Towards Model-based Engineering of Machining-induced Residual Stress

In light of recent trends towards near-net-shape manufacturing via precision casting, extrusion and additive manufacturing, high performance finish machining is becoming increasingly important. Moreover, functional performance of many engineered components such as turbine blades and biomedical implants is significantly impacted by the surface and sub-surface characteristics, especially residual stress, strain hardening, and microstructural alterations. However, currently available modeling approaches of surface integrity either lack predictive power or require tremendous computational time. Experimental measurement of near-surface residual stress profiles is time consuming and expensive and simple correlations between observed stress profiles and process parameters (feed, speed, edge radius, etc.) are seldom generalizable. As a result, the manufacturing industry has largely adopted an empirical approach, and designers do not leverage finishing processes for ‘pro-active surface engineering’. Moreover, there are currently no provisions to integrate critical surface integrity parameters, such as sub-surface residual stresses and micro-hardness, in the emerging Digital Thread.

This talk will present recent developments on a novel integrated approach of high-speed in-situ characterization and semi-analytical modeling that is envisioned to finally enable real-time modeling of process-induced surface integrity evolution, with particular focus on machining-induced residual stress (MIRS). Experimental results of sub-surface displacement fields and residual stress profiles, as well as relevant implications for more sustainable product and process design will be discussed. Results of a high-accuracy, high-throughput, sub-surface MIRS gradient characterization technique based on ASM E837-08 and full-field digital image correlation (DIC) will be presented. Moreover, MIRS measurements will be compared with physics-based prediction of thermal and mechanical loads and resulting MIRS profiles. Finally, a vision for more data-driven and physics-informed machining process design and optimization for tailored surface integrity of machined components will be presented.