Improving Industrial Quality Control by Machine Learning Techniques

Esraa Raheem Alzaidi

doi:10.37899/journallamultiapp.v5i5.1537

Esraa Raheem Alzaidi College of Since, University of Al-Qadisiyah, Al-Qadisiyah, Iraq

DOI: https://doi.org/10.37899/journallamultiapp.v5i5.1537

Keywords: machine learning techniques, flexibility, industrial control, sustainability, production systems, energy consumed, cost

Abstract

In light of the development of computing systems and machine learning techniques, the development of industrial control processes in production processes has become easier, more accurate, and more flexible. Machine learning techniques, after being integrated with industrial control processes, have become one of the most important tools that achieve sustainability in the field of industry. Thus, economic sustainability is achieved. Through it, production systems can be improved, costs reduced, energy consumption reduced, quality increased, and future malfunctions predicted. Thus, reducing the cost of repair and maintenance. The study aims to clarify the importance of machine learning techniques in industrial control processes, and that integrating machine learning techniques with industrial control techniques contributes to achieving sustainability in the field of industry. The study also aims to identify the obstacles and challenges facing the field of machine learning techniques in the industrial control process and how to solve them. Through a combination of description, analysis, comparison and simulation methodologies, the results indicated that 10% to 20% of the total cost was saved, 1% to 10% of the energy consumed was saved, and the response was improved by a rate ranging between 10% and 20%. The results also indicated to improve system flexibility using machine learning techniques, increase product quality, and reduce operation time. The use of machine learning techniques to improve the proposed model led to an improvement in reducing the cost by 10%, improving energy consumption by 1%, and improving the response by 1%.

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