability for these type of products. Methods to predict long term reliability by extending ALT methods, like HALT (Highly Accelerated Life Testing) and MEOST (Multiple Environmental Stress Testing) will be discussed in the presentation. A problem in application of these methods is definition of adequate stress profiles. It is our experience that to gain benefit from accelerated testing, insight in the Physic of Failure of a product is essential. ]]> Fri, 10 Nov 2017 10:10:56 GMT /slideshow/2017-accelerated-testing-alt-halt-and-meost/81847327 JanEiteBullema@slideshare.net(JanEiteBullema) 2017 Accelerated Testing: ALT, HALT and MEOST JanEiteBullema Accelerated Life Testing (ALT) is a lifetime prediction methodology commonly used by the industry in the past decades. This method , however, is reaching its limitations with the development of products within emerging technologies requiring long-term reliability. At TNO we work on technology development with long expected lifetimes , e.g. solar cells and LED lighting. New methodologies are required to predict long term reliability for these type of products. Methods to predict long term reliability by extending ALT methods, like HALT (Highly Accelerated Life Testing) and MEOST (Multiple Environmental Stress Testing) will be discussed in the presentation. A problem in application of these methods is definition of adequate stress profiles. It is our experience that to gain benefit from accelerated testing, insight in the Physic of Failure of a product is essential. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2017-bullema-acceleratedtestingforicd-171110101056-thumbnail.jpg?width=120&height=120&fit=bounds" /> Accelerated Life Testing (ALT) is a lifetime prediction methodology commonly used by the industry in the past decades. This method , however, is reaching its limitations with the development of products within emerging technologies requiring long-term reliability. At TNO we work on technology development with long expected lifetimes , e.g. solar cells and LED lighting. New methodologies are required to predict long term reliability for these type of products. Methods to predict long term reliability by extending ALT methods, like HALT (Highly Accelerated Life Testing) and MEOST (Multiple Environmental Stress Testing) will be discussed in the presentation. A problem in application of these methods is definition of adequate stress profiles. It is our experience that to gain benefit from accelerated testing, insight in the Physic of Failure of a product is essential.

2017 Accelerated Testing: ALT, HALT and MEOST from Jan Eite Bullema

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0 2016 Deep Learning with R and h2o /slideshow/2016-deep-learning-with-r-and-h2o/80695480 2016-bullema-deeplearningwithrandh2opublic-171011132559
Deep Learning with H2O and R In my previous TNO4U talk I gave an introduction about how I addressed the classification problem for autonomous driving using fuzzy logic based insights. I also gave a very concise introduction on deep learning. In this talk I want to go more into the details of deep learning - what is it - and why people think it is so important. Due to the duration of the talk I will not go through the complete history of Artificial Intelligence from the perceptron, via the Hopfield net, towards modern Restricted Bolzmann Machines and Convoluted Neural Networks. Nor get philosophical and do a Gödel, Escher, Bach exposé. I will just give some basic theoretical considerations and demonstrate how one easy it is to get results with deep learning using – open source- tools like R and H2O. You can install these for free on any computer, Windows, Linux or Mac. R is of course the computer language of choice for data science, H2O is an easy to use interface between R and Big Data (like Spark). During the talk we will do some small workshop style examples. Handwriting recognition with a Restricted Bolzmann Machine, analyze heartbeats with machine learning and do a little predictive modelling on an industrial process. Are this the heartbeats of a healthy person? Let’s ask our algorithm (The computer has seen more heartbeats than any living doctor) ]]>
