Recognition of control chart patterns with incomplete samples

International Research and Innovation Summit 2017

Abdelrahman Senoussi, Ahmed Miftah^1 ; Masood, Ibrahim^1 ; Abdol Rahman, Mohd Nasrull^1 ; Hassan, Mohd Fahrul^1

Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Batu Pahat

86400, Malaysia^1

关键词: Automated recognition;    Control chart pattern;    Corrective actions;    Industrial practitioners;    Manufacturing process;    Measurement sensor;    Recognition accuracy;    Training and testing;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012098/pdf
DOI  :  10.1088/1757-899X/226/1/012098

来源: IOP

【 摘 要 】

In quality control, automated recognition of statistical process control (SPC) chart patterns is an effective technique for monitoring unnatural variation (UV) in manufacturing process. In most studies, focus was given on complete patterns by assuming there is no constrain in the SPC samples. Nevertheless, there is in-practice case whereby the SPC samples cannot be captured properly due to measurement sensor error or human error. Thus, this research aims to design a recognition scheme for incomplete samples pattern that will be useful for an industrial application. The design methodology involves three phases: (i) simulation of UV and SPC chart patterns, (ii) design of pattern recognition scheme, and (iii) evaluation of performance recognition. It involves modelling of the simulated SPC samples in bivariate quality control, raw data input representation, and recognizer training and testing. The proposed technique indicates a high recognition accuracy (normal pattern = 99.5%, shift patterns = 97.5%). This research will provide a new perspective in SPC charting scheme when dealing with constraint in terms of incomplete samples, which is greatly useful for an industrial practitioner in finding the solution for corrective action.