Process control based on pattern recognition for routing carbon fiber reinforced polymer

  1. Get@NRC: Process control based on pattern recognition for routing carbon fiber reinforced polymer (Opens in a new window)
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Journal titleJournal of Intelligent Manufacturing
Pages115; # of pages: 15
SubjectMachining; Pattern recognition; Process control; Carbon fiber reinforced polymer; Knowledge extraction; Logical analysis of data; Carbon fiber reinforced plastics
AbstractCarbon fiber reinforced polymer (CFRP) is an important composite material. It has many applications in aerospace and automotive fields. The little information available about the machining process of this material, specifically when routing process is considered, makes the process control quite difficult. In this paper, we propose a new process control technique and we apply it to the routing process for that important material. The measured machining conditions are used to evaluate the quality and the geometric profile of the machined part. The machining conditions, whether controllable or uncontrollable are used to control part accuracy and its quality. We present a pattern-based machine learning approach in order to detect the characteristic patterns, and use them to control the quality of a machined part at specific range. The approach is called logical analysis of data (LAD). LAD finds the characteristic patterns which lead to conforming products and those that lead to nonconforming products. As an example, LAD is used for online control of a simulated routing process of CFRP. We introduce the LAD technique, we apply it to the high speed routing of woven carbon fiber reinforced epoxy, and we compare the accuracy of LAD to that of an artificial neural network, since the latter is the most known machine learning technique. By using experimental results, we show how LAD is used to control the routing process by tuning autonomously the routing conditions. We conclude with a discussion of the potential use of LAD in manufacturing.
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AffiliationAerospace; National Research Council Canada
Peer reviewedYes
NPARC number21272951
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Record identifier6c99345f-abc2-43fe-9491-ed5723e01ffd
Record created2014-12-03
Record modified2016-05-09
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