Deep Learning with H2O and R In my previous TNO4U talk I gave an introduction about how I addressed the classification problem for autonomous driving using fuzzy logic based insights. I also gave a very concise introduction on deep learning. In this talk I want to go more into the details of deep learning - what is it - and why people think it is so important. Due to the duration of the talk I will not go through the complete history of Artificial Intelligence from the perceptron, via the Hopfield net, towards modern Restricted Bolzmann Machines and Convoluted Neural Networks. Nor get philosophical and do a Gödel, Escher, Bach exposé. I will just give some basic theoretical considerations and demonstrate how one easy it is to get results with deep learning using – open source- tools like R and H2O. You can install these for free on any computer, Windows, Linux or Mac. R is of course the computer language of choice for data science, H2O is an easy to use interface between R and Big Data (like Spark). During the talk we will do some small workshop style examples. Handwriting recognition with a Restricted Bolzmann Machine, analyze heartbeats with machine learning and do a little predictive modelling on an industrial process. Are this the heartbeats of a healthy person? Let’s ask our algorithm (The computer has seen more heartbeats than any living doctor) ]]> Wed, 11 Oct 2017 13:25:59 GMT /slideshow/2016-deep-learning-with-r-and-h2o/80695480 JanEiteBullema@slideshare.net(JanEiteBullema) 2016 Deep Learning with R and h2o JanEiteBullema Deep Learning with H2O and R In my previous TNO4U talk I gave an introduction about how I addressed the classification problem for autonomous driving using fuzzy logic based insights. I also gave a very concise introduction on deep learning. In this talk I want to go more into the details of deep learning - what is it - and why people think it is so important. Due to the duration of the talk I will not go through the complete history of Artificial Intelligence from the perceptron, via the Hopfield net, towards modern Restricted Bolzmann Machines and Convoluted Neural Networks. Nor get philosophical and do a Gödel, Escher, Bach exposé. I will just give some basic theoretical considerations and demonstrate how one easy it is to get results with deep learning using – open source- tools like R and H2O. You can install these for free on any computer, Windows, Linux or Mac. R is of course the computer language of choice for data science, H2O is an easy to use interface between R and Big Data (like Spark). During the talk we will do some small workshop style examples. Handwriting recognition with a Restricted Bolzmann Machine, analyze heartbeats with machine learning and do a little predictive modelling on an industrial process. Are this the heartbeats of a healthy person? Let’s ask our algorithm (The computer has seen more heartbeats than any living doctor) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2016-bullema-deeplearningwithrandh2opublic-171011132559-thumbnail.jpg?width=120&height=120&fit=bounds" /> Deep Learning with H2O and R In my previous TNO4U talk I gave an introduction about how I addressed the classification problem for autonomous driving using fuzzy logic based insights. I also gave a very concise introduction on deep learning. In this talk I want to go more into the details of deep learning - what is it - and why people think it is so important. Due to the duration of the talk I will not go through the complete history of Artificial Intelligence from the perceptron, via the Hopfield net, towards modern Restricted Bolzmann Machines and Convoluted Neural Networks. Nor get philosophical and do a Gödel, Escher, Bach exposé. I will just give some basic theoretical considerations and demonstrate how one easy it is to get results with deep learning using – open source- tools like R and H2O. You can install these for free on any computer, Windows, Linux or Mac. R is of course the computer language of choice for data science, H2O is an easy to use interface between R and Big Data (like Spark). During the talk we will do some small workshop style examples. Handwriting recognition with a Restricted Bolzmann Machine, analyze heartbeats with machine learning and do a little predictive modelling on an industrial process. Are this the heartbeats of a healthy person? Let’s ask our algorithm (The computer has seen more heartbeats than any living doctor)

