Development of digital characteristics of machining based on physics-guided data

This article presents a new concept of analysing machining (big) data as the digital characteristics (DC) of machining to identify key insights from data. The DC are a mapping of unique data features to represent a specific behaviour for the stages of design, manufacturing and applications. Its development is based on the visualization of processed data. Herein, the processed data are sub-grouped into distinctive characteristics i.e., mechanical characteristics, thermal characteristics and interfacial characteristics. The research value of this article lies on the proposal, detailed illustration, and interpretation of digital characteristics of machining, and how it is used to discover data driven machining research. Prior to building DC, the physics-guided data was collected from experimentally validated finite element analysis (FEA) models. The detailed methodology of FEA-based approach, from model development to model validation, is presented as a mean to collect time series and element-series data of machining. Some of the observed key attributes are the zones of high strain, temperature, the response distribution on boundaries, and evolutionary natures in interfacial properties. The future guidance on research based on the digital characteristics has been presented too.

Automated summary

This article presents a new concept of analysing machining data through digital characteristics, which allows for identifying key insights. The digital characteristics map unique data features to specific behaviors in the stages of design, manufacturing, and applications. The research value of this article lies in its proposal, detailed illustration, and interpretation of digital characteristics and their application in data-driven machining research.