Towards Developing Big Data Analytics for Machining Decision-Making

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 consist 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 system components are elucidated. In addition, JAVA™- and spreadsheet-based systems are developed to realize the proposed 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 summary, 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.

Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques

“…This study's research justification is the urgent need to improve both the precision and effectiveness of electric discharge machining (EDM) procedures. EDM is a commonly used process for producing complex parts made of hard materials, and its optimization can result in significant cost savings and better product quality [4]. This research aims to close current gaps in existing scholarship and advance the creation of more robust and resilient EDM practices by combining machine instruction along with programming operating mathematics [5].…”