2016 Deep Learning with R and h2o from Jan Eite Bullema

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0 2007 Introduction MEOST /slideshow/2007-introduction-meost/80317251 2007introductionmoest-170930160547
This presentation is an introduction into Multiple Over Stress Testing. A method to design robust and reliable products. It is a relaibility method that requires much insight in the Physics of Failure of the product in development]]>
This presentation is an introduction into Multiple Over Stress Testing. A method to design robust and reliable products. It is a relaibility method that requires much insight in the Physics of Failure of the product in development]]> Sat, 30 Sep 2017 16:05:47 GMT /slideshow/2007-introduction-meost/80317251 JanEiteBullema@slideshare.net(JanEiteBullema) 2007 Introduction MEOST JanEiteBullema This presentation is an introduction into Multiple Over Stress Testing. A method to design robust and reliable products. It is a relaibility method that requires much insight in the Physics of Failure of the product in development <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2007introductionmoest-170930160547-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation is an introduction into Multiple Over Stress Testing. A method to design robust and reliable products. It is a relaibility method that requires much insight in the Physics of Failure of the product in development

2007 Introduction MEOST from Jan Eite Bullema

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0 2015 Deep learning and fuzzy logic /slideshow/deep-learning-and-fuzzy-logic/79838162 deeplearningandfuzzylogic-170916120122
This painting is a painting by Matisse. It is a painting called: “The fall of Icarus” I use this painting for this colloquium lecture, because twenty years ago, there was a German company called Fuzzytech that had this Matisse painting as their poster. Also whit the text precision is not truth. I have had this poster of Fuzzytech for more than ten years over my desk at home. Because I liked this basic concept of Fuzzy Logic very much: Precision is not truth. Twenty years ago I gave a Fuzzy Logic course for CTT and Fontys, because I had made several Fuzzy Control algorithms and had become a national expert in Fuzzy Logic. Eventually the Fuzzy Logic hype dwindled down and I proceeded concentrating on other advanced process control methods A few months ago I encounter in the Crystal project a classification problem, for safety evaluation of autonomous driving, that could be solved using Fuzzy Logic. So I read about the latest developments and saw that there have been interesting developments in this field. New set theory and potential coupling of Fuzzy Logic with Big Data analytics. I decided to give this colloquium, based upon my old three day Fuzzy Logic course. So I start with a concise introduction, give an example of an application. And then jump into the developments in soft computing and deep learning, which is a broader than fuzzy logic. The precision is not truth part of the lecture is an outline of my current work for safety classification of collaborative driving. ]]>
This painting is a painting by Matisse. It is a painting called: “The fall of Icarus” I use this painting for this colloquium lecture, because twenty years ago, there was a German company called Fuzzytech that had this Matisse painting as their poster. Also whit the text precision is not truth. I have had this poster of Fuzzytech for more than ten years over my desk at home. Because I liked this basic concept of Fuzzy Logic very much: Precision is not truth. Twenty years ago I gave a Fuzzy Logic course for CTT and Fontys, because I had made several Fuzzy Control algorithms and had become a national expert in Fuzzy Logic. Eventually the Fuzzy Logic hype dwindled down and I proceeded concentrating on other advanced process control methods A few months ago I encounter in the Crystal project a classification problem, for safety evaluation of autonomous driving, that could be solved using Fuzzy Logic. So I read about the latest developments and saw that there have been interesting developments in this field. New set theory and potential coupling of Fuzzy Logic with Big Data analytics. I decided to give this colloquium, based upon my old three day Fuzzy Logic course. So I start with a concise introduction, give an example of an application. And then jump into the developments in soft computing and deep learning, which is a broader than fuzzy logic. The precision is not truth part of the lecture is an outline of my current work for safety classification of collaborative driving. ]]> Sat, 16 Sep 2017 12:01:22 GMT /slideshow/deep-learning-and-fuzzy-logic/79838162 JanEiteBullema@slideshare.net(JanEiteBullema) 2015 Deep learning and fuzzy logic JanEiteBullema This painting is a painting by Matisse. It is a painting called: “The fall of Icarus” I use this painting for this colloquium lecture, because twenty years ago, there was a German company called Fuzzytech that had this Matisse painting as their poster. Also whit the text precision is not truth. I have had this poster of Fuzzytech for more than ten years over my desk at home. Because I liked this basic concept of Fuzzy Logic very much: Precision is not truth. Twenty years ago I gave a Fuzzy Logic course for CTT and Fontys, because I had made several Fuzzy Control algorithms and had become a national expert in Fuzzy Logic. Eventually the Fuzzy Logic hype dwindled down and I proceeded concentrating on other advanced process control methods A few months ago I encounter in the Crystal project a classification problem, for safety evaluation of autonomous driving, that could be solved using Fuzzy Logic. So I read about the latest developments and saw that there have been interesting developments in this field. New set theory and potential coupling of Fuzzy Logic with Big Data analytics. I decided to give this colloquium, based upon my old three day Fuzzy Logic course. So I start with a concise introduction, give an example of an application. And then jump into the developments in soft computing and deep learning, which is a broader than fuzzy logic. The precision is not truth part of the lecture is an outline of my current work for safety classification of collaborative driving. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/deeplearningandfuzzylogic-170916120122-thumbnail.jpg?width=120&height=120&fit=bounds" /> This painting is a painting by Matisse. It is a painting called: “The fall of Icarus” I use this painting for this colloquium lecture, because twenty years ago, there was a German company called Fuzzytech that had this Matisse painting as their poster. Also whit the text precision is not truth. I have had this poster of Fuzzytech for more than ten years over my desk at home. Because I liked this basic concept of Fuzzy Logic very much: Precision is not truth. Twenty years ago I gave a Fuzzy Logic course for CTT and Fontys, because I had made several Fuzzy Control algorithms and had become a national expert in Fuzzy Logic. Eventually the Fuzzy Logic hype dwindled down and I proceeded concentrating on other advanced process control methods A few months ago I encounter in the Crystal project a classification problem, for safety evaluation of autonomous driving, that could be solved using Fuzzy Logic. So I read about the latest developments and saw that there have been interesting developments in this field. New set theory and potential coupling of Fuzzy Logic with Big Data analytics. I decided to give this colloquium, based upon my old three day Fuzzy Logic course. So I start with a concise introduction, give an example of an application. And then jump into the developments in soft computing and deep learning, which is a broader than fuzzy logic. The precision is not truth part of the lecture is an outline of my current work for safety classification of collaborative driving.

