Predictive Quality in Electronics Manufacturing

January 25, 2019

Predictive Quality in Electronics Manufacturing

Speaker: Dr.-Ing. Jochen Böning, Siemens AG

Due to the increasing competitive pressure of globalization, the production of high-quality products is crucial for the long-term success of a company. In order to guarantee the delivery and transfer of faultless products, it is therefore essential to ensure quality. In the classical sense, this is in turn associated with high testing volumes, so that on the one hand high investments are made for testing equipment and on the other hand high testing costs are incurred. Especially against the background of reducing throughput times, increasing flexibility and increasing efficiency, strategies for reducing the inspection scope are economically attractive compared to the acquisition of further systems and test equipment. With a strong brand awareness and in order to avoid high recall actions, this was however in the past a rather unusual measure for traditional companies. Due to the increasing spread of modern information and communication technologies, however, the use of data-driven methods, e.g. the use of data mining methods, in industrial production environments is also favoured in this context and, in addition, high economic potentials can be leveraged.

The use of data mining in production offers great potential for the development and integration of strategies for the optimization of products and production processes. By applying statistical methods to structured and unstructured data, previously unknown patterns and laws can be extracted and new knowledge can be generated. This enables the creation of forecast models for data-based and computer-aided prediction of future events.

This presentation will show how the use of data mining in electronics production can relieve X-ray inspection by predicting quality. On the basis of collected process data, prognosis models are trained, which allow a prediction of the expected X-ray result of the printed circuit boards. Early knowledge of the product quality to be expected enables early control interventions, so that on the one hand the inspection scope is reduced and on the other hand additional added value of defective products is prevented.

Dr.-Ing. Jochen Böning

Manufacturing Consultant, Siemens AG

Dr. Jochen Bönig studied mechanical engineering at the Technical University of Nuremberg and graduated with distinction in 2011 as Master of Science. During his studies he was a scholarship holder of the Faculty of Applied Mathematics and Physics in the field of numerical solution methods and supported research projects on laser beam source development and thermal long-term storage. With his dissertation 'Integration of the system behavior of automotive high-voltage cables into the virtual validation by structural-mechanical simulation', he received his doctorate degree Dr.-Ing. at the Friedrich-Alexander-University Erlangen-Nuremberg in 2016. From 2011 to 2015 he worked as a research assistant at the Chair of Manufacturing Automation and Production Systems in the field of System Engineering, researching virtual validation in assembly. From 2015 to 2018, Dr. Bönig worked at Siemens AG in Erlangen in production engineering at the equipment plant. Here he was responsible for Lean Virtual Engineering and acted as a project manager for flexible automation technology. Since mid-2018, Dr. Bönig has been a Manufacturing Consultant in the Factory Automation business unit of Siemens AG, where he is responsible for the transformation to a digital factory and heads the Customer Experience group.