|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|115477||2017||6 صفحه PDF||سفارش دهید||4370 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Procedia CIRP, Volume 60, 2017, Pages 38-43
This paper introduces the novel concept of self-evolving measurement system with the aim of rapidly identifying and localising defect patterns in multi-stage assembly systems with compliant non-ideal parts. This allows to enhance the level of diagnosability which cannot be achieved using fixed and static pre-determined measurement systems. The proposed methodology helps to identify and select new measurement points to increase the likelihood of isolating root causes of defects. This happens by automatically classifying defect patterns and associating them to critical key control characteristics. The methodology integrates supervised machine learning tools with first principle engineering simulations. It is based on the principle of pattern similarity, taking into account data generated by the self-evolving measurement system. The methodology is demonstrated and validated using the results of an automotive door assembly system.