2015 Deep learning and fuzzy logic from Jan Eite Bullema

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0 2015 3D Printing for microfluidics manufacturing /JanEiteBullema/3-d-printing-for-microfluidics-manufacturing 3dprintingformicrofluidicsmanufacturing-170916112141
3D Printing / Additive Manufacturing appears to be an attractive technology to realize fluidic devices. By many still mainly seen as technology for development purposes, as 3D printing makes it relative easy to make small series with design iterations (e.g. different inlet apertures, different channel length, mixer size). In fact 3D printing evolves rapidly as a manufacturing technology. This is especially true for fluidic devices that have a more complex design – like many organ-on-chip devices. Recent developments in 3D printing have made 3D printing more attractive as a manufacturing technology. Dolomite has introduced the Fluidic Factory 3D printer for fast prototyping. The Continuous Liquid Interface Process (CLIP) announced by Carbon in the beginning of 2015, makes VAT polymerization 100 timed faster. Carbon has demonstrated (and patented) production of microfluidic products. At TNO we have developed production equipment that enable low cost production of integrated microfluidics with 3D printing. With 3D printing technology it becomes possible to manufacture functional 3D fluidic structures, e.g. serpentine mixers, Brownian ratchets, Tesla valves. 3D printing makes it also possible to easily integrate fluidic functionalities, like mixing, valving, metering in one device. Which leads to a reduction of integral device costs. It is expected that especially for complex integrated lab-on-a-chip / organ-on-a-chip devices, 3D printing will become the production technology of choice.]]>
3D Printing / Additive Manufacturing appears to be an attractive technology to realize fluidic devices. By many still mainly seen as technology for development purposes, as 3D printing makes it relative easy to make small series with design iterations (e.g. different inlet apertures, different channel length, mixer size). In fact 3D printing evolves rapidly as a manufacturing technology. This is especially true for fluidic devices that have a more complex design – like many organ-on-chip devices. Recent developments in 3D printing have made 3D printing more attractive as a manufacturing technology. Dolomite has introduced the Fluidic Factory 3D printer for fast prototyping. The Continuous Liquid Interface Process (CLIP) announced by Carbon in the beginning of 2015, makes VAT polymerization 100 timed faster. Carbon has demonstrated (and patented) production of microfluidic products. At TNO we have developed production equipment that enable low cost production of integrated microfluidics with 3D printing. With 3D printing technology it becomes possible to manufacture functional 3D fluidic structures, e.g. serpentine mixers, Brownian ratchets, Tesla valves. 3D printing makes it also possible to easily integrate fluidic functionalities, like mixing, valving, metering in one device. Which leads to a reduction of integral device costs. It is expected that especially for complex integrated lab-on-a-chip / organ-on-a-chip devices, 3D printing will become the production technology of choice.]]> Sat, 16 Sep 2017 11:21:41 GMT /JanEiteBullema/3-d-printing-for-microfluidics-manufacturing JanEiteBullema@slideshare.net(JanEiteBullema) 2015 3D Printing for microfluidics manufacturing JanEiteBullema 3D Printing / Additive Manufacturing appears to be an attractive technology to realize fluidic devices. By many still mainly seen as technology for development purposes, as 3D printing makes it relative easy to make small series with design iterations (e.g. different inlet apertures, different channel length, mixer size). In fact 3D printing evolves rapidly as a manufacturing technology. This is especially true for fluidic devices that have a more complex design – like many organ-on-chip devices. Recent developments in 3D printing have made 3D printing more attractive as a manufacturing technology. Dolomite has introduced the Fluidic Factory 3D printer for fast prototyping. The Continuous Liquid Interface Process (CLIP) announced by Carbon in the beginning of 2015, makes VAT polymerization 100 timed faster. Carbon has demonstrated (and patented) production of microfluidic products. At TNO we have developed production equipment that enable low cost production of integrated microfluidics with 3D printing. With 3D printing technology it becomes possible to manufacture functional 3D fluidic structures, e.g. serpentine mixers, Brownian ratchets, Tesla valves. 3D printing makes it also possible to easily integrate fluidic functionalities, like mixing, valving, metering in one device. Which leads to a reduction of integral device costs. It is expected that especially for complex integrated lab-on-a-chip / organ-on-a-chip devices, 3D printing will become the production technology of choice. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3dprintingformicrofluidicsmanufacturing-170916112141-thumbnail.jpg?width=120&height=120&fit=bounds" /> 3D Printing / Additive Manufacturing appears to be an attractive technology to realize fluidic devices. By many still mainly seen as technology for development purposes, as 3D printing makes it relative easy to make small series with design iterations (e.g. different inlet apertures, different channel length, mixer size). In fact 3D printing evolves rapidly as a manufacturing technology. This is especially true for fluidic devices that have a more complex design – like many organ-on-chip devices. Recent developments in 3D printing have made 3D printing more attractive as a manufacturing technology. Dolomite has introduced the Fluidic Factory 3D printer for fast prototyping. The Continuous Liquid Interface Process (CLIP) announced by Carbon in the beginning of 2015, makes VAT polymerization 100 timed faster. Carbon has demonstrated (and patented) production of microfluidic products. At TNO we have developed production equipment that enable low cost production of integrated microfluidics with 3D printing. With 3D printing technology it becomes possible to manufacture functional 3D fluidic structures, e.g. serpentine mixers, Brownian ratchets, Tesla valves. 3D printing makes it also possible to easily integrate fluidic functionalities, like mixing, valving, metering in one device. Which leads to a reduction of integral device costs. It is expected that especially for complex integrated lab-on-a-chip / organ-on-a-chip devices, 3D printing will become the production technology of choice.

