Study on Machining Process of Multi-step Holes by Using The Standard Cutting Tool and Stepped Drill
DOI:
https://doi.org/10.37385/1fpc2e08Keywords:
Machining process, cutting tool, cutting parameter, machine tool, multi-step holesAbstract
Besides technological solutions, machining productivity is a factor that it is always considered by manufacturers. The cutting tool (CT) is one of the key points to boost the cutting efficiency and improve machining productivity of the machining process. The proper cutting tool is a good solution for saving the cutting time, enhancing the machining quality, and improving the time life of machine tools. Traditional drilling method uses a standard cutting tool (SCT) with single cutting diameter that revealed disadvantages of low cutting velocity and high cost. This paper demonstrates a method of using the combination cutting tool (CCT)that equipped more cutting diameters in the same knife body to enhance machining productivity and reduce cutting time during drilling multi-step holes. By introducing a CCT, the machining productivity increases of about 131.72% and 257.9% in comparison to the CC in the option 1 and option 2, the cutting time deceases of about 72.06% and 56.85% in comparison to the CC in the option 1 and option 2, and the technology cost decreases of about 42.86% and 63.64% in comparison to the CC in the option 1 and option 2, respectively. The results show that the combination cutting tool is a good solution to improve the machining productivity and cost effectiveness during machining multi-step holes.
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Abellán-Nebot, J. V., Vila Pastor, C., & Siller, H. R. (2024). A Review of the Factors Influencing Surface Roughness in Machining and Their Impact on Sustainability. Sustainability, 16(5), 1917. doi:10.3390/su16051917
Adin, M. Ş. (2024). Machining aerospace aluminium alloy with cryo-treated and untreated HSS cutting tools. Advances in Materials and Processing Technologies, 10(3), 2664-2689. doi: 10.1080/2374068X.2023.2273035
Ahmed Al-Dujaili, M. A. (2013). Study of the relation between types of the quality costs and its impact on productivity and costs: a verification in manufacturing industries. Total Quality Management & Business Excellence, 24(3-4), 397-419. doi: 10.1080/14783363.2012.669552
Akgün, M., & Kara, F. (2021). Analysis and Optimization of Cutting Tool Coating Effects on Surface Roughness and Cutting Forces on Turning of AA 6061 Alloy. Advances in Materials Science and Engineering, 2021(1), 6498261. doi: https://doi.org/10.1155/2021/6498261
Bobyr, M. V., Yakushev, A. S., & Dorodnykh, A. A. (2020). Fuzzy devices for cooling the cutting tool of the CNC machine implemented on FPGA. Measurement, 152, 107378. doi: https://doi.org/10.1016/j.measurement.2019.107378
Chen, M., Liu, G., & Zhang, X.-h. (2007). OPTIMIZATION STUDIES ON HOLE-MAKING TOOLS FOR HIGH-PERFORMANCE CUTTING AUSTENITIC STAINLESS STEEL. Machining Science and Technology, 11(2), 183-200. doi: 10.1080/10910340701339994
Chen, Y., Wang, J., & Chen, M. (2019). Enhancing the machining performance by cutting tool surface modifications: a focused review. Machining Science and Technology, 23(3), 477-509. doi: 10.1080/10910344.2019.1575412
Derakhshandeh, M. R., Eshraghi, M. J., & Razavi, M. (2023). Recent developments in the new generation of hard coatings applied on cemented carbide cutting tools. International Journal of Refractory Metals and Hard Materials, 111, 106077. doi: https://doi.org/10.1016/j.ijrmhm.2022.106077
Duan, Y., Hou, L., & Leng, S. (2021). A novel cutting tool selection approach based on a metal cutting process knowledge graph. The International Journal of Advanced Manufacturing Technology, 112(11), 3201-3214. doi: 10.1007/s00170-021-06606-5
Fouathiya, A., Meziani, S., Sahli, M., & Barrière, T. (2021). Experimental investigation of microtextured cutting tool performance in titanium alloy via turning. Journal of Manufacturing Processes, 69, 33-46. doi: https://doi.org/10.1016/j.jmapro.2021.07.030
Gimadeev, M. R., Stelmakov, V. A., Yakuba, D. D., & Uliskov, M. V. (2025, 2025//). Optimization of Hole Milling for Increasing Productivity in Automated Manufacturing. Paper presented at the Proceedings of the 11th International Conference on Industrial Engineering, Cham.
