AI-aided design optimization and topology optimization for lightweight mechanical components: Trends, challenges, and future directions

Abstract

The increasing demand for energy-efficient and high-performance engineering systems has intensified the need for lightweight mechanical components with high stiffness, strength, and reliability. Topology optimization (TO) enables efficient material distribution within a prescribed design domain, but conventional methods remain limited by high computational cost, numerical sensitivity, restricted multi-physics capability, and inadequate consideration of manufacturing constraints. This review critically examines recent advances in artificial intelligence (AI) and machine learning (ML)-assisted TO for lightweight mechanical components, focusing on surrogate modeling, deep learning, reinforcement learning, physics-informed learning, manufacturability-aware design, uncertainty quantification, multi-physics and multi-scale optimization, and digital engineering. Particular emphasis is placed on manufacturing constraints, including minimum feature size, overhang, support requirements, build orientation, machining accessibility, and process-related effects. The review further distinguishes numerical feasibility, fabrication feasibility, experimental validation, and industrial deployment to assess the practical maturity of AI-assisted TO. The analysis indicates that AI can accelerate optimization and design-space exploration, but challenges remain in data dependency, generalization, physical consistency, uncertainty, and validation. Hybrid AI–physics frameworks therefore offer the most promising pathway toward reliable and manufacturable lightweight designs. Future research should prioritize standardized benchmarks, reproducible workflows, uncertainty-aware optimization, process-specific manufacturability models, and experimental validation to support the transition of AI-assisted TO from research applications to dependable industrial practice.