An Improved Radar-Graph Method for Comprehensive Evaluation of Material Machinability
International Journal of Mechanical Engineering and Applications
Volume 6, Issue 2, April 2018, Pages: 23-28
Received: Mar. 25, 2018;
Accepted: Apr. 18, 2018;
Published: May 11, 2018
Views 477 Downloads 34
Tao Sun, State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China; Sichuan Province Engineering Laboratory for Superalloy Cutting Technology, Sichuan Engineering Technical College, Deyang, China
Jin Liang, State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
Dengwan Li, Sichuan Province Engineering Laboratory for Superalloy Cutting Technology, Sichuan Engineering Technical College, Deyang, China
Huizhong Wang, Sichuan Province Engineering Laboratory for Superalloy Cutting Technology, Sichuan Engineering Technical College, Deyang, China
Xinxing Li, Sichuan Province Engineering Laboratory for Superalloy Cutting Technology, Sichuan Engineering Technical College, Deyang, China
Material machinability evaluation is the basis of a reasonable manufacturing process. Material machinability can be evaluated qualitatively and quantitatively using the radar-graph method. However, two key questions remain unresolved, and these are indicator weight confirmation and effective evaluation. A comprehensive evaluation method is proposed to address the first question. A statistical method is used to compute the indicator weight, which is determined by a subjective or objective weighting method. An optimization model is established based on minimizing the total deviation between the original evaluation weight and the combination weight. As to the second question, a comprehensive evaluation index K, including the area vector and perimeter vector of a radar-graph, is defined to quantitatively evaluate material machinability. Machinability examples of Ti6Al4V titanium alloy, AISI316L stainless steel, P20 mold steel, 20 steel, and normalized 45 steel are provided. The results show that the method is feasible, reliable, and effective.
An Improved Radar-Graph Method for Comprehensive Evaluation of Material Machinability, International Journal of Mechanical Engineering and Applications.
Vol. 6, No. 2,
2018, pp. 23-28.
Wu, Y. D. and Y. P. Zhang. Metal Cutting and Tool, 1st Ed. Beijing, China: China Machine Press, 2016, 66-69.
Grzesik, W. Advanced Machining Processes of Metallic Materials, 2nd Ed. ELSEVIER, 2017, 241-264.
Segreto, T., A. Caggiano and R. Teti (2015). Neuro-fuzzy System Implementation in Multiple Sensor Monitoring for Ni-Ti Alloy Machinability Evaluation. Procedia CIRP 37, 193-198.
Henzinger, M., S. Krinninger and D. Nanongkai (2016). Improved Algorithms for Decremental Single-Source Reachability on Directed Graphs. Lecture Notes in Computer Science 9134(7), 725-736.
Rao, R. V. and D. Singh (2016). An Improved Grey Relational Analysis as a Decision-Making Method for Manufacturing Situations. International Journal of Decision Sciences Risk & Management, 2(1/2), 1-23.
Tavana, M., M. A. Kaviani, D. D. Caprio and B. Rahpeyma (2016). A Two-Stage Data Envelopment Analysis Model for Measuring Performance in Three-Level Supply Chains. Measurement, 78, 322-333.
Dey, S. and S. Chakraborty (2016). A Study on The Machinability of Some Metal Alloys Using Grey Topsis Method. Decision Science Letters 5(1), 31-44.
Deli, I. and Y. Şubaş (2017). A Ranking Method of Single Valued Neutrosophic Numbers and Its Applications to Multi-Attribute Decision Making Problems. International Journal of Machine Learning & Cybernetics, 8(4), 1309-1322.
Wan, Y., K. Cheng, Z. Q. Liu and H. T. Ye (2013). An Investigation on Machinability Assessment of Difficult-to-cut Materials based on Radar-Graphs. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture 227(12), 1916-1920.
Fan, C. Q., X. T. Tian and S. N. Liu (2011). Evaluation of Material Machinability based Extension Set. Modern Manufacturing Engineering 1, 67-69.
Golwala, H. and R. Chudamani (2016). New Three-Dimensional Space Vector-Based Switching Signal Generation Technique Without Null Vectors and With Reduced Switching Losses for a Grid-Connected Four-Leg Inverter. IEEE Transactions on Power Electronics, 31(2), 1026-1035.
J. E. Ståhl and M. Andersson, “Polar machinability diagrams- a model to predict the machinability of a work material,” Swedish Production Symposium, Göteborg, Sweden, 2007.
Du, J. and Z. Q. Liu (2010). Evaluation of Material Machinability based on Radar-Graph Method. Tool Engineering 44(7), 3-6.
Xu, L. H., Z. F. Jiang and J. E. Ståhl (2010). Machinability Prediction of Workpiece Material with a Diagraph Method. Advanced Materials Research 97-101, 2072-2075.
Xu, L. H., F. Schultheiss, M. Andersson and J. E. Ståhl (2013). General Conception of Polar Diagrams for the Evaluation of the Potential Machinability of Workpiece Materials. International Journal of Machining & Machinability of Materials 1, 24-44.
Ayatollahi, M. R. and M. Moazzami (2017). Digital Image Correlation Method for Calculating Coefficients of Williams Expansion in Compact Tension Specimen. Optics & Lasers in Engineering, 90, 26-33.
Kang, C., Y. H. Park, J. T. V. Lew, A. Ying, M. Abdou and S. Cho (2017). Transient Hot-Wire Experimental System for Measuring the Effective Thermal Conductivity of a Ceramic Breeder Pebble Bed. Fusion Science & Technology, 72(1), 1-8.
F. Zhang, D. Li, B. Geng and Z. Liu, “Study on comprehensive weighting method based on subjective and objective weights,” International Conference on Logistics Engineering, Management and Computer Science, pp. 737-741, 2014.
Zhou, D. C. (2014). Material Similarity Algorithm for Process Cases Retrieval based on Granular Computing. Journal of Mechanical Engineering 50(13), 170-177.
W. H. Zheng, Processing Technology of Difficult-to-cut Materials. Beijing, China: National Defense Industry Press, 2008, 18-19.