Grigoriev, S. N., Migranov, M. S., Melnik, Y. A., Okunkova, A. A., Fedorov, S. V., Gurin, V. D., & Volosova, M. A. (2021). Application of Adaptive Materials and Coatings to Increase Cutting Tool Performance: Efficiency in the Case of Composite Powder High Speed Steel. Coatings, 11(7), 855. doi:10.3390/coatings11070855
Hao, G., & Liu, Z. (2020). The heat partition into cutting tool at tool-chip contact interface during cutting process: a review. The International Journal of Advanced Manufacturing Technology, 108(1), 393-411. doi: 10.1007/s00170-020-05404-9
Heshmati, A. (2003). Productivity Growth, Efficiency and Outsourcing in Manufacturing and Service Industries. Journal of Economic Surveys, 17(1), 79-112. doi: https://doi.org/10.1111/1467-6419.00189
Hodgyai, N., Máté, M., Oancea, G., & Dragoi, M.-V. (2024). Gear Hobs—Cutting Tools and Manufacturing Technologies for Spur Gears: The State of the Art. Materials, 17(13), 3219. doi:10.3390/ma17133219
Kadam, G. S., & Pawade, R. S. (2024). Water vapor cutting fluid assisted productive machining of Inconel 718. Materials and Manufacturing Processes, 39(1), 98-109. doi: 10.1080/10426914.2023.2190389
Kamble, Y. G., Pantawane, P. D., Rajiv, B., & Ahuja, B. B. (2020, 2020//). Design and Development of Combination Tool for Drilling and Tapping Operation on PVC. Paper presented at the Advances in Simulation, Product Design and Development, Singapore.
Karpuschewski, B., Byrne, G., Denkena, B., Oliveira, J., & Vereschaka, A. (2021). Machining Processes. In K.-H. Grote & H. Hefazi (Eds.), Springer Handbook of Mechanical Engineering (pp. 409-460). Cham: Springer International Publishing.
Kasiviswanathan, S., Gnanasekaran, S., Thangamuthu, M., & Rakkiyannan, J. (2024). Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review. Journal of Sensor and Actuator Networks, 13(5), 53. doi:10.3390/jsan13050053
Krebs, C., Weigold, M., & Abele, E. (2025, 2025//). Design of a Combination Tool for Drilling and Deburring Cross Holes. Paper presented at the Production at the Leading Edge of Technology, Cham.
Kuntoğlu, M., Salur, E., Gupta, M. K., Sarıkaya, M., & Pimenov, D. Y. (2021). A state-of-the-art review on sensors and signal processing systems in mechanical machining processes. The International Journal of Advanced Manufacturing Technology, 116(9), 2711-2735. doi: 10.1007/s00170-021-07425-4
Lee, B. Y., Liu, H. S., & Tarng, Y. S. (1998). Modeling and optimization of drilling process. Journal of Materials Processing Technology, 74(1), 149-157. doi: https://doi.org/10.1016/S0924-0136(97)00263-X
Lee, C.-S., Lee, J.-H., Kim, D.-S., Heo, E.-Y., & Kim, D.-W. (2013). A hole-machining process planning system for marine engines. Journal of Manufacturing Systems, 32(1), 114-123. doi: https://doi.org/10.1016/j.jmsy.2012.10.005
Li, B., Huang, C., Chen, Z., Liu, H., Niu, J., Wang, J., . . . Huang, S. (2025). Change of the machined hole wall surface roughness, microstructure and microhardness of low alloy steel caused by drill-guide-pads during BTA deep hole drilling. The International Journal of Advanced Manufacturing Technology, 139(3), 1595-1605. doi: 10.1007/s00170-025-15989-8
Machado, A. R., da Silva, L. R. R., Pimenov, D. Y., de Souza, F. C. R., Kuntoğlu, M., & de Paiva, R. L. (2024). Comprehensive review of advanced methods for improving the parameters of machining steels. Journal of Manufacturing Processes, 125, 111-142. doi: https://doi.org/10.1016/j.jmapro.2024.07.044
