Assessment of Wear Resistance in Additively Manufactured Tool Steels Under Industrial Milling Conditions

Thesis Abstract

Wear resistance is a critical performance criterion for tool steels used in high-precision milling, where conventional wrought materials increasingly give way to additively manufactured equivalents that promise enhanced strength-to-weight ratios, microstructural tunability, and extended service life. The study addresses the gap in empirical understanding of how additive manufacturing (AM) parameters and post-process treatments influence wear behavior under industrial milling conditions, with a focus on correlating microstructural features to wear mechanisms and tool life. The aim is to quantify wear resistance of AM tool steels and identify parameter–outcome linkages that enable predictive maintenance and optimized process windows. Specific objectives include (i) evaluating the wear performance of candidate AM tool steels produced by selective laser melting (SLM) across three layer thicknesses (20, 40, 60 µm) and four laser powers (200, 250, 300, 350 W); (ii) assessing the effects of post-process treatments—hot isostatic pressing (HIP) and double-temper heat treatment—on hardness, fracture toughness, and wear rate; (iii) characterizing wear mechanisms via debris analysis and surface profilometry after standardized milling tests; (iv) developing regression models and ANOVA-based sensitivity analyses to link AM parameters, microstructural metrics (grain size, prior-austenite grain boundaries, bainitic/martensitic fraction) and surface integrity to wear resistance; and (v) proposing a predictive framework for tool life under given milling loads. The methodology adopts an empirical field-oriented design with controlled laboratory replication to mimic industrial milling environments. A population of three commercially relevant tool-steel chemistries (AISI H13, H13-Modified, and M300) will be produced by SLM, yielding a total of 108 specimens (3 chemistries × 3 AM parameters × 3 replicate batches × 4 post-process states). Data collection will integrate instrumental characterization and performance testing (i) microstructural analysis by scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and X-ray diffraction (XRD) to quantify phase fractions and grain size; (ii) surface integrity assessment via roughness measurements (ARI 5200 profilometer) and residual stress evaluation (X-ray diffraction sin^2? method); (iii) hardness and fracture toughness determinations using Vickers microhardness and indentation fracture tests; (iv) wear testing under industrial milling conditions using a CNC milling rig with standardized tool paths, employing a realistic workpiece material (AISI 1045 steel) to simulate chip-workpiece interactions, and capturing wear rate through mass loss, volume loss, and flank wear measurements; (v) debris morphology and third-body interaction analysis through SEM and energy-dispersive X-ray spectroscopy (EDS); and (vi) data collection of process parameters via in-situ sensors to capture temperature, vibration, and acoustic emission during milling. Analytical approaches include multivariate regression to relate AM parameters and post-processing to wear rate, ANOVA to determine the significance of individual factors and interactions, and survival-analysis-inspired models to estimate tool life under variable loads. Regression will incorporate microstructural descriptors derived from EBSD and XRD as covariates, enabling mechanistic interpretation of wear mechanisms (abrasive, adhesive, and diffusive wear). The study will test hypotheses on the significance of layer thickness and laser power on wear resistance, and the effectiveness of HIP and double-temper treatment in mitigating microstructural defects that accelerate wear. Validity will be ensured through cross-validation, repeatability assessments across replicates, and calibration against baseline wrought tool-steel performance. Expected findings include (i) finer grain sizes and refined martensitic fractions associated with enhanced hardness and reduced wear rate, contingent on optimal layer thickness and laser power; (ii) HIP and double-temper treatments yielding reduced porosity, residual stresses, and improved surface integrity, translating to lower flank wear rates; (iii) distinct wear mechanisms dominating at different AM parameter sets, with higher laser powers potentially promoting thermal embrittlement yet improving surface hardness, thereby altering wear mode transitions; and (iv) robust predictive models enabling estimation of tool life given milling load, material chemistry, and AM/post-processing conditions. The study contributes to knowledge by integrating AM process physics, post-processing metallurgy, and wear science into a cohesive empirical framework, offering practical guidelines for selecting AM tool steels and post-processing routes to achieve targeted wear resistance in industrial milling. The main conclusion is expected to emphasize that wear resistance in AM tool steels is tunable through a deliberate combination of AM parameter optimization and post-processing, with predictable improvements in wear life under realistic milling loads. Recommendations include adopting optimized layer thickness–laser power trade-offs for specific tool geometries, implementing HIP and carefully calibrated tempering schedules, and deploying the predictive framework in manufacturing settings to inform tool procurement, maintenance scheduling, and process planning.