A digital approach to automatically assess the machining-induced microstructural surface integrity

la Monaca, Andrea; Liao, Zhirong; Axinte, Dragos

Abstract

© 2020 Elsevier B.V. When it comes to advanced materials for safety-critical applications, the evaluation of the machining-induced microstructural surface integrity represents a primary aspect within the assessment of part quality. Nowadays, presence and extent of machining-induced microstructural anomalies in the workpiece subsurface is manually measured by human inspection of digital micrographs. In the present work, computer-based performance of this task is achieved through a set of algorithms designed to automatically identify microstructural anomalies resulting from material removal operations. Digital surface integrity assessment has been demonstrated with application to scanning electron micrographs exhibiting different levels of microstructural deformation and obtained under different imaging conditions. Furthermore, the digitally detected material condition has been investigated with the support of in-depth field emission gun scanning electron microscopy (FEG-SEM) and electron backscatter diffraction (EBSD) analysis. This has allowed the relationship between the material evidence observed through different strategies to be established. Finally, the set of algorithms has been applied to study the microstructural condition of a large material region, by performing sequential processing of a series of micrographs. In this way, the measurement procedure has been calibrated and its capability to perform surface-integrity evaluation on large areas in an automated and standardised way has been demonstrated.

Citation

la Monaca, A., Liao, Z., & Axinte, D. (2020). A digital approach to automatically assess the machining-induced microstructural surface integrity. Journal of Materials Processing Technology, 282, https://doi.org/10.1016/j.jmatprotec.2020.116703

• Journal Article Type: Article
• Acceptance Date: Mar 30, 2020
• Online Publication Date: Apr 8, 2020
• Publication Date: Aug 1, 2020
• Deposit Date: Apr 16, 2020
• Publicly Available Date: Apr 9, 2022
• Journal: Journal of Materials Processing Technology
• Print ISSN: 0924-0136
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 282
• Article Number: 116703
• DOI: https://doi.org/10.1016/j.jmatprotec.2020.116703
• Keywords: Modelling and Simulation; Industrial and Manufacturing Engineering; Metals and Alloys; Ceramics and Composites; Computer Science Applications
• Public URL: https://nottingham-repository.worktribe.com/output/4279380
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0924013620301175?via%3Dihub