Year: 2026 | Month: October | Volume: 13 | Issue: 10 | Pages: 88-101
DOI: https://doi.org/10.52403/ijrr.20261008
Simulation-Driven Distortion, Thermal-Strain, Scan-Strategy, Melt-Pool and Porosity Analysis of a Generatively Designed AlSi10Mg Fastener Component Using ANSYS Additive
Vamsi Sai Molleti1, Kondeti Sravanth2, Satya Narayan Patro Kattakota3, Venkata Mani Arveti4, Pavan Teja Molleti5
1Cyient, Structures Engineer, Hyderabad
2,3Cyient, Technical Lead Structures Engineer, Hyderabad
4Cyient, Customer Interface Sr Manager, East Hartford, US
5Tech Mahindra, Senior Design Engineer, Hyderabad
Corresponding Author: Vamsi Sai Molleti
ABSTRACT
Metal additive manufacturing (AM) processes, such as L-PBF, are highly sensitive to the thermal history induced by the process. This results in objects containing residual stresses, distortions, and porosity, which can severely impact the dimensions of the printed object as well as its mechanical properties. To combat these issues, designers engage in simulation-driven process planning. This allows designers to design for additive manufacturing (DFAM) by predicting and avoiding defects in the design before it is physically printed. This paper presents a multi-parametric, simulation-based case study to design and optimize a fastener component, Exercise-80, made of AlSi10Mg. The generatively designed part is analysed using the ANSYS Additive suite of simulation tools. A range of five different studies were performed on the same generatively optimized part, and the results are discussed in detail. The five different studies were: (i) an assumed (uniform) strain distortion study; (ii) a thermal-strain-based distortion study; (iii) an anisotropic scan-strategy-based distortion study; (iv) a single-bead parametric melt-pool study based on a 3×3 DOE of laser power and scan speed; and (v) a full-factorial porosity study with 3⁵ (243 runs) for laser power, scan speed, layer thickness, hatch spacing, and slicing stripe width. For each of the five studies, the relevant physics, input, and AM process qualification are presented and compared with current literature (2021–2025) on inherent-strain modelling, scan-strategy effects, melt-pool prediction, and generative design for porosity. A structured, multi-fidelity, simulation-iteration approach is provided that enables the designer to move from a generatively optimized design to a build-ready, defect-aware process plan, all without using up physical build material.
Keywords: Additive Manufacturing; ANSYS Additive; Distortion Prediction; Assumed/Inherent Strain; Thermal Strain; Scan Pattern; Melt-Pool Simulation; Porosity Prediction; AlSi10Mg; Generative Design; Design for Additive Manufacturing (DFAM)
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