In-situ workpiece perception: A key to zero-defect manufacturing in Industry 4.0 compliant job shops

被引:9
作者
Babalola, Simeon A. [1 ,2 ]
Mishra, Debasish [3 ]
Dutta, Samik [1 ,2 ]
Murmu, Naresh C. [1 ,2 ]
机构
[1] CSIR Cent Mech Engn Res Inst CMERI, Mahatma Gandhi Ave, Durgapur 713209, W Bengal, India
[2] Acad Sci & Innovat Res AcSIR, Ghaziabad 201002, India
[3] Univ Connecticut, UTC Inst Adv Syst Engn, 159 Discovery Dr, Storrs, CT 06268 USA
关键词
Workpiece perception; Zero-defect manufacturing; Friction Stir Welding; Machine learning; Industrial recommender system; STIR WELDING PROCESS; TO-ORDER; FRICTION; IDENTIFICATION; MACHINE;
D O I
10.1016/j.compind.2023.103891
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Job shop manufacturing is characterized by excellent job flexibility and highly customizable products. The dynamic nature of jobs in this manufacturing segment poses a relatively high challenge to actualizing zero-defect manufacturing (ZDM). The continuous emergence of new engineering materials and improved consumer's contributions to product development driven by IIoT further complicates the uphill task. This study develops a methodological framework that positions in-situ workpiece perception and industrial recommender system as tools to trivialize the challenge of ZDM in job shops. In-situ workpiece perception was experimented using a case study of friction stir welding (FSW), a thermomechanical manufacturing process where existing algorithms for online quality assessment are material-specific. The novelty in this study is deciphering unique applications for the hitherto jettisoned sensor data acquired prior to the FSW tool traverse (the actual welding stage), ensuring that the material perception process does not induce defects in the products. This study presents two major contributions towards attaining first-time-right ZDM in job shops. First, a novel interlink for IIoT-driven production planning to aid the selection of optimal process parameters; second, the selection of appropriate quality assessment algorithms during the welding process.
引用
收藏
页数:13
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