Energy Consumption Modeling and Statistical Analysis of CNC Milling Using CAM-Based Toolpath Optimization

Abstract

Energy efficiency has become a goal in the contemporary manufacturing process as a result of rising energy prices and the rising demands of sustainable industrialist process. The CNC machining processes are very demanding in terms of electrical energy they use in spindle rotation, feed movement, and during the operation of auxiliary system. Unplanned toolpaths and redundant machine travel may add to machining time, and result in increased total energy use. Hence, better process planning with optimal CAM can be significant in the minimization of energy requirement in the machining processes. The paper examines how CAM-based toolpath strategies can affect energy consumption in CNC milling processes. Milling machine was a three-axis CNC machine on which experimental machining was carried out on aluminium alloy as the workpiece material. Two machining strategies were compared, namely the use of conventional toolpaths, which are contour-based, and optimization toolpaths, which are aimed at reducing machine motion which is not productive. The measurement of electrical energy consumption was performed with the help of a digital power monitoring system during the experiments, in which the machining parameters, including spindle speed and feed rate, were changed. The experimental findings reveal that optimized toolpaths are found to be very effective in enhancing the efficiency of machining since they minimize unnecessary air-cutting movements besides wasting machine time. Due to this, overall electrical energy demanded by the machining strategy was less than that of the traditional tool paths. The effect of machining parameters on energy consumption was statistically analysed and a regressive-based predictive model was constructed to estimate the energy demand at various machining conditions. The results indicate that smart CAM-based planning of processes can greatly enhance the energy efficiency of CNC milling processes. The suggested strategy offers a feasible model of considering energy-consciousness strategies in the planning of machining processes as a way of ensuring more efficient and sustainable manufacturing processes.

Introduction

The text discusses how CNC milling, despite being highly accurate and widely used in modern manufacturing industries, consumes significant electrical energy due to both cutting and non-cutting operations. A major contributor to inefficiency is the toolpath strategy generated by CAM systems, where traditional paths often include unnecessary movements, idle time, and air-cutting, all of which increase machining time and energy consumption. To address this, modern CAM techniques such as adaptive clearing and optimized toolpaths are proposed to improve efficiency, reduce cycle time, and lower environmental impact.

The study focuses on experimentally evaluating how CAM-based toolpath optimization affects energy consumption during CNC milling. It compares conventional contour-based toolpaths with optimized toolpaths under controlled machining conditions using a 3-axis CNC machine and aluminum alloy workpieces. Key variables include spindle speed, feed rate, and toolpath strategy, while energy consumption is measured using a power monitoring device.

Experimental results from multiple machining trials show that optimized toolpaths consistently reduce machining time and energy usage compared to conventional methods. Statistical analysis methods such as ANOVA and regression are used to evaluate the significance of machining parameters and to potentially develop predictive models for energy consumption.

Conclusion

This paper has examined how CAM-based toolpath planning and machining parameters can affect energy usage in CNC milling process in terms of experimental research and statistical modeling. Experimental machining was carried out in a three axis CNC milling machine under varying conditions of machining to determine the interrelationship between the process planning and the energy requirement. The results of the experimental activity revealed that the toolpath strategies were optimized; they were much less time consuming than the machining time using traditional toolpath-based contours. The optimized toolpaths minimized movements of unnecessary tools, cut-offs of air, and reduced the machining efficiency and the machining time. This caused a reduction in the overall electrical power of the CNC milling machine. It was also revealed during the analysis that machining parameters like spindle speed and feed rate determine the use of energy depending on the impact that they bring about on material removal efficiency and machining time. It is thus possible to achieve better productivity through proper selection of machining parameters and at the same time minimize energy requirement. The statistical analysis was to validate that toolpath strategy and machining parameters play a larger role in energy consumption. Moreover, a predictive model was obtained using regression to determine the usage of energy in various machining conditions. The model can aid planners of the process to consider the energy efficiency of the machining processes prior to real production. Moreover, the findings of this paper demonstrate that optimization of toolpaths that are based on CAM is an efficient and feasible strategy that can be used to enhance the energy efficiency of CNC milling activities. Manufacturers can achieve this by integrating energy awareness strategies into machining process planning to save electrical energy use, enhance operational efficiency and being able to develop more sustainable manufacturing systems.

References

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Copyright

Copyright © 2026 Abhishek Kushwaha, Mr. Rajneesh Kumar, Dr. Narendra Kumar. 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.