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Description
In the course of a milling process, heat is generated because of work done during the interaction(s) between the endmill (cutting tool) and the workpiece. This includes work done in: inducing plastic deformation within the workpiece, initiating fracture for chip production, and work due to friction between the cutting edges of the endmill and workpiece. The heat leads to a temperature distribution in the tool. Very high cutting temperatures are detrimental to both the tool and the workpiece. Excessive temperatures increase tool wear and lead to a diminished quality of the finishing on the workpiece surface as a result of the development of built-up edges on the endmill’s cutting surfacs. Studies have shown that cutting temperature is affected by endmill shape parameters like helix angle, rake angle, relief angle, and clearance angle. Thus, it is a research interest to carry out an investigation of an optimization process that reduces the maximum milling process cutting temperature by using these endmill shape parameters as optimization variables. This work presents a novel approach for automated endmill shape design and optimisation that is completely based on Matlab coding. Through the use of smooth cubic splines and the initial values for the endmill geometric parameters, a shape profile for an endmill’s tooth is defined and copied over up to the number of cutting edges required by the initial endmill design. The profile is meshed and extruded through the helix angle using the gibbonCode, a robust meshing Matlab toolbox. An Abaqus input file for a milling process simulation is scripted in Matlab using the nodes and elements from the designed solid endmill. Abaqus finite element software is then called to run the simulation and the required results for the endmill’s nodal cutting temperatures are extracted and processed. Particle Swarm Optimisation algorithm is coupled to the automatic endmill shape generation and milling process simulaition design. It uses the maximum nodal cutting temperature as optimization criterion value to carry out an optimizaiton process. The optimisation objective is to find the best combination of the endmill’s geometric parameters that would bring about the lowest maximum cutting temperature. The results from this study are compared to the results of a previous study where, instead of maximum cutting temperature, the maximum cutting force was minimized. It is expected that the automated design and optimisation process presented here will bring about design improvements that significantly delay tool wear by reducing cutting temperature.
