Manufacturing Process Optimization Using Open Data and Different Analysis Methods

Material removal processes, or machining (encompassing milling, turning, and drilling), constitute an indispensable facet of manufacturing. To attain optimal machining performance—characterized by a high material removal rate, minimal tool wear, and superior surface finish—cutting conditions (such as the depth of the cut, feed rate, and cutting speed) must be meticulously optimized. Traditionally, this optimization has been contingent upon datasets collected from a singular, reliable source. However, in the paradigm of smart manufacturing, this data dependency is transitioning from a single source to a confluence of heterogeneous, open sources. Accordingly, this study elucidates a systematic approach for harnessing open-source machining datasets in a cogent and efficacious manner. Specifically, an open data source pertaining to turning operations, comprising 1013 records related to tool wear, is studied. From this corpus, 289 records corresponding to mild steel (JIS code: S45C) undergo rigorous analysis via Analysis of Variance (ANOVA), Signal-to-Noise Ratio (SNR), and possibility distributions. The empirical findings reveal that possibility distributions exhibit superior efficacy over ANOVA and SNR in extracting salient insights for optimization. Nevertheless, in certain scenarios, an integrative approach leveraging all three methods is requisite to attain optimal results. This study thus proffers a pragmatic computational framework, augmenting the optimization of machining within the purview of smart manufacturing.