Technical Library | 2023-11-03 18:01:35.0
This case study explores the changing climate for manufacturing in Mexico as volumes and demand increase in the region and manufacturers, including IMI, look to grow through greater automation and closer collaboration with key technology partners like Koh Young. With interviews with Oscar Valdivia, IMI's Manufacturing Unit Manager for Guadalajara, and Philip Reyes, their Regional Engineering Head, the case study digs into how manufacturing excellence can be developed with the right use of automation and by investing in equipment that creates efficiency and quality through better use of accurate data.
Technical Library | 2010-11-18 19:19:50.0
In this article we present both a relatively new and innovative family of packages that is suitable for medium pin count needs and an innovative method for fabricating the substrates for such a package. With respect to lead count, this packaging family is
Technical Library | 2021-11-22 20:39:44.0
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
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