Mikolajczyk, T., Pimenov, D. Y., Pruncu, C. I., Patra, K., Latos, H., Krolczyk, G., . . . Gupta, M. K. (2019). Obtaining Various Shapes of Machined Surface Using a Tool with a Multi-Insert Cutting Edge. Applied Sciences, 9(5), 880. doi:10.3390/app9050880
Minquiz, G. M., Borja, V., López-Parra, M., Ramírez-Reivich, A. C., Ruiz-Huerta, L., Lázaro, R. C. A., . . . Flores Méndez, J. (2020). Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations. Mathematical Problems in Engineering, 2020(1), 8718597. doi: https://doi.org/10.1155/2020/8718597
Mironova, L., & Kondratenko, L. (2019). Mathematical modeling of the processing of holes on CNC machines. Materials Today: Proceedings, 19, 2354-2357. doi: https://doi.org/10.1016/j.matpr.2019.07.691
Moghaddas, M. A., Short, M. A., Wiley, N. R., Yi, A. Y., & Graff, K. F. (2018). Improving Productivity in an Ultrasonic-Assisted Drilling Vertical Machining Center. Journal of Manufacturing Science and Engineering, 140(6). doi: 10.1115/1.4039109
Mohamed, A., Hassan, M., M’Saoubi, R., & Attia, H. (2022). Tool Condition Monitoring for High-Performance Machining Systems—A Review. Sensors, 22(6), 2206. doi:10.3390/s22062206
Navaneethan, G., Palanisamy, S., Jayaraman, P. P., Kang, Y.-B., Stephens, G., Papageorgiou, A., & Navarro, J. (2024). A review of automated cutting tool selection methods. The International Journal of Advanced Manufacturing Technology, 133(3), 1063-1082. doi: 10.1007/s00170-024-13823-1
Niu, J., Huang, C., Li, C., Zou, B., Xu, L., Wang, J., & Liu, Z. (2020). A comprehensive method for selecting cutting tool materials. The International Journal of Advanced Manufacturing Technology, 110(1), 229-240. doi: 10.1007/s00170-020-05534-0
Othmani, R., Bouzid, W., & Hbaieb, M. (2008). Machining time in rough milling. Materials Technology, 23(3), 169-173. doi: 10.1179/175355508X266953
Özbek, N. A., Özbek, O., & Kara, F. (2021). Statistical Analysis of the Effect of the Cutting Tool Coating Type on Sustainable Machining Parameters. Journal of Materials Engineering and Performance, 30(10), 7783-7795. doi: 10.1007/s11665-021-06066-8
Pattnaik, S. K., Bhoi, N. K., Padhi, S., & Sarangi, S. K. (2018). Dry machining of aluminum for proper selection of cutting tool: tool performance and tool wear. The International Journal of Advanced Manufacturing Technology, 98(1), 55-65. doi: 10.1007/s00170-017-0307-0
Pawanr, S., & Gupta, K. (2024). Dry Machining Techniques for Sustainability in Metal Cutting: A Review. Processes, 12(2), 417. doi:10.3390/pr12020417
Peng, Y., Song, Q., Wang, R., Du, Y., Li, Z., Ma, H., . . . Liu, Z. (2025). Toward high-performance machining of thin-walled parts: Fusion of parallel spatial–temporal information in high-speed milling for monitoring tool wear. Measurement, 248, 116899. doi: https://doi.org/10.1016/j.measurement.2025.116899
Pessoles, X., Landon, Y., Segonds, S., & Rubio, W. (2013). Optimisation of workpiece setup for continuous five-axis milling: application to a five-axis BC type machining centre. The International Journal of Advanced Manufacturing Technology, 65(1), 67-79. doi: 10.1007/s00170-012-4151-y
Pimenov, D. Y., Bustillo, A., Wojciechowski, S., Sharma, V. S., Gupta, M. K., & Kuntoğlu, M. (2023). Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. Journal of Intelligent Manufacturing, 34(5), 2079-2121. doi: 10.1007/s10845-022-01923-2
Qudeiri, J. E. A., Raid, A. m., Jamali, M. A., & Yamamoto, H. (2006, 25-27 Oct. 2006). Optimization Hole-Cutting Operations Sequence in CNC Machine Tools Using GA. Paper presented at the 2006 International Conference on Service Systems and Service Management.
Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778. doi: 10.1080/00207543.2021.1956675
Rawat, G. S., Gupta, A., & Juneja, C. (2018). Productivity Measurement of Manufacturing System. Materials Today: Proceedings, 5(1, Part 1), 1483-1489. doi: https://doi.org/10.1016/j.matpr.2017.11.237
Sharma, V. S., Dogra, M., & Suri, N. M. (2009). Cooling techniques for improved productivity in turning. International Journal of Machine Tools and Manufacture, 49(6), 435-453. doi: https://doi.org/10.1016/j.ijmachtools.2008.12.010
Shi, P., & Ning, F. (2025). Optimal tool combination for complex pocket cutting based on medial axis transform. The International Journal of Advanced Manufacturing Technology, 140(7), 3559-3574. doi: 10.1007/s00170-025-16470-2
Singh, J., Gill, S. S., & Mahajan, A. (2024). Experimental Investigation and Optimizing of Turning Parameters for Machining of Al7075-T6 Aerospace Alloy for Reducing the Tool Wear and Surface Roughness. Journal of Materials Engineering and Performance, 33(17), 8745-8756. doi: 10.1007/s11665-023-08584-z
Sokolova, I. D., Beckel, L. S., & Pilipushko, A. Y. (2021). Mathematical model of changing the combined cutting tool durability. IOP Conference Series: Materials Science and Engineering, 1047(1), 012013. doi: 10.1088/1757-899X/1047/1/012013
Soori, M., Arezoo, B., & Dastres, R. (2023). Machine learning and artificial intelligence in CNC machine tools, A review. Sustainable Manufacturing and Service Economics, 2, 100009. doi: https://doi.org/10.1016/j.smse.2023.100009
Sun, H., Liu, Y., Pan, J., Zhang, J., & Ji, W. (2020). Enhancing cutting tool sustainability based on remaining useful life prediction. Journal of Cleaner Production, 244, 118794. doi: https://doi.org/10.1016/j.jclepro.2019.118794
Szczotkarz, N., Mrugalski, R., Maruda, R. W., Królczyk, G. M., Legutko, S., Leksycki, K., . . . Pruncu, C. I. (2021). Cutting tool wear in turning 316L stainless steel in the conditions of minimized lubrication. Tribology International, 156, 106813. doi: https://doi.org/10.1016/j.triboint.2020.106813
Tolouei-Rad, M., & Bidhendi, I. M. (1997). On the optimization of machining parameters for milling operations. International Journal of Machine Tools and Manufacture, 37(1), 1-16. doi: https://doi.org/10.1016/S0890-6955(96)00044-2
Ullah, S., Liang, Z., Ma, Y., Du, Y., Su, Z., Yin, Z., . . . Zhou, T. (2025). State-of-the-art review on ultrasonic vibration-assisted micro-drilling. The International Journal of Advanced Manufacturing Technology, 138(11), 4935-4963. doi: 10.1007/s00170-025-15792-5
Woo, W.-S., Curtis, D., Bagni, C., Lee, C.-M., Lee, J.-H., & Kim, D.-H. (2022). Productivity Enhancement of Aircraft Turbine Disk Using a Two-Step Strategy Based on Tool-Path Planning and NC-Code Optimization. Metals, 12(4), 567. doi:10.3390/met12040567
Xiao, W., Huang, J., Wang, B., & Ji, H. (2022). A systematic review of artificial intelligence in the detection of cutting tool breakage in machining operations. Measurement, 190, 110748. doi: https://doi.org/10.1016/j.measurement.2022.110748
Xie, Y., Lian, K., Liu, Q., Zhang, C., & Liu, H. (2021). Digital twin for cutting tool: Modeling, application and service strategy. Journal of Manufacturing Systems, 58, 305-312. doi: https://doi.org/10.1016/j.jmsy.2020.08.007
Xin, T., Pei, H., & Shucai, Y. (2022). Coating and micro-texture techniques for cutting tools. Journal of Materials Science, 57(36), 17052-17104. doi: 10.1007/s10853-022-07704-9
Zaman, P. B., & Dhar, N. R. (2024). Current research trends in coolant application for machining Ti-6Al-4V alloy: a state-of-the-art review. Advances in Materials and Processing Technologies, 1-71. doi: 10.1080/2374068X.2024.2423492
Zhang, J., Wang, J., Zhang, G., Huo, Z., Huang, Z., & Wu, L. (2024). A review of diamond synthesis, modification technology, and cutting tool application in ultra-precision machining. Materials & Design, 237, 112577. doi: https://doi.org/10.1016/j.matdes.2023.112577
Zhang, N., Jiang, Z., Sun, Y., Liu, Z., Hou, J., & Wu, F. (2024). Model-data hybrid driven approach for remaining useful life prediction of cutting tool based on improved inverse Gaussian process. Journal of Manufacturing Processes, 124, 604-620. doi: https://doi.org/10.1016/j.jmapro.2024.06.027
Zhang, X., Lu, X., Li, W., & Wang, S. (2021). Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM. The International Journal of Advanced Manufacturing Technology, 112(7), 2277-2299. doi: 10.1007/s00170-020-06447-8
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