Cost and Environmental Assessment of Tool Replacement Strategies Under Imperfect Wear Monitoring in Ti6Al4V Milling
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Published
Nov 11, 2025
Lorenzo Colantonio
Gérôme Moroncini
Olivier Sénéchal
Pierre Dehombreux
François Ducobu
Lucas Equeter
Abstract
The policy for the replacement of cutting tools has a direct influence on the risk of producing out-of-tolerances workpieces, but can also avoid excessive consumption of new tools and production interruption. Current industrial practice tries to achieve an economical balance that does not directly incorporate the environmental impact of the policy. This often results in systematic preventive replacement of tools after a safely fixed number of produced workpieces, wasting a portion of the tool’s useful life (at an environmental cost) and increasing the machine’s downtime. In this study, we simulate the production of Ti6Al4V parts under three different tool replacement scenarios: (1) at fixed intervals, (2) using an imperfect cutting tool monitoring system where the tool can only be replaced between the production of two workpieces, and (3) with a cutting tool monitoring system that allows tool replacement every minute during machining. The simulation demonstrates that even with an imperfect monitoring system, condition-based replacement leads to improved economic performance (expressed in EUR) and environmental performance (expressed in kg CO2-eq). In comparison to systematic replacement, the condition monitoring allows reducing the environmental impact up to 8.7% and the cost up to 8.1%.
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Keywords
Sustainable Maintenance, Modeling, Machining
References
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Siddhpura, A., & Paurobally, R. (2013). A review of flank wear prediction methods for tool condition monitoring in a turning process. The International Journal of Advanced Manufacturing Technology, 65(1-4), 371–393. doi: 10.1007/s00170-012-4177-1
Sun, H., Liu, Y., Pan, J., Zhang, J., & Ji, W. (2020, January). Enhancing cutting tool sustainability based on remaining useful life prediction. Journal of Cleaner Production, 244, 118794. doi: 10.1016/j.jclepro.2019.118794
Tian, C., Zhou, G., Lu, Q., Zhang, J., Xiao, Z., & Wang, R. (2019, July). An integrated decision-making approach on cutting tools and cutting parameters for machining features considering carbon emissions. International Journal of Computer Integrated Manufacturing, 32(7), 629–641. doi: 10.1080/0951192X.2019.1610575
Triebe, M., Mendis, G., Zhao, F., & Sutherland, J. (2018). Understanding Energy Consumption in a Machine Tool through Energy Mapping. Procedia CIRP, 69, 259–264. doi: 10.1016/j.procir.2017.11.041
Zhang, L., Zhang, B., Bao, H., & Huang, H. (2018, April). Optimization of Cutting Parameters for Minimizing Environmental Impact: Considering Energy Efficiency, Noise Emission and Economic Dimension. International Journal of Precision Engineering and Manufacturing, 19(4), 613–624. doi: 10.1007/s12541-018-0074-3
Campatelli, G., & Scippa, A. (2016). Environmental Impact Reduction for a Turning Process: Comparative Analysis of Lubrication and Cutting Inserts Substitution Strategies. Procedia CIRP, 55, 200–205. doi: 10.1016/j.procir.2016.08.020
Colantonio, L., Equeter, L., Dehombreux, P., & Ducobu, F. (2021). A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques. Machines, 9(12), 351. doi: 10.3390/machines9120351
Colantonio, L., Equeter, L., Dehombreux, P., & Ducobu, F. (2024, May). Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks. Sensors, 24(11), 3432. doi: 10.3390/s24113432
Dahmus, J. B., & Gutowski, T. G. (2004, November). An environmental analysis of machining. Proceedings of IMECE2004.
De Souza, L. G. P., Gomes, J. E. M., Arruda, E. M., Silva, G., De Paiva, A. P., & Ferreira, J. R. (2022, December). Evaluation of trade-off between cutting time and surface roughness robustness regarding tool wear in hard turning finishing. The International Journal of Advanced Manufacturing Technology, 123(9-10), 3047–3078. doi: 10.1007/s00170-022-10354-5
Ding, F., & He, Z. (2011). Cutting tool wear monitoring for reliability analysis using proportional hazards model. The International Journal of Advanced Manufacturing Technology, 57(5-8), 565–574. doi: 10.1007/s00170-011-3316-4
Electricity Maps. (2025). Electricity Maps - Belgium, January 2025. https://app.electricitymaps.com/zone/BE/12mo/monthly.
Elnourani, M., Johansen, K., & Öhrwall Rönnbäck, A. (2024, April). Enabling Factors for Circularity in the Metal Cutting Industry – With Focus on High-Value Circular Tools. In J. Andersson, S. Joshi, L. Malmsköld, & F. Hanning (Eds.), Advances in Transdisciplinary Engineering. IOS Press. doi: 10.3233/ATDE240193
Fairoz, I., & Shokrani, A. (2025). Life cycle assessment of cutting tool coatings (in press). Procedia CIRP.
Fratila, D. (2013). Sustainable manufacturing through environmentally-friendly machining. In: Davim, J. P. (ed.), Green Manufacturing Processes and Systems (pp. 1-21). Springer. doi: 10.1007/978-3-642-33792-5_1.
