OBJECTIVES

The goal of this project is to develop a next-generation CNC fixture that integrates AI-driven generative design, topology optimization, and predictive modelling to achieve exceptional performance in machining environments. The detailed objectives include:

• To create a parametric and modular fixture design capable of accommodating a wide variety of prismatic workpiece geometries with minimal operator intervention.

• To reduce fixture weight by 30–50% through topology optimization while preserving or improving stiffness and functional integrity.

• To apply generative AI and LLM-assisted design refinement to identify efficient geometric configurations aligned with stress flow and manufacturability.

• To evaluate structural behavior under realistic machining loads (1300–1500 N) using static and modal finite element analysis (FEA).

• To develop a high-accuracy ANN surrogate model for predicting deformation, stress, and stiffness instantaneously without the need for repeated simulations.

• To validate and compare ANN predictions with FEA outputs, quantifying accuracy through metrics such as R², MSE, and regression plots

• To select the best-performing design iteration based on stiffness-to-mass ratio, stress distribution, modal stability, and manufacturability considerations.

• To propose a future-ready framework supporting autonomous, AI-driven fixture design systems suitable for Industry 4.0/5.0 smart manufacturing.