Abstract: Efficient material utilization is a critical challenge in sheet metal manufacturing industries, particularly in CNC-based cutting environments. Traditional manual nesting approaches often result in suboptimal placement of irregular parts, leading to excessive material wastage, higher production costs, and increased operational time. This project presents an intelligent web-based sheet metal optimization system that automates geometric extraction and implements AI-driven heuristic nesting algorithms to improve sheet utilization efficiency. The proposed system accepts DXF (Drawing Exchange Format) files as input, extracts geometric entities, computes bounding boxes, and performs collision-free placement using optimized space partitioning logic. The AI-based nesting engine arranges irregular shapes within predefined sheet dimensions while dynamically allocating additional sheets when necessary. The system provides real-time visualization of part placement, material efficiency computation, and detailed wastage reports. Experimental validation demonstrates significant improvement in sheet utilization and reduction in material waste compared to conventional manual nesting practices. The framework is particularly beneficial for small and medium scale industries.

Keywords: Web-Based Manufacturing Optimisation, Sheet Metal Nesting, DXF Geometry Extraction, Heuristic Optimisation, Irregular Shape Packing, Material Utilisation, and Collision Detection.

Downloads: | DOI: 10.17148/IARJSET.2026.133106