11 accepted papers
Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult
Motivated by recent advances in optimization-based machine learning algorithms, this research proposes a suboptimal explainable scheme (SES) for monitoring and reasoning machining outcomes by data-driven decision tree models. After machining, the time-frequency features in the wavelet package are ex
Determining thresholds of the primary control loops (System 1) of an additive manufacturing (AM) process is challenging when realizing System 1 with its fast and intuitive capability for adapting to different metal powers, machine configurations, and process parameters. Based on the convolution neur
Wheel balance plays an important role in vehicle safety. The existing inspection method for wheel balance mainly relies on the off-machine measurement technique, which is time- and manpower-consuming as the worldwide requirement of the automated production system gradually increases. However, the mu
Maintaining tool availability is the key to ensure machining quality. To evaluate tool availability so as to improve its utilization, the tool diagnosis models for tool-remaining-useful-life (RUL) estimation and tool-state classification are required. These models need sufficient samples to cover al
Electrical discharge machining (EDM) can machine hard conductive workpieces that are difficult to machine using traditional machining techniques. For monitoring the EDM process using virtual metrology (VM), probes with a very high sampling rate are needed to acquire the voltage and current signals o
The capability of measuring melt pool variation is the key evaluating metal additive manufacturing quality. To measure the variation, a metrology architecture with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> melt pool measurement and
Industry 4.0 paves the way to smart factory through integrating Internet of Things, cyber-physical systems, cloud manufacturing, and big data analytics for various industrial fields. Especially, the machinery industry significantly benefits from these automation techniques in machine-tool quality in
One of the core values of Industry 4.0 targets to integrate people's demand into manufacturing for enhanced products, systems, and services for a wider variety of increasingly personalized customization of products. Thus, Industry 4.0 advances the traditional manufacturing techniques from mass produ
As semiconductor manufacturing processes are becoming more and more sophisticated, how to maintain their feasible production yield becomes an important issue. Also, how to build a smart manufacturing platform that can facilitate realizing smart factories is essential and desirable for current manufa
Industry 4.0 is set to be one of the new manufacturing objectives. The technologies involved to achieve Industry 4.0 are Internet of Things (IoT), cyber physical systems (CPS), and cloud manufacturing (CM). However, the current objectives defined by Industry 4.0 do not include zero defects; it only