Optimization of Machining Parameters in Turning Operation by Evolutionary Technique

Abstract

This paper presents the procedure to obtain the machining conditions for turning operation. Considering unit cost ofproduction as an objective function. The optimality conditions for single point cutting operation are determined based on objective function. Machining conditions are obtained for minimum cost incorporating various important cost related machining criteria such as machining cost, tool life, tool changing time etc. An example illustrates the optimization by population based Genetic Algorithm.

Introduction

In modern CNC machining, selecting optimal machining parameters—cutting speed, feed rate, and depth of cut—is critical for reducing costs, improving product quality, and enhancing efficiency. Due to the high cost of CNC operations, there's a strong economic incentive to operate under optimal conditions.

Literature Review:

Numerous researchers have explored various optimization techniques:

• Dynamic programming (Shin et al.) and quality cost models (White et al.) were used for parameter selection and cost analysis.

• Simulated annealing, genetic algorithms (GA), and particle swarm optimization (PSO) were applied for multi-pass turning (Chen et al., Reddy et al., Kennedy et al.).

• Other methods include ant colony algorithms (Vijayakumar) and hybrid GA-SA models (Saravanan).

Proposed Methodology:

This work adopts a Genetic Algorithm (GA), a bio-inspired optimization technique that simulates natural selection:

• Initial population is randomly generated.

• Selection, crossover, and mutation operators are used to evolve better solutions over generations.

• Parameters used:

  • Population size: 100

  • Crossover probability: 0.80

  • Mutation probability: 0.05

  • Iterations: 54

Objective:

To minimize the unit production cost (UC) in single-pass turning by optimizing cutting parameters. UC is composed of:

• Machining cost

• Machine idle cost

• Tool replacement cost

• Tool cost

Mathematical Model:

The total unit cost is calculated using various sub-functions:

• Machining time, loading/unloading time, and tool replacement time.

• Tool life is determined using the Taylor’s equation:
  T=CVαfβdγT = \frac{C}{V^\alpha f^\beta d^\gamma}T=VαfβdγC?

• Cost model includes:

  UC=K0(TM+TL+TR)+(KtT)TMUC = K_0(T_M + T_L + T_R) + \left(\frac{K_t}{T}\right) T_MUC=K0?(TM?+TL?+TR?)+(TKt??)TM?

Where TMT_MTM?, TLT_LTL?, and TRT_RTR? are times for machining, loading, and tool replacement respectively.

Constraints:

To ensure realistic solutions, the model includes:

• Cutting speed constraint: vmin≤v≤vmaxv_{\text{min}} \leq v \leq v_{\text{max}}vmin?≤v≤vmax?

• Feed constraint: fmin≤f≤fmaxf_{\text{min}} \leq f \leq f_{\text{max}}fmin?≤f≤fmax?

• Depth of cut constraint: dmin≤d≤dmaxd_{\text{min}} \leq d \leq d_{\text{max}}dmin?≤d≤dmax?

Results:

Using MATLAB’s GA toolbox, the optimized parameters (cutting speed, feed, depth of cut) were identified, leading to minimum cost of fabrication (COF). The GA effectively navigated the solution space while satisfying physical and parameter constraints.

Conclusion

In this work, the mathematical models of turningmachining operation is considered for optimization. The objective function is to minimize the unit production cost except material cost in turning and the machining parameters are cutting speed, feed, and depth of cut. The non-traditional optimization techniques such as genetic algorithm optimization is used to optimize machining parameters with the application of MATLAB Software. The software is completely generalized and problem independent, so that it can be easily modified to optimize any machining operation under various economic criteria and numerous practical constraints. Moreover, all the non-traditional techniques can be easily used to implement for other engineering applications.

References

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Copyright

Copyright © 2025 Smt. Manjulata Soni, Mahesh Soni. 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.