Ai-/Ml-Driven Product Design Optimisation: Intelligent Approaches for Next-Generation Manufacturing and Innovation

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into product design has redefined the traditional design and development process, introducing intelligent, data-driven methodologies that enable enhanced decision-making, faster prototyping, and superior optimization of performance, cost, and quality. This paper explores the key principles, frameworks, and challenges of AI-/ML-driven product design optimization, focusing on its applications in product lifecycle management, predictive modeling, generative design, and multi-objective optimization. The study also emphasizes how intelligent algorithms, such as neural networks, reinforcement learning, and evolutionary optimization, transform the conventional design process into an adaptive, autonomous, and customer-centric ecosystem. Furthermore, it highlights the research challenges, future scope, and emerging directions of this field, particularly in sustainable and quality-focused design systems.

KEYWORDS: Artificial Intelligence, Machine Learning, Product Design Optimization, Generative Design, Predictive Modeling, Smart Manufacturing, Quality Engineering, Digital Twin, Design Automation.