2015 3D Printing for microfluidics manufacturing from Jan Eite Bullema

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0 2016 3D printing for organ on a chip /JanEiteBullema/3-d-printing-for-organ-on-a-chip 3dprintingfororganonachip-170907113527
33D Printing Organ on a Chip, Jan Eite Bullema, TNO Industrial Science The goal of this so-called deep dive exploration is to identify business potential of biomimetic microfluidic systems (organ-on-a-chip). One of the most attractive applications of organ-on-a-chip at the moment appears to be mimicking human’s physiological responses for medicine development. Efficacy of medicine is a big challenge for the pharmaceutical industry. Depending on the illness specific drugs can have an efficacy of less than 30 %. Drug efficacy is one of the topics addressed by the Netherlands by an "Over de grenzen" KNAW program. In the presentation I will focus on recent -3D Printing developments- in the field of organ / organ-on-a-chip printing. Just to give an impression of the awesome, fantastic, amazing, wow - no - WOW!!- developments. Since a few years organs are printed in the lab, and I will start with some examples of printed organs bones, kidneys, blood vesels, livers, ears, that can be made at the moment. Then I will dive deeper into organ-on-a-chip, a true micro sysmtems topic - my area of expertise here- , and explain a little on what organs-on-chip are. Subsequent I will go into various technologies for 3D printing of cell and bio materials. And I will finish with some ideas on organ printing that are trully amazing, most impressive are Craig Venter's . ]]>
33D Printing Organ on a Chip, Jan Eite Bullema, TNO Industrial Science The goal of this so-called deep dive exploration is to identify business potential of biomimetic microfluidic systems (organ-on-a-chip). One of the most attractive applications of organ-on-a-chip at the moment appears to be mimicking human’s physiological responses for medicine development. Efficacy of medicine is a big challenge for the pharmaceutical industry. Depending on the illness specific drugs can have an efficacy of less than 30 %. Drug efficacy is one of the topics addressed by the Netherlands by an "Over de grenzen" KNAW program. In the presentation I will focus on recent -3D Printing developments- in the field of organ / organ-on-a-chip printing. Just to give an impression of the awesome, fantastic, amazing, wow - no - WOW!!- developments. Since a few years organs are printed in the lab, and I will start with some examples of printed organs bones, kidneys, blood vesels, livers, ears, that can be made at the moment. Then I will dive deeper into organ-on-a-chip, a true micro sysmtems topic - my area of expertise here- , and explain a little on what organs-on-chip are. Subsequent I will go into various technologies for 3D printing of cell and bio materials. And I will finish with some ideas on organ printing that are trully amazing, most impressive are Craig Venter's . ]]> Thu, 07 Sep 2017 11:35:27 GMT /JanEiteBullema/3-d-printing-for-organ-on-a-chip JanEiteBullema@slideshare.net(JanEiteBullema) 2016 3D printing for organ on a chip JanEiteBullema 33D Printing Organ on a Chip, Jan Eite Bullema, TNO Industrial Science The goal of this so-called deep dive exploration is to identify business potential of biomimetic microfluidic systems (organ-on-a-chip). One of the most attractive applications of organ-on-a-chip at the moment appears to be mimicking human’s physiological responses for medicine development. Efficacy of medicine is a big challenge for the pharmaceutical industry. Depending on the illness specific drugs can have an efficacy of less than 30 %. Drug efficacy is one of the topics addressed by the Netherlands by an "Over de grenzen" KNAW program. In the presentation I will focus on recent -3D Printing developments- in the field of organ / organ-on-a-chip printing. Just to give an impression of the awesome, fantastic, amazing, wow - no - WOW!!- developments. Since a few years organs are printed in the lab, and I will start with some examples of printed organs bones, kidneys, blood vesels, livers, ears, that can be made at the moment. Then I will dive deeper into organ-on-a-chip, a true micro sysmtems topic - my area of expertise here- , and explain a little on what organs-on-chip are. Subsequent I will go into various technologies for 3D printing of cell and bio materials. And I will finish with some ideas on organ printing that are trully amazing, most impressive are Craig Venter's . <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/3dprintingfororganonachip-170907113527-thumbnail.jpg?width=120&height=120&fit=bounds" /> 33D Printing Organ on a Chip, Jan Eite Bullema, TNO Industrial Science The goal of this so-called deep dive exploration is to identify business potential of biomimetic microfluidic systems (organ-on-a-chip). One of the most attractive applications of organ-on-a-chip at the moment appears to be mimicking human’s physiological responses for medicine development. Efficacy of medicine is a big challenge for the pharmaceutical industry. Depending on the illness specific drugs can have an efficacy of less than 30 %. Drug efficacy is one of the topics addressed by the Netherlands by an "Over de grenzen" KNAW program. In the presentation I will focus on recent -3D Printing developments- in the field of organ / organ-on-a-chip printing. Just to give an impression of the awesome, fantastic, amazing, wow - no - WOW!!- developments. Since a few years organs are printed in the lab, and I will start with some examples of printed organs bones, kidneys, blood vesels, livers, ears, that can be made at the moment. Then I will dive deeper into organ-on-a-chip, a true micro sysmtems topic - my area of expertise here- , and explain a little on what organs-on-chip are. Subsequent I will go into various technologies for 3D printing of cell and bio materials. And I will finish with some ideas on organ printing that are trully amazing, most impressive are Craig Venter's .

