Introduction

Intoduction

Fixtures play a critical role in CNC manufacturing by securely holding and accurately positioning the workpiece during machining operations. The performance of any CNC process—whether in terms of accuracy, surface finish, dimensional stability, or repeatability—depends heavily on the quality and rigidity of the fixture being used. Traditional fixture designs, however, are often bulky, material-intensive, and difficult to modify for different workpiece geometries. These limitations result in increased manufacturing costs, longer setup times, and reduced adaptability in modern high-mix, high-precision production environments.

To overcome these challenges, the manufacturing industry is shifting toward lightweight, intelligent, and performance-optimized fixtures. Computational methods such as topology optimization, generative design, finite element analysis (FEA), and modern AI-powered design systems now enable engineers to create fixture geometries that provide high stiffness while minimizing material use. At the same time, Artificial Neural Networks (ANNs) have emerged as powerful predictive tools capable of estimating deformation, stress, and stiffness without requiring time-consuming simulations.

This project integrates these advanced technologies to develop an AI-assisted generative fixture design framework for CNC machining. By combining generative design, topology optimization, FEA, and ANN-based prediction models, the project aims to create a fixture that is significantly lighter yet structurally superior to conventional designs. The optimized fixture demonstrates improved stiffness, reduced deformation under machining loads, and superior load-path efficiency—all aligned with the goals of Industry 4.0 and the emerging Industry 5.0 vision of intelligent, autonomous, and sustainable manufacturing systems.