Optimization of material removal rate in milling of thin-walled structures using penalty cost function

Ringgaard, Kasper; Mohammadi, Yaser; Merrild, Christian Haastrup; Balling, Ole; Ahmadi, Keivan

doi:10.1016/j.ijmachtools.2019.103430

Cited by 39 publications

(7 citation statements)

References 22 publications

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“…The objective functions were determined using regression analysis, and optimal machining parameters to enhance the quality and productivity were determined using NSGA-II. Ringgaard et al [21] made an attempt to optimize the thin-wall machining process by maximizing the material removal rate. The researchers considered chatter stability and forced vibration as constraints and used the penalty cost function approach to optimize the process.…”

Section: Introductionmentioning

confidence: 99%

Sustainable thin-wall machining: holistic analysis considering the energy efficiency, productivity, and product quality

Bolar

Joshi

Das

2022

Int J Interact Des Manuf

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Enhanced energy efficiency, product quality, and productivity have become crucial requirements in thin-wall machining. Therefore, the work examined the impact of axial depth of cut, radial depth of cut, feed per tooth, and tool diameter on three performance measures. Full factorial was used to design experiments, and Analysis of Variance (ANOVA), a statistical method, was employed to analyze and interpret the influence of process variables on the machining performance. Additionally, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was adopted to arrive at the Pareto-optimal solutions to evaluate the trade-off between the three performance measures. The optimized process parameters for roughing operation helped maximize the process productivity at the expense of product quality. In contrast, the Pareto solutions for finishing operation effectively improved energy efficiency and produced quality open straight and curved thin-wall parts. Improved surface finish with minimal deflection can be achieved by milling with a cutter of diameter 8 mm and maintaining the feed, axial, and radial depth at 0.02 mm/z, 8 mm, and 0.3125 mm, respectively. The proposed findings can provide effective solutions for machining open straight and curved thin-wall parts with improved productivity, product quality, and energy efficiency.

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Section: Introductionmentioning

confidence: 99%

Sustainable thin-wall machining: holistic analysis considering the energy efficiency, productivity, and product quality

Bolar

Joshi

Das

2022

Int J Interact Des Manuf

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“…Where a r s, k, q (r = 1, 2, 3) is the random coefficient, which is taken the number of interval [21,1].…”

Section: F T Optimization Based On the Golden Section Methodsmentioning

confidence: 99%

“…Germashev et al 20 analyzed the vibration response of the flexible thin-walled parts during the milling process and found that the dynamic action between the force and workpiece would be different at various spindle speeds, and a better spindle speed was selected by considering the vibration amplitude of workpiece. Recently, Ringgaard et al 21 presented an optimization method to maximize the MRR without violating forced vibration and chatter stability constraints. The cost function was formulated using penalty terms which penalized the function as chatter and forced vibration constraints.…”

Section: Introductionmentioning

confidence: 99%

Research on machining parameter optimization in finishing milling with multiple constraints

Xing

Zhao

Guo

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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High efficiency and high precision milling, as the eternal goal of CNC machining, needs to balance many constraints for selecting the most reasonable processing parameters. This paper presents an efficient machining parameter optimization method for finishing milling operation with multiple constraints. Firstly, under the multiple constraints of parameter feasible region, milling force, milling stability, roughness, and machining contour accuracy, a multi-variable parameter optimization model with machining efficiency as the objective is established. A four level cycle optimization strategy has been detailly described for solving the optimization problem, in which the feed per tooth is optimized by using the golden section method, and with the aid of the random vector search method, the spindle speed, radial, and axial depth cuts are both numerically iterated. The optimal machining parameter combination of the tooth number, feed per tooth, spindle speed, radial, and axial depth of cuts are achieved at last. Finally, the experimental verification results show that the proposed method can greatly improve the machining efficiency under chatter free condition and achieve an efficient finishing milling with consideration of the multiple constraints.

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“…In contrast, the tool with a high helix angle of 55°provided a superior surface finish [20]. A penalty cost function approach was used to optimize the MRR while machining thin-wall structures [21]. Recently, Cheng et al [22] utilized Artificial Bee Colony (ABC) algorithm to minimize the surface roughness and wall deformation and determine the optimum process variables.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation and optimization of wall deflection and material removal rate in milling thin-wall parts

Bolar

Joshi

2021

Manufacturing Rev.

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The selection of optimal process parameters is essential while machining thin-wall parts since it influences the quality of the product and affects productivity. Dimensional accuracy affects the product quality, whereas the material removal rate alters the process productivity. Therefore, the study investigated the effect of tool diameter, feed per tooth, axial and radial depth of cut on wall deflection, and material removal rate. The selected process parameters were found to significantly influence the in-process deflection and thickness deviation due to the generation of unfavorable cutting forces. Further, an increase in the material removal rate resulted in chatter, thus adversely affecting the surface quality during the final stages of machining. Considering the conflicting nature of the two performance measures, Non-dominated Sorting Genetic Algorithm-II was adopted to solve the multi-objective optimization problem. The developed model could predict the optimal combination of process variables needed to lower the in-process wall deflection and maintain a superior surface finish while maintaining a steady material removal rate.

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Optimization of material removal rate in milling of thin-walled structures using penalty cost function

Cited by 39 publications

References 22 publications

Sustainable thin-wall machining: holistic analysis considering the energy efficiency, productivity, and product quality

Sustainable thin-wall machining: holistic analysis considering the energy efficiency, productivity, and product quality

Research on machining parameter optimization in finishing milling with multiple constraints

Experimental investigation and optimization of wall deflection and material removal rate in milling thin-wall parts

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