Machine vision based quality control of free-form profiles in automatic cutting processes

• Published in: Computers & industrial engineering 2017-07, Vol.109, p.221-232
• Main Authors: Pacella, Massimo, Grieco, Antonio, Blaco, Marzia
• Format: Article
• Language: English
• Subjects: Cutting process; Machine vision; Profile monitoring; Quality control
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2017.04.039

•Statistical monitoring of free-form profiles with different argument variables.•The control charting procedure is based on images acquired by machine vision systems.•The control chart can be extended from the univariate case to the multivariate one. Profile monitoring has been recently considered as one of the most promising areas of research in statistical process control. Often, when physical profiles are acquired by machine vision systems, the number and locations of the argument variables can change from one item to another. In this article, non-parametric approaches for statistical monitoring of free-form profiles, e.g., characterized by irregular shapes and with different argument variables, are proposed. A machine vision system is used for process control by implementing on a computer the following procedure: (i) a feature extraction phase, which extracts contour of the produced part from image and returns it in form of polygonal curve; (ii) a registration phase, for aligning the current polygonal curve to the baseline model; (iii) a matching computation phase, which is based on the deviation area, for quantifying the discrepancies of the current polygonal curve from the baseline one; (iv) a control charting phase, which is based on univariate or multivariate statistics, for process monitoring. The automatic cutting process in leather part manufacturing is used as the reference industrial case. The leather hide is cut into parts that may assume shapes different from the ‘baseline’, e.g., the reference ideal profile. Two control charting procedures are illustrated and validated through simulated test cases and a real-life case of industrial relevance.