Statistical Process Control with Intelligence Based on the Deep Learning Model

Abstract

Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified.

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

“…Despite of this method simplicity, it is necessary to manually determine if there is any abnormality in the control charts and what kind of abnormality occurs. Furthermore, it is easy to detect abnormalities beyond the control limit, but difficult to do so within the control limit, which is easily affected by the experience level of quality control personnel [358].…”

Supply Chain Logistics Information Quality Evaluation From Just-in-Time Perspective

“…Dr. W Edwards Deming, an engineer from Bell Laboratories popularizes it worldwide after World War II [51]. Since then, SPC plays an important role in product quality improvement and quality supervision [52]. Now, SPC is not only a key tool of quality improvement but also a philosophy, a strategy, and a set of methods for ongoing improvement of systems, processes, and outcomes [50].…”

“…The chart usually includes a series of measurement plots and three horizontal lines (the center line (typically, the mean), the upper control limit (UCL), and the lower control limit (LCL)) [51], [53]. Reference [52] presents nine typical control charts patterns of the production process. In the traditional application of SPC, the UCL and LCL are usually calculated from the inherent in the data, and most SPC experts recommend control limits set at ±3σ , where σ is the standard deviation of uncorrelated noise in the process [51], [53].…”

Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns

“…However, as time goes on, the manufacturing process may experience tool wear, operator fatigue, seasonal effects, failure of machine parts, fluctuation in power supply, and lose fixture, among others. For example, a sudden shift pattern could be attributed to failures in machined parts, and a cyclic pattern could be attributed to seasonal changes like fluctuation in temperature [3,4]. Identification and classification of these patterns complemented with process knowledge could be linked to a set of assignable causes for diagnosis purposes.…”

“…Sugumaran and Ramachandran [11] reported an application of a decisio for feature selection and for generation of rule set for a fuzzy classifier for fault dia of roller bearing. Recently Zan et al [4,12] reported a potential application of convo neural network (CNN) and information fusion for CCPR. However, CNN remain o especially among new researchers who need to understand the classification logic to exploring more complex and advanced techniques.…”