Affiliation:
1. university of pembangunan nasional veteran jawa timur
2. Universitas Islam 45
3. National Research and Innovation Agency (BRIN), KST Samaun Samadikun BRIN
4. Universitas Serang Raya
Abstract
Abstract
This study aims to investigate the optimal milling process parameters in minimizing the machining time of ankle foot as a component of the transtibial prostheses. The experimental design was carried out using the Boc Behnken technique with four factors and three levels for each factor and machining time as a response. The machining parameters evaluated in this study are spindle speed, feed rate, step over, and toolpath strategy. The physical experiments were conducted on three axes CNC milling machine using a flat endmill cutting tool. The experiment results were evaluated by variance analysis, graphical and numerical methods. Mathematical model of the optimal cutting combination parameters on machining time is determined by response surface method. Based on the results, it was found that the optimal cutting parameters in the ankle foot prosthesis manufacturing process on a CNC milling machine were spindle speed of 6500 rpm, feed rate of 800 m/min, step over of 0.2 mm, and toolpath strategy of the flowline. The optimal conditions were determined to obtain the machining time value of the ankle-foot prosthesis. The results of this study demonstrated that CNC milling provided the shortest time for machining of transtibial prosthesis components, which can guide the development of machining strategies for ankle-foot prostheses made from Al 6061 material.
Publisher
Research Square Platform LLC
Reference22 articles.
1. “Transtibial prosthetics;DeWees T;Orthot. Prosthetics Rehabil.,2019
2. A comparison of cooling methods in the pocket milling of AA5083-H36 alloy via Taguchi method;Pinar AM;Int. J. Adv. Manuf. Technol.,2016
3. Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods;Asiltürk I;Meas. J. Int. Meas. Confed.,2016
4. I. Maher, M. E. H. Eltaib, A. A. D. Sarhan, and R. M. El-Zahry, “Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining,” Int. J. Adv. Manuf. Technol., vol. 76, no. 5–8, pp. 1459–1467, Feb. 2015, doi: 10.1007/s00170-014-6379-1.
5. Machine learning-based instantaneous cutting force model for end milling operation;Vaishnav S;J. Intell. Manuf.,2020