Furberg, A., Arvidsson, R., & Molander, S. (2019, February). Environmental life cycle assessment of cemented carbide (WC-Co) production. Journal of Cleaner Production, 209, 1126–1138. doi: 10.1016/j.jclepro.2018.10.272
Hoffmann Group. (n.d.). Hoffmann Group. https://www.hoffmann-group.com/.
International Organization for Standardization. (1989). ISO 8688-2:1989 Tool life testing in milling - End milling.
Iqbal, A., Zhao, G., Cheok, Q., He, N., & Nauman, M. M. (2022, May). Sustainable Machining: Tool Life Criterion Based on Work Surface Quality. Processes, 10(6), 1087. doi: 10.3390/pr10061087
Karandikar, J. M., Abbas, A., & Schmitz, T. L. (2020, November). Remaining useful tool life predictions in turning using Bayesian inference. International Journal of Prognostics and Health Management, 4(2). doi: 10.36001/ijphm.2013.v4i2.2122
Kshitij, G., Khanna, N., Yıldırım, C¸ . V., Daglı, S., & Sarıkaya, M. (2022, December). Resource conservation and sustainable development in the metal cutting industry within the framework of the green economy concept: An overview and case study. Sustainable Materials and Technologies, 34, e00507. doi: 10.1016/j.susmat.2022.e00507
Liu, C., Li, Y., Hua, J., Lu, N., & Mou,W. (2018, July). Realtime cutting tool state recognition approach based on machining features in NC machining process of complex structural parts. The International Journal of Advanced Manufacturing Technology, 97(1-4), 229–241. doi: 10.1007/s00170-018-1916-y
Lyons, R., Newell, A., Ghadimi, P., & Papakostas, N. (2021, January). Environmental impacts of conventional and additive manufacturing for the production of Ti-6Al-4V knee implant: A life cycle approach. The International Journal of Advanced Manufacturing Technology, 112(3-4), 787–801. doi: 10.1007/s00170-020-06367-7
Nystad, B. H., Gola, G., & Hulsund, J. E. (2012, July). Lifetime models for remaining useful life estimation with randomly distributed failure thresholds. PHM Society European Conference, 1(1). doi: 10.36001/phme.2012.v1i1.1442
Pakshal Steel. (2025). Titanium price per kg. https://www.pakshalsteel.com/titanium-metalprice.html.
Patange, A. D., & Jegadeeshwaran, R. (2021, March). Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling. International Journal of Prognostics and Health Management, 11(2). doi: 10.36001/ijphm.2020.v11i2.2929
Pimenov, D. Y., Mia, M., Gupta, M. K., Machado, Á . R., Pintaude, G., Unune, D. R., . . . Kunto˘glu, M. (2022, September). Resource saving by optimization and machining environments for sustainable manufacturing: A review and future prospects. Renewable and Sustainable Energy Reviews, 166, 112660. doi: 10.1016/j.rser.2022.112660
Seco Tools. (2025). Seco Tools cutting data calculator. https://www.secotools.com/.
Shi, J., Hu, J., Ma, M., & Wang, H. (2021, August). An environmental impact analysis method of machine-tool cutting units based on LCA. Journal of Engineering, Design and Technology, 19(5), 1192–1206. doi: 10.1108/JEDT-06-2020-0247
Shokrani, A., Arrazola, P., Biermann, D., Mativenga, P., & Jawahir, I. (2024). Sustainable machining: Recent technological advances. CIRP Annals, 73(2), 483–508. doi: 10.1016/j.cirp.2024.06.001
Siddhpura, A., & Paurobally, R. (2013). A review of flank wear prediction methods for tool condition monitoring in a turning process. The International Journal of Advanced Manufacturing Technology, 65(1-4), 371–393. doi: 10.1007/s00170-012-4177-1
Sun, H., Liu, Y., Pan, J., Zhang, J., & Ji, W. (2020, January). Enhancing cutting tool sustainability based on remaining useful life prediction. Journal of Cleaner Production, 244, 118794. doi: 10.1016/j.jclepro.2019.118794
Tian, C., Zhou, G., Lu, Q., Zhang, J., Xiao, Z., & Wang, R. (2019, July). An integrated decision-making approach on cutting tools and cutting parameters for machining features considering carbon emissions. International Journal of Computer Integrated Manufacturing, 32(7), 629–641. doi: 10.1080/0951192X.2019.1610575
Triebe, M., Mendis, G., Zhao, F., & Sutherland, J. (2018). Understanding Energy Consumption in a Machine Tool through Energy Mapping. Procedia CIRP, 69, 259–264. doi: 10.1016/j.procir.2017.11.041
Zhang, L., Zhang, B., Bao, H., & Huang, H. (2018, April). Optimization of Cutting Parameters for Minimizing Environmental Impact: Considering Energy Efficiency, Noise Emission and Economic Dimension. International Journal of Precision Engineering and Manufacturing, 19(4), 613–624. doi: 10.1007/s12541-018-0074-3
Section
Technical Papers