Topic discovery innovations for sustainable ultra-precision machining by social network analysis and machine learning approach

Ultra-precision machining (UPM) is an advanced manufacturing technology that experiences increasing demand. Therefore, it is necessary to minimize the environmental impacts from its enormous consumptions of resources. Achieving sustainable UPM is still a challenge is it involves complicated influencing relationships among relevant factors like energy consumption, and human health, which could affect sustainable performance. And some influencing relationships between two parameters have not been fully studied yet, which are named as undiscussed two-parameter relationships. Therefore, this paper proposed a new topic discovery model based on social network analysis (SNA) and machine learning approach to discover the undiscussed two-parameter relationships with high potential value in the sustainable UPM research field. By using the link prediction metrics obtained by SNA and principal components analysis in this study, the interactive relationships among the parameters of sustainable ultra-precision machining are determined to discover the potential values of undiscussed two-parameter topics. Then, the k-means algorithm is applied to classify the topics based on the similarity of the metrics results to present the potential value distribution of the undiscussed topics in sustainable UPM. From the metrics results, the topic of the relationship between environmental damage and resource waste was found to be the most valuable potential two-parameter topic in the area of sustainable UPM. This paper also contributes to showing the potential value distribution of undiscussed two-parameter relationships and predicting the sustainable development trend in the UPM sectors.