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In the field of CNC machining, process parameters play a crucial role in ensuring precision and cost-effectiveness. If these parameters are not chosen properly, it can lead to reduced accuracy, increased costs, or even machine downtime due to excessive cutting forces. Therefore, conducting multi-objective optimization of CNC cutting process parameters is essential for improving machining efficiency, reducing costs, and achieving high-quality outputs. This paper explores the optimization of key parameters such as spindle speed, feed rate, depth of cut, and milling width for both turning and milling operations. A multi-objective mathematical model is established, and an effective optimization algorithm is applied to achieve optimal results.
The optimization variables for CNC turning include spindle speed (n), feed speed (vf), and depth of cut (ap), represented as X = (n, vf, ap)T. For CNC milling, additional variables like milling width (ae) are included, resulting in X = (n, vf, ap, ae)T. The objective functions focus on maximizing productivity and minimizing machining time. The total machining time is calculated using the formula t = lw(1 + tct) + t0 / vf * T, where lw is the cutting stroke, tct is the tool change time, and T represents tool life. Tool life equations differ for turning and milling, incorporating factors like cutting speed, tool geometry, and material properties.
In addition to time, cost is another critical factor. The total cost includes production time, tool costs, and auxiliary times. By substituting the tool life equations into the cost formula, we derive the cost function for both turning and milling. Quality objectives, such as dimensional accuracy and surface roughness, are also considered. Dimensional accuracy is influenced by tool deformation under cutting forces, while surface quality is measured by Ra values.
Constraints such as cutting force, power, torque, and tool strength must be satisfied during optimization. Multi-objective optimization models are developed using methods like the main goal approach and linear weighted summation. In the main goal method, one objective is prioritized while others are treated as constraints. The linear weighted summation method combines all objectives with weights based on their importance.
Optimization algorithms, such as grid-directed search, are used to solve the non-linear mathematical models. An example of CNC turning on a CK7815 lathe demonstrates how different optimization goals—such as minimizing time, cost, or balancing both—lead to varying parameter settings. Similarly, a case study on CNC milling highlights the trade-offs between time, cost, and quality.
In conclusion, this research establishes a comprehensive multi-objective optimization model for CNC machining, considering time, cost, and quality. It provides a theoretical foundation for optimizing CNC processes and achieving better economic outcomes. Through real-world examples and detailed analysis, the study showcases the effectiveness of multi-objective optimization in modern manufacturing.