2016 3D printing for organ on a chip from Jan Eite Bullema

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0 2012 Introduction wire bonding /slideshow/introduction-wire-bonding/79479066 introductionwirebonding-170906085338
A basic introduction into wire bonding technology based upon Harman's book and Tumala's Fundamentals of Microsystem Packaging]]>
A basic introduction into wire bonding technology based upon Harman's book and Tumala's Fundamentals of Microsystem Packaging]]> Wed, 06 Sep 2017 08:53:38 GMT /slideshow/introduction-wire-bonding/79479066 JanEiteBullema@slideshare.net(JanEiteBullema) 2012 Introduction wire bonding JanEiteBullema A basic introduction into wire bonding technology based upon Harman's book and Tumala's Fundamentals of Microsystem Packaging <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introductionwirebonding-170906085338-thumbnail.jpg?width=120&height=120&fit=bounds" /> A basic introduction into wire bonding technology based upon Harman's book and Tumala's Fundamentals of Microsystem Packaging

2012 Introduction wire bonding from Jan Eite Bullema

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0 2014 2D and 3D printing to realize innovative electronic products /slideshow/2d-and-3d-printing-to-realize-innovative-electronic-products/79478100 2d3dprintingtorealizeinnovativeelectronicproducts-170906082134
Most people active in electronics industry are not yet aware that 3D printing can become a game changer. Currently printing and dispensing is done on a limited scale in the electronics industry. For instance: (a) printing of conformal coatings, (b) glob topping of bare dies, (c) dam and fill as packaging technology, (d) dispensing underfill materials, (e) dispensing of conductive adhesives, even dispensing of 3D electrical interconnects. There are three reasons, why printable electronics is gaining considerable attention. The first is that the printing process can be applied to many different kinds of substrates, and also three-dimensional printing is possible. This enables the changing of the whole system of producing electronic devices, including the design and manufacturing phases, material selection, and device structure and architecture. Second, printed electronics offers better economics to electronics manufacturing. Traditional electronics is cheap only on the mass production scale, in contrast to printing, and especially inkjet printing, which offers flexible and cheap production for tailored small-volume products. Third, printing offers new business models. E.g. Inkjet technology enables also ‘‘desktop manufacturing’’, which applies to small-scale micro factories with small fixed costs. ]]>
Most people active in electronics industry are not yet aware that 3D printing can become a game changer. Currently printing and dispensing is done on a limited scale in the electronics industry. For instance: (a) printing of conformal coatings, (b) glob topping of bare dies, (c) dam and fill as packaging technology, (d) dispensing underfill materials, (e) dispensing of conductive adhesives, even dispensing of 3D electrical interconnects. There are three reasons, why printable electronics is gaining considerable attention. The first is that the printing process can be applied to many different kinds of substrates, and also three-dimensional printing is possible. This enables the changing of the whole system of producing electronic devices, including the design and manufacturing phases, material selection, and device structure and architecture. Second, printed electronics offers better economics to electronics manufacturing. Traditional electronics is cheap only on the mass production scale, in contrast to printing, and especially inkjet printing, which offers flexible and cheap production for tailored small-volume products. Third, printing offers new business models. E.g. Inkjet technology enables also ‘‘desktop manufacturing’’, which applies to small-scale micro factories with small fixed costs. ]]> Wed, 06 Sep 2017 08:21:34 GMT /slideshow/2d-and-3d-printing-to-realize-innovative-electronic-products/79478100 JanEiteBullema@slideshare.net(JanEiteBullema) 2014 2D and 3D printing to realize innovative electronic products JanEiteBullema Most people active in electronics industry are not yet aware that 3D printing can become a game changer. Currently printing and dispensing is done on a limited scale in the electronics industry. For instance: (a) printing of conformal coatings, (b) glob topping of bare dies, (c) dam and fill as packaging technology, (d) dispensing underfill materials, (e) dispensing of conductive adhesives, even dispensing of 3D electrical interconnects. There are three reasons, why printable electronics is gaining considerable attention. The first is that the printing process can be applied to many different kinds of substrates, and also three-dimensional printing is possible. This enables the changing of the whole system of producing electronic devices, including the design and manufacturing phases, material selection, and device structure and architecture. Second, printed electronics offers better economics to electronics manufacturing. Traditional electronics is cheap only on the mass production scale, in contrast to printing, and especially inkjet printing, which offers flexible and cheap production for tailored small-volume products. Third, printing offers new business models. E.g. Inkjet technology enables also ‘‘desktop manufacturing’’, which applies to small-scale micro factories with small fixed costs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2d3dprintingtorealizeinnovativeelectronicproducts-170906082134-thumbnail.jpg?width=120&height=120&fit=bounds" /> Most people active in electronics industry are not yet aware that 3D printing can become a game changer. Currently printing and dispensing is done on a limited scale in the electronics industry. For instance: (a) printing of conformal coatings, (b) glob topping of bare dies, (c) dam and fill as packaging technology, (d) dispensing underfill materials, (e) dispensing of conductive adhesives, even dispensing of 3D electrical interconnects. There are three reasons, why printable electronics is gaining considerable attention. The first is that the printing process can be applied to many different kinds of substrates, and also three-dimensional printing is possible. This enables the changing of the whole system of producing electronic devices, including the design and manufacturing phases, material selection, and device structure and architecture. Second, printed electronics offers better economics to electronics manufacturing. Traditional electronics is cheap only on the mass production scale, in contrast to printing, and especially inkjet printing, which offers flexible and cheap production for tailored small-volume products. Third, printing offers new business models. E.g. Inkjet technology enables also ‘‘desktop manufacturing’’, which applies to small-scale micro factories with small fixed costs.

