[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126954-en":3,"doc-seo-126954-105":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},126954,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Utilizing Machine Learning to Assess Data Collection Methods in Manufacturing and Mechanical Engineering","This research examines how machine learning (ML) reshapes data collection practices in manufacturing and mechanical engineering, highlighting advantages over traditional approaches. Using data from 20 case studies and 15 industry reports, it shows that neural networks and support vector machines improve accuracy, efficiency, and reliability. ML methods better handle large datasets, automate data processing, and reduce human error, strengthening data quality and operational performance. The study also reports measurable gains in predictive maintenance and quality control, including lower downtime, fewer defect-detection mistakes, streamlined workflows, and cost savings, while optimizing process parameters and identifying bottlenecks.","RESEARCH ARTICLE   OPEN ACCESS   \nUTILIZING MACHINE LEARNING TO ASSESS DATA COLLECTION METHODS IN MANUFACTURING AND MECHANICAL ENGINEERING  \n1 Md Aliahsan Bappy , 2 Manam Ahmed   \n1 Mechanical Engineering, College of Engineering, Lamar University, Texas, USA [email:](email: mbappy@lamar.edu)[ mbappy@lamar.edu](email: mbappy@lamar.edu)  \n2 Mechanical Engineering, College of Engineering, Lamar University, Texas, USA  \n[email:](email: manam.ahmed1996@gmail.com)[ manam.ahmed1996@gmail.com](email: manam.ahmed1996@gmail.com)  \nThis study explores the significant impact of machine learning (ML) on data collection methods within the manufacturing and mechanical engineering sectors, emphasizing its superiority over traditional techniques. By analyzing data from 20 case studies and 15 industry reports, the research highlights how ML models such as neural networks and support vector machines enhance accuracy, efficiency, and reliability. The findings reveal that ML-based methods excel in handling large datasets, automating processes, and reducing human error, thereby improving data quality and operational performance. Applications in predictive maintenance and quality control demonstrate substantial reductions in equipment downtime and defect detection errors, alongside streamlined workflows and cost savings. Additionally, the study shows that ML can optimize process parameters and identify bottlenecks more effectively, leading to enhanced overall efficiency in industrial operations. These results underscore the transformative potential of ML in optimizing data collection practices, marking a significant advancement in industrial operations and paving the way for more innovative and efficient practices across the sector.  \nSubmitted: May 02, 2024  \nAccepted: June 05, 2024  \nPublished: June 13, 2024 Corresponding Author:  \nMd Aliahsan Bappy  \nMechanical Engineering, College of Engineering, Lamar University, Texas, USA  \n[email:](email: mbappy@lamar.edu)[ mbappy@lamar.edu](email: mbappy@lamar.edu)  \nMachine Learning, Data Collection, Manufacturing, Mechanical Engineering, Predictive Maintenance, Quality Control, Operational Efficiency  \n 10.69593/ajsteme.v4i02.73  \n1 Introduction  \nIn the modern era of Industry 4.0, the manufacturing and mechanical engineering sectors are undergoing profound transformations driven by rapid technological advancements (Huang et al., 2006) . These advancements have introduced a new paradigm where traditional methods are increasingly being replaced or augmented by digital technologies. One of the most impactful of these technologies is machine learning (ML), which has emerged as a critical tool for enhancing data collection and analysis processes. According to Lehr et al. (2020) , ML algorithms enable more precise and efficient operations by automating data processing tasks that were previously prone to human error and inefficiencies. Similarly, Rodič (2017) emphasizes that ML not only improves the accuracy of data collection but also facilitates the handling of large volumes of data, which is crucial in today's dataintensive industrial environment.  \nTraditional data gathering methods in manufacturing and mechanical engineering have long been criticized for their inherent limitations. Methods such as manual recording and basic sensor technologies are often plagued by issues like human error, time consumption, and insufficient data granularity. Schütze et al. (2018) argue that these traditional methods fail to meet the  \ndemands of modern manufacturing environments that require high precision and rapid data processing. Furthermore, van Stein et al. (2016) notes that the inefficiencies associated with manual data collection can lead to significant delays and increased operational costs. The advent of ML offers a promising solution to these challenges by providing automated, accurate, and real-time data collection capabilities.  \nThe integration of ML into data collection processes has shown significant promise ","cbCaioWwmurArtiC","https://ap.wps.com/l/cbCaioWwmurArtiC","pdf",691270,1,12,"English","en",105,"# Introduction\n## Traditional data gathering limitations\n## ML-driven improvements and evidence\n## Applications: predictive maintenance and quality control\n## Broader industrial implications","[{\"question\":\"How does machine learning improve data collection in manufacturing and mechanical engineering?\",\"answer\":\"It enhances accuracy and efficiency by automating data processing and identifying patterns and anomalies that traditional methods often miss.\"},{\"question\":\"What evidence sources were used to support the study’s conclusions?\",\"answer\":\"The research analyzes data from 20 case studies and 15 industry reports to compare ML-based approaches with traditional techniques.\"},{\"question\":\"Where are ML-based data collection methods especially useful in industrial operations?\",\"answer\":\"They are applied to predictive maintenance and quality control, reducing equipment downtime and defect-detection errors while streamlining workflows.\"}]","Utilizing Machine Learning to Assess Data Collection Methods in Manufacturing and Mechanical Engineering | 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