Ghosh, A.K.; Fattahi, S.; Ura, S. Towards Developing Big Data Analytics for Machining Decision-Making. J. Manuf. Mater. Process.2023, 7, 159.
Ghosh, A.K.; Fattahi, S.; Ura, S. Towards Developing Big Data Analytics for Machining Decision-Making. J. Manuf. Mater. Process. 2023, 7, 159.
Ghosh, A.K.; Fattahi, S.; Ura, S. Towards Developing Big Data Analytics for Machining Decision-Making. J. Manuf. Mater. Process.2023, 7, 159.
Ghosh, A.K.; Fattahi, S.; Ura, S. Towards Developing Big Data Analytics for Machining Decision-Making. J. Manuf. Mater. Process. 2023, 7, 159.
Abstract
This paper presents a systematic approach to developing big data analytics for manufacturing process-relevant decision-making activities from the perspective of smart manufacturing. The proposed analytics consists of five integrated system components: 1) data preparation system, 2) data exploration system, 3) data visualization system, 4) data analysis system, and 5) knowledge extraction system. The functional requirements of the integrated systems are elucidated. In addition, JAVA- and spreadsheet-based systems are developed to realize the proposed integrated system components. Finally, the efficacy of the analytics is demonstrated using a case study where the goal is to determine the optimal material removal conditions of a dry electrical discharge machining operation. The analytics identified the variables (among voltage, current, pulse-off time, gas pressure, and rotational speed) that effectively maximize the material removal rate. It also identified the variables that do not contribute to the optimization process. The analytics also quantified the underlying uncertainty. In synopsis, the proposed approach results in transparent, big-data-inequality-free, and less resource-dependent data analytics, which is desirable for small and medium enterprises—the actual sites where machining is carried out.
Keywords
smart manufacturing; big data; manufacturing process; big data analytics; decision-making; uncertainty
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