2014 2D and 3D printing to realize innovative electronic products from Jan Eite Bullema

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0 2014 Medical applications of Micro and Nano Technologies /slideshow/medical-applications-of-micro-and-nano-technologies/79476660 medicalapplicationsofmntlabonachip-170906073422
An overview of the use of micro and nano technology in amdedical applications. I gave this presentation for ten year (2004-2014) at TU/e]]>
An overview of the use of micro and nano technology in amdedical applications. I gave this presentation for ten year (2004-2014) at TU/e]]> Wed, 06 Sep 2017 07:34:22 GMT /slideshow/medical-applications-of-micro-and-nano-technologies/79476660 JanEiteBullema@slideshare.net(JanEiteBullema) 2014 Medical applications of Micro and Nano Technologies JanEiteBullema An overview of the use of micro and nano technology in amdedical applications. I gave this presentation for ten year (2004-2014) at TU/e <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/medicalapplicationsofmntlabonachip-170906073422-thumbnail.jpg?width=120&height=120&fit=bounds" /> An overview of the use of micro and nano technology in amdedical applications. I gave this presentation for ten year (2004-2014) at TU/e

2014 Medical applications of Micro and Nano Technologies from Jan Eite Bullema

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0 2016 How to make big data productive in semicon manufacturing /slideshow/how-to-make-big-data-productive-in-semicon-manufacturing/79476412 howtomakebigdataproductiveinsemiconmanufacturing-170906072349
PMML, Predictive Model Markup Language, Prognositcs, Use of Big Data in Manufacturing, Basic Architecture, Holonics, Agent Based Control, Advanced Process Control]]>
PMML, Predictive Model Markup Language, Prognositcs, Use of Big Data in Manufacturing, Basic Architecture, Holonics, Agent Based Control, Advanced Process Control]]> Wed, 06 Sep 2017 07:23:49 GMT /slideshow/how-to-make-big-data-productive-in-semicon-manufacturing/79476412 JanEiteBullema@slideshare.net(JanEiteBullema) 2016 How to make big data productive in semicon manufacturing JanEiteBullema PMML, Predictive Model Markup Language, Prognositcs, Use of Big Data in Manufacturing, Basic Architecture, Holonics, Agent Based Control, Advanced Process Control <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/howtomakebigdataproductiveinsemiconmanufacturing-170906072349-thumbnail.jpg?width=120&height=120&fit=bounds" /> PMML, Predictive Model Markup Language, Prognositcs, Use of Big Data in Manufacturing, Basic Architecture, Holonics, Agent Based Control, Advanced Process Control

