[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117148-en":3,"doc-seo-117148-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117148,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Improving Industrial Quality Control by Machine Learning Techniques","Machine learning techniques strengthen industrial control processes by increasing accuracy, flexibility, and ease of implementation across production systems. Integrating these methods supports industrial sustainability through lower operating costs and reduced energy consumption, while raising product quality and enabling prediction of future malfunctions. The study also targets practical barriers such as insufficient or non-matching data, high complexity of algorithms, and higher implementation costs, and discusses approaches to mitigate these challenges.","JOURNAL LA MULTIAPP  \nVOL. 05, ISSUE 05 (501-520), 2024  \nDOI: 10.37899/journallamultiapp.v5i5 .1537  \nImproving Industrial Quality Control by Machine Learning Techniques Esraa Raheem Alzaidi1  \n1 College of Since, University of Al-Qadisiyah, Al-Qadisiyah, Iraq  \n*Corresponding Author: Esraa Raheem Alzaidi [Email: ](Email: Esraa.alzaidi@qu.edu.iq)[Esraa.alzaidi@qu.edu.iq](Email: Esraa.alzaidi@qu.edu.iq)  \nArticle Info  \nArticle history:  \nReceived 6 August 2024 Received in revised form 68September 2024 Accepted 20 September 2024  \nKeywords:  \nMachine Learning Techniques Flexibility  \nIndustrial Control Sustainability Production Systems Energy Consumed Cost  \nIn 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%.   \nAbstract  \nIntroduction  \nMachine learning techniques in industrial control processes are among the most important tools that can contribute to the sustainability of the industrial sector. Machine learning plays an important role in industrial control processes, as it can improve performance, reduce energy consumption, reduce costs, improve quality, and also add more Flexibility and efficiency in production systems or processes (Mokhtari et al., 2021) . Through machine learning techniques, malfunctions can be predicted by using algorithms to analyse operating data and predict potential malfunctions before they occur, which paves the way for carrying out preventive maintenance operations and avoiding malfunctions that may hinder the workflow, thus reducing the cost of repair. And improving productivity. Also, through machine learning techniques, optimal control of production processes is possible, by improving time control parameters, based on operating conditions, as well as all variables that may lead to improving the efficiency of the process and in terms of quality, reducing time, reducing energy consumption, and reducing the consumption of raw materials. Not only that, but through computer vision, which means realizing vision through computer programs that is very similar  \n501  \nISSN: 2716-3865 (Print), 2721-1290 (Online)  \nCopyright © 2024, Journal La Multiapp, Under the license CC BY-SA 4.0  \nto human vision, but instead of using biological organs such as the eye, specific algorithmsand programs are used that simulate human perception of some specific processes, and through computer vision. Product inspections can be carried out automatic","cbCaikfSWWW00yGm","https://ap.wps.com/l/cbCaikfSWWW00yGm","pdf",1684043,1,20,"English","en",105,"# Abstract\n# Introduction\n## Benefits of Machine Learning in Industrial Control\n## Challenges and Obstacles\n## Study Aims","[{\"question\":\"How do machine learning techniques improve industrial control processes?\",\"answer\":\"They enhance performance and flexibility, reduce energy use and costs, improve product quality, and enable prediction of malfunctions using analysis of operating data.\"},{\"question\":\"What sustainability outcomes are associated with machine learning in industry?\",\"answer\":\"Economic sustainability is supported by lowering repair and maintenance costs, reducing energy consumption, and improving overall production efficiency and quality.\"},{\"question\":\"What challenges must be addressed to apply machine learning in industrial control?\",\"answer\":\"Key challenges include needing data and algorithms that match the data, ensuring sufficient and diverse datasets, dealing with algorithmic complexity, and managing relatively high implementation costs.\"}]","Improving Industrial Quality Control by Machine Learning Techniques | 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