Development of a machining allowance model for rectangular pocket milling operations
DOI:
https://doi.org/10.37636/recit.v9n3e498Keywords:
Machining allowance, Pocket milling, Multi-objective optimization, Energy consumption, Cutting forceAbstract
Machining allowance plays a critical role in determining productivity, energy consumption, cutting force, and dimensional accuracy in pocket milling operations, yet it is commonly selected empirically in industrial practice. This study proposes an analytical, optimization-based framework for determining an efficient machining allowance for rectangular pocket milling. Mathematical models are developed to quantify total energy consumption, machining time, and peripheral cutting force as functions of cutting parameters and allowance distribution between rough and finish milling stages. A multi-objective optimization problem is formulated and solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously minimize energy consumption, machining time, and cutting force. Experimental validation is conducted on polymer workpieces and compared with CAM-recommended parameters. The results show that the balanced Pareto-optimal solution reduces machining time from 5 min 58.52 s to 2 min 26.47 s, corresponding to an approximate 59% reduction, and decreases the peripheral cutting force from 698.18 N to 382.73 N, corresponding to an approximate 45% reduction, while achieving the target pocket depth of approximately 22 mm and eliminating the ~2 mm over-machining observed under CAM-default settings. A minimum-energy solution further reduces energy consumption from 0.44 kWh to 0.267 kWh, but at the cost of a significantly increased cutting force (1203.45 N). These results indicate that treating machining allowance as an optimization variable enables systematic trade-offs among productivity, energy consumption, cutting force, and dimensional accuracy, offering a practical alternative to conventional CAM-based parameter selection.
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Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
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Copyright (c) 2026 Tran Thanh Tung, Nguyen Thi Anh, Nguyen Xuan Quynh, Tran Vu Minh

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