2016 How to make big data productive in semicon manufacturing from Jan Eite Bullema

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0 2015 Reliability of complex systems /slideshow/reliability-of-complex-systems/79476126 reliabilityofcomplexsystems-170906071404
Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase, and defense budgets decrease. Reliability has been a recognized performance factor for at least 50 years. During World War II, the V-1 missile team, led by Dr. Wernher von Braun, developed what was probably the first reliability model. The model was based on a theory advanced by Eric Pieruschka that if the probability of survival of an element is 1/x, then the probability that a set of n identical elements will survive is (1/x)n . The formula derived from this theory is sometimes called Lusser’s law (Robert Lusser is considered a pioneer of reliability) but is more frequently known as the formula for the reliability of a series system: Rs = R1 x R2 x . . x Rn. ]]>
Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase, and defense budgets decrease. Reliability has been a recognized performance factor for at least 50 years. During World War II, the V-1 missile team, led by Dr. Wernher von Braun, developed what was probably the first reliability model. The model was based on a theory advanced by Eric Pieruschka that if the probability of survival of an element is 1/x, then the probability that a set of n identical elements will survive is (1/x)n . The formula derived from this theory is sometimes called Lusser’s law (Robert Lusser is considered a pioneer of reliability) but is more frequently known as the formula for the reliability of a series system: Rs = R1 x R2 x . . x Rn. ]]> Wed, 06 Sep 2017 07:14:04 GMT /slideshow/reliability-of-complex-systems/79476126 JanEiteBullema@slideshare.net(JanEiteBullema) 2015 Reliability of complex systems JanEiteBullema Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase, and defense budgets decrease. Reliability has been a recognized performance factor for at least 50 years. During World War II, the V-1 missile team, led by Dr. Wernher von Braun, developed what was probably the first reliability model. The model was based on a theory advanced by Eric Pieruschka that if the probability of survival of an element is 1/x, then the probability that a set of n identical elements will survive is (1/x)n . The formula derived from this theory is sometimes called Lusser’s law (Robert Lusser is considered a pioneer of reliability) but is more frequently known as the formula for the reliability of a series system: Rs = R1 x R2 x . . x Rn. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/reliabilityofcomplexsystems-170906071404-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reliability is a discipline that continues to increase in importance as systems become more complex, support costs increase, and defense budgets decrease. Reliability has been a recognized performance factor for at least 50 years. During World War II, the V-1 missile team, led by Dr. Wernher von Braun, developed what was probably the first reliability model. The model was based on a theory advanced by Eric Pieruschka that if the probability of survival of an element is 1/x, then the probability that a set of n identical elements will survive is (1/x)n . The formula derived from this theory is sometimes called Lusser’s law (Robert Lusser is considered a pioneer of reliability) but is more frequently known as the formula for the reliability of a series system: Rs = R1 x R2 x . . x Rn.

2015 Reliability of complex systems from Jan Eite Bullema

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0 2012 Reliable and Durable Micro Joining /slideshow/reliable-and-durable-micro-joining/79475897 reliabledurablemicrojoiningfinal-170906070508
What are micro interconnections? Reliable electrical micro interconnections with long lifetime expectations? Solder micro interconnects and common failure mechanisms Adhesive micro interconnect and common failure mechanisms How to achieve durability in a micro interconnect Conclusion ]]>
What are micro interconnections? Reliable electrical micro interconnections with long lifetime expectations? Solder micro interconnects and common failure mechanisms Adhesive micro interconnect and common failure mechanisms How to achieve durability in a micro interconnect Conclusion ]]> Wed, 06 Sep 2017 07:05:08 GMT /slideshow/reliable-and-durable-micro-joining/79475897 JanEiteBullema@slideshare.net(JanEiteBullema) 2012 Reliable and Durable Micro Joining JanEiteBullema What are micro interconnections? Reliable electrical micro interconnections with long lifetime expectations? Solder micro interconnects and common failure mechanisms Adhesive micro interconnect and common failure mechanisms How to achieve durability in a micro interconnect Conclusion <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/reliabledurablemicrojoiningfinal-170906070508-thumbnail.jpg?width=120&height=120&fit=bounds" /> What are micro interconnections? Reliable electrical micro interconnections with long lifetime expectations? Solder micro interconnects and common failure mechanisms Adhesive micro interconnect and common failure mechanisms How to achieve durability in a micro interconnect Conclusion

2012 Reliable and Durable Micro Joining from Jan Eite Bullema

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0 https://cdn.slidesharecdn.com/profile-photo-JanEiteBullema-48x48.jpg?cb=1704367503 Micro Systems Zero Defects Machine Learning Data Science ASQ Certified Reliability Engineer ASQ Certified Six Sigma Black Belt APICS Certification Integrated Resource Management APICS Certification Production and Inventory Management Specialties: Process Improvement and Optimization (DMAIC) Design for Six Sigma (DMADV) Design of Experiments (Classic, Taguchi and Shainin) home.hccnet.nl/j.bullema/ https://cdn.slidesharecdn.com/ss_thumbnails/2018exampleofadigitaltwinfor3dprinting-210514092432-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/2018-example-of-a-digital-twin-for-3-d-printing/248312164 2018 Example of a Digi... https://cdn.slidesharecdn.com/ss_thumbnails/mfmanufacturingworkshopelectricalinterconnectsinmirofluidicsimec2017-200409074653-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/2017-electrical-interconnects-in-miro-fluidics/231693224 2017 Electrical interc... https://cdn.slidesharecdn.com/ss_thumbnails/2012biocompatibelememspackaging-190907123701-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/2012-biocompatibele-mems-microsystems-packaging/169829220 2012 Biocompatibele ME...