IEEE Transactions on Robotics and Automation · 2001 · 87 citations · 21 references
EngineeringData ScienceManufacturing EngineeringData MiningManufacturing ProcessesQuality EngineeringDefect DetectionDefectsFault DetectionFault DiagnosisQuality Data AnalysisStatistical Process ControlDefect ClusteringDefect DiscoveryManufacturing Defect AnalysisPrinted Circuit BoardsProcess Fault Detection
An industrial case study applies data mining to address quality engineering issues in electronics assembly, focusing on solder balls that appear beneath components on printed circuit boards. The study aims to identify the root causes of solder defects in circuit boards using a data mining approach. The authors analyze selected features and collected data, then employ a data mining solution to pinpoint quality fault causes in the industrial setting. Statistical process control and design‑of‑experiment methods failed to yield conclusive results.
This paper discusses an industrial case study in which data mining has been applied to solve a quality engineering problem in electronics assembly. During the assembly process, solder balls occur underneath some components of printed circuit boards. The goal is to identify the cause of solder defects in a circuit board using a data mining approach. Statistical process control and design of experiment approaches did not provide conclusive results. The paper discusses features considered in the study, data collected, and the data mining solution approach to identify causes of quality faults in an industrial application.
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