Development of a Knowledge-Based Python AddIn/Scripting for Automated CNC Tool Path Generation in Stone Fabrication Using Autodesk Fusion 360 CAM

Abstract

Computer Numerical Control (CNC) machining has become an essential manufacturing process in the stone fabrication industry for producing countertops, vanity slabs, sink cutouts, edge profiles, and architectural stone components with high dimensional accuracy. However, conventional CAM programming for stone machining remains highly manual, requiring repetitive selection of machining tools, feed rates, spindle speeds, and depth parameters for every individual operation. This not only increases programming time but also introduces dependency on operator experience and inconsistency in machining standards. To overcome these limitations, the present research proposes a Knowledge-Based Engineering (KBE) framework for automation of CNC tool path generation in Autodesk Fusion 360 using Python-based scripting. The developed system captures machining expertise in the form of rule-based knowledge including material-specific machining parameters, slab thickness rules, edge profile tool mapping, and operation-wise tool assignment logic. A custom Python AddIn/Scripting was developed using Fusion 360 CAM API that automatically reads CAM operations, identifies machining intent, selects the appropriate tool from a JSON-based digital tool library, applies predefined spindle speed, feed rate, and step-down values, renames operations, and regenerates CNC tool paths automatically. The proposed system significantly reduces repetitive CAM programming effort while ensuring standardization of machining parameters across stone fabrication jobs. Experimental implementation on multiple machining scenarios such as edge profiling, sink cutout machining, and drilling operations demonstrates that the proposed KBE system offers a practical and scalable solution for intelligent CAM automation in the stone manufacturing sector. The research establishes an effective bridge between domain knowledge capture and industrial CNC programming automation.

Introduction

This study presents a Knowledge-Based Engineering (KBE) automation system for CNC stone fabrication using a Python-based Add-In integrated with Autodesk Fusion 360 CAM. The stone industry relies heavily on manual CAM programming for operations such as sink cutouts, drilling, edge profiling, and contour machining, making the process time-consuming, repetitive, and dependent on operator expertise. This often leads to inconsistencies in tool selection and machining parameters.

To address these challenges, the proposed system captures expert machining knowledge and converts it into rule-based automation. Users provide inputs such as stone material type, slab thickness, and edge profile, and the system automatically selects appropriate tools, assigns optimized spindle speeds, feed rates, and step-down values, renames operations, and regenerates CNC toolpaths.

The framework consists of four layers: User Input Layer, Knowledge Base Layer, Inference Engine Layer, and CAM Execution Layer. A JSON-based digital tool database stores machining tools, while the inference engine applies predefined IF–THEN rules to identify machining operations and assign suitable parameters based on material properties and machining requirements.

Implemented through the Fusion 360 API and Python scripting, the Add-In automates tool assignment, parameter modification, and toolpath generation, significantly reducing manual intervention. Experimental validation on various stone machining operations demonstrated improved programming efficiency, standardized machining decisions, reduced dependence on operator experience, and enhanced consistency in CNC toolpath preparation. The study establishes a scalable and intelligent automation framework specifically designed for the stone fabrication industry.

Conclusion

The present research successfully developed and implemented a Knowledge-Based Automation framework for CNC tool path generation in stone fabrication using Python-based scripting in Autodesk Fusion 360. The developed system captures expert machining knowledge related to stone material, slab thickness, edge profile selection, and operation-wise tooling decisions, and translates this knowledge into a rule-based computational inference engine. A custom Fusion 360 AddIn/Scripting was created to automatically scan machining operations, load tools from a digital JSON library, assign corresponding tools, apply machining parameters, rename operations, and regenerate tool paths without repetitive manual intervention. Experimental validation confirmed that the proposed automation significantly improves CAM programming consistency, reduces operator dependency, and simplifies repetitive machining preparation tasks. Thus, the research demonstrates that Knowledge-Based Engineering can be effectively integrated with modern commercial CAM environments to achieve practical intelligent manufacturing automation for stone fabrication industries.

References

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Copyright

Copyright © 2026 Prashant P. Atram , Prof. Mithlesh Pande, Prof. Swapnil Choudhary , Dr. Dilip Gangwani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.