Technical Library | 2008-05-07 17:54:58.0
Tracking goods through manufacturing was originally accomplished with pencil, paper and human input. Barcodes introduced an automated, machine-readable tracking mechanism that streamlined all types of manufacturing. But modern printed circuit board (PCB) assemblies are running into limitations because of barcode labels. And though barcodes and RFID tags will co-exist, the relatively large barcode labels have to find increasingly scarce real estate on high density boards.
Technical Library | 2013-01-30 14:02:44.0
Many OEM’s require that individual wires and cables used in their products be clearly identified with a mark or label. For some, such as in the military and aerospace markets, wire and cable identification (or “wire ID”) is mandatory and the process is governed by stringent specifications, such as SAE AS50881 (formerly MIL5088L). For others, the decision to use wire ID is a voluntary one. This article will describe what type of information is typically identified on wire and cables, concepts for improved productivity, what types of systems are available and the pros and cons of each.
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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