A framework for data-driven decision making in advanced manufacturing systems: Development and implementation DOI

Vimlesh Kumar Ojha,

Sanjeev Goyal, Mahesh Chand

и другие.

Concurrent Engineering, Год журнала: 2024, Номер 32(1-4), С. 58 - 77

Опубликована: Ноя. 5, 2024

Integration of sophisticated technologies such as Internet Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing (AMS). However, implementation data-driven decision making in AMS still remains challenging due to heterogeneity, real-time processing demands, integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption DDDM enhance its decision-making capabilities. consists six stages: stage, sensing knowledge application stage. The proposed leverages extract actionable insights from diverse datasets, integrates CPS create seamless interaction between digital systems, employs IoT acquisition monitoring. is validated through comprehensive case study involving CNC milling machine dataset, demonstrating significant improvements operational efficiency, accuracy, response time. involves detailed collection steps, preprocessing, analysis, showcasing framework’s practical effectiveness. results show that addresses existing challenges provides scalable solution AMS.

Язык: Английский

Machine Learning for Predicting Resistance Spot Weld Quality in Automotive Manufacturing DOI Creative Commons

Nuttapong Chuenmee,

Nattachai Phothi,

Kontorn Chamniprasart

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103570 - 103570

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

3
A framework for data-driven decision making in advanced manufacturing systems: Development and implementation DOI

Vimlesh Kumar Ojha,

Sanjeev Goyal, Mahesh Chand

и другие.

Concurrent Engineering, Год журнала: 2024, Номер 32(1-4), С. 58 - 77

Опубликована: Ноя. 5, 2024

Integration of sophisticated technologies such as Internet Things, cyber physical systems and big data analytics have revolutionized the advanced manufacturing (AMS). However, implementation data-driven decision making in AMS still remains challenging due to heterogeneity, real-time processing demands, integration complexities. This paper overcomes this challenge by presenting a novel framework for adoption DDDM enhance its decision-making capabilities. consists six stages: stage, sensing knowledge application stage. The proposed leverages extract actionable insights from diverse datasets, integrates CPS create seamless interaction between digital systems, employs IoT acquisition monitoring. is validated through comprehensive case study involving CNC milling machine dataset, demonstrating significant improvements operational efficiency, accuracy, response time. involves detailed collection steps, preprocessing, analysis, showcasing framework’s practical effectiveness. results show that addresses existing challenges provides scalable solution AMS.

Язык: Английский

Процитировано

0