Optimized mechanical properties of AISI 316 stainless steel via robotized ultrasonic multi-needle peening: An experimental and numerical study

Conventional peening methods often struggle to achieve uniform, controllable strengthening on thin-walled and complex components under combined thermo–mechanical loading. This study develops a robotized ultrasonic multi-needle peening (UMNP) system and a validated experimental–numerical framework for automated process planning and transferable mechanism-based design. The process decouples robotic coverage control (trajectory, speed, step-over) from local impact severity (ultrasonic amplitude, air pressure, stand-off) using a 65-needle array that provides stochastic impacts with spatial averaging. A parameterized multi-needle FE model (ABAQUS/Python), informed by high-speed measurements of needle-tip velocity distributions, predicts plastic indentation and residual-stress profiles with < 10 % deviation and is demonstrated on both a thin-walled AISI 316 cylindrical shell (circumferential segmentation with rotary indexing) and a flat plate (raster zig-zag tracks). Two stable regimes (UMNP-2: 21 μ m; UMNP-3: 35 μ m amplitude) bound the operating window and establish impact energy density and overlap/coverage as governing design rules. For AISI 316 stainless steel cylindrical shell tested at 10–40 N and 25–300 °C, the high-energy regime produces a 532 μ m graded hardened layer, increases surface hardness to 410 HV (113%), and introduces peak compressive residual stress of − 1 . 1 GPa with EBSD-confirmed grain refinement to 9 μ m. A wear-mechanism transition is identified: wear increases at 100 °C due to brittle oxide spallation but decreases at 200–300 °C via a stable Cr 2 O 3 -rich tribo-oxide, improving wear resistance by 33% at 300 °C (with friction–wear decoupling). The combined automation strategy and validated modeling framework enable scalable surface engineering of thin-walled and non-axisymmetric components for aerospace, energy, and marine applications. • A novel robotized UMNP system decouples robotic motion from ultrasonic impacts for uniform treatment. • A validated multi-needle FE model predicts residual stress within <10% error, generalizing process mechanics. • Process rules achieve a deep layer (>500 μ m) with 113% higher hardness and –1.1 GPa stress. • Surface refinement enables a tribo-oxidation transition, enhancing high-temperature wear resistance by 33%.