A Hybrid Expert Decision Support System Based on Artificial Neural Networks in Process Control of Plaster Production – An Industry 4.0 Perspective

Abstract

Emerging technologies could affect future of factories and smartness is the main trend to receive that points. Quality was important and will be crucial in future but the question is how to build Smart Systems to guaranty quality in workshop level. This is an important challenge in Industry 4.0 paradigm. In this paper the main objective is to present practical solution under the light of Industry 4.0. The aim of this study is to presents propose a Hybrid Expert Decision Support System (EDSS) model, which integrates Neural Network (NN) and Expert System (ES) to detect unnatural CCPs and to estimate the corresponding parameters and starting point of the detected CCP. For this purpose, Learning Vector Quantization (LVQ) and Multi-Layer Perceptron (MLP) networks architecture have been designed to identify unnatural CCPs. Moreover, a rule based ES has been developed for diagnosing causes of process variations and subsequently recommending corrective action. The proposed model was successfully implemented in Construction Plaster producing company to demonstrate the capabilities and applicability of the model.

Positive Effect of Industry 4.0 on Quality and Operations Management

“…According to the surveys conducted by the research company LNS, most manufacturing companies will need to be transformed to Industry 4.0 including the conversion of quality management to Quality 4.0 [36]. This smart quality will enable intelligent monitoring and diagnosis, real-time decision making and automatic tracking of equipment and materials, especially for processes that have become more volatile and complex [39],also a prediction which states that within five years smart gloves will be most commonly used for quality control, shipping and handling [40].…”

Quality 4.0 in Action: Smart Hybrid Fault Diagnosis System in Plaster Production

“…Among the ANNs, the multilayer perceptron (MLP) was successfully exploited by many researchers in order to address the unnatural CCP recognition problem. Learning vector quantization (LVQ) is a well-applied alternative method to solve the problem of slowness in training the MLP network [4,12,20,27].…”

“…The impact of I4.0 on manufacturing is beyond just the physical production of goods, involving targeting all processes and functions to achieve flexibility, smartness, cost-effectiveness, and resilience. Artificial intelligence and machine learning are among the aforementioned technologies that can be utilized to enhance the quality as the heart of smart manufacturing [12,33,34].…”

“…Industry 4.0, with its automation, connectivity, and digital access capacity, is anticipated to be capable of increasing the efficiency and productivity of SPC. This could happen through enabling intelligent monitoring and diagnosis, automatic tracking of equipment and material, and real-time decision-making, especially in situations where the process is becoming more volatile and complex (Figure 3) [12,30].…”

“…Therefore, these decision rules are not particularly useful for CCP recognition [10,11]. Since the analysis of control charts is complicated, because it relies on considerable statistical knowledge, skill, and experience of the practitioners (quality control personnel), developing an efficient automated pattern recognition system that can ensure steady and unbiased analysis of CCPs can compensate for this gap [12]. Table 1.…”