Catastrophic Tool Failure Detection in Aeronautical Industrial Drilling Systems Based on Spindle Power Consumption Analysis

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The aeronautical industry is at the forefront of the fourth industrial revolution, which implies an exponential deployment of monitorization, Data Analytics, and connectivity. In alignment with this new paradigm, this research work presents a Catastrophic Tool Failure (CTF) analysis based on spindle power consumption monitoring in an industrial aircraft fuselage drilling process. In the case under study, the airframe components are arranged in hybrid stacks of Carbon Fiber Reinforced Plastic (CFRP) and titanium (Ti6Al4V) during drilling, which adds to the highly variable industrial machining conditions. This inherent complexity can lead to CTF, a significant concern due to its associated cost and time, especially in automatic processes. Industrial CTF detection systems based on motor power consumption establish maximum and minimum power limits to detect tool breakage. However, these systems generate many false positives and false negatives due to process variability and unforeseen events. Therefore, an Exploratory Data Analysis (EDA) of the power spindle consumption signals and other machining-related features is proposed to gain insights into the breakage nature and develop more effective detection systems. This analysis is oriented to set the basis for real-time Catastrophic Tool Failure detection from power spindle consumption monitoring. As a result, advanced processing time-domain detection methods are proposed.

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31-40

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October 2023

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© 2023 Trans Tech Publications Ltd. All Rights Reserved

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[1] Airbus. Airbus Global Market Forecast 2021-2040. 2021.

Google Scholar

[2] Chryssolouris G, Papakostas N, Mavrikios D. A perspective on manufacturing strategy: Produce more with less. CIRP J Manuf Sci Technol 2008;1.

DOI: 10.1016/j.cirpj.2008.06.008

Google Scholar

[3] Stavropoulos P, Chantzis D, Doukas C, Papacharalampopoulos A, Chryssolouris G. Monitoring and control of manufacturing processes: A review. Procedia CIRP, vol. 8, 2013.

DOI: 10.1016/j.procir.2013.06.127

Google Scholar

[4] Nath C. Integrated tool condition monitoring systems and their applications: A comprehensive review. vol. 48, 2020.

DOI: 10.1016/j.promfg.2020.05.123

Google Scholar

[5] Li X, Liu X, Yue C, Liang SY, Wang L. Systematic review on tool breakage monitoring techniques in machining operations. Int J Mach Tools Manuf 2022;176.

DOI: 10.1016/j.ijmachtools.2022.103882

Google Scholar

[6] Serin G, Sener B, Ozbayoglu AM, Unver HO. Review of tool condition monitoring in machining and opportunities for deep learning. International Journal of Advanced Manufacturing Technology 2020;109.

DOI: 10.1007/s00170-020-05449-w

Google Scholar

[7] Teti R, Jemielniak K, O'Donnell G, Dornfeld D. Advanced monitoring of machining operations. CIRP Ann Manuf Technol 2010;59.

DOI: 10.1016/j.cirp.2010.05.010

Google Scholar

[8] Coady J, Toal D, Newe T, Dooly G. Remote acoustic analysis for tool condition monitoring. Procedia Manuf, vol. 38, 2019.

DOI: 10.1016/j.promfg.2020.01.165

Google Scholar

[9] Gomathi K, Balaji A. Tool condition monitoring of PCB milling machine based on vibration analysis. Mater Today Proc, vol. 45, 2021.

DOI: 10.1016/j.matpr.2020.12.778

Google Scholar

[10] Balsamo V, Caggiano A, Jemielniak K, Kossakowska J, Nejman M, Teti R. Multi Sensor Signal Processing for Catastrophic Tool Failure Detection in Turning. Procedia CIRP, vol. 41, 2016.

DOI: 10.1016/j.procir.2016.01.010

Google Scholar

[11] Guerra-Sancho A, Perez JF, Garrido MHM, Guisandez JLC, Alvarez JD. Countersink depth control in aeronautical structural components drilling processes by machining forces analysis. 2022 8th International Engineering, Sciences and Technology Conference (IESTEC), IEEE; 2022, p.715–22.

DOI: 10.1109/IESTEC54539.2022.00117

Google Scholar

[12] Zhou L, Li J, Li F, Meng Q, Li J, Xu X. Energy consumption model and energy efficiency of machine tools: A comprehensive literature review. J Clean Prod 2016;112.

DOI: 10.1016/j.jclepro.2015.05.093

Google Scholar

[13] Diaz N, Redelsheimer E, Dornfeld D. Energy consumption characterization and reduction strategies for milling machine tool use. Glocalized Solutions for Sustainability in Manufacturing - Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, 2011.

DOI: 10.1007/978-3-642-19692-8_46

Google Scholar

[14] Hu L, Zheng H, Shu L, Jia S, Cai W, Xu K. An investigation into the method of energy monitoring and reduction for machining systems. J Manuf Syst 2020;57.

DOI: 10.1016/j.jmsy.2020.10.012

Google Scholar

[15] Domínguez-Monferrer C, Fernández-Pérez J, de Santos R, Miguélez MH, Cantero JL. CFRP drilling process control based on spindle power consumption from real production data in the aircraft industry. Procedia CIRP 2022;107:1533–8.

DOI: 10.1016/j.procir.2022.05.187

Google Scholar

[16] Domínguez-Monferrer C, Fernández-Pérez J, de Santos R, Miguélez MH, Cantero JL. Machine learning approach in non-intrusive monitoring of tool wear evolution in massive CFRP automatic drilling processes in the aircraft industry. J Manuf Syst 2022;65:622–39.

DOI: 10.1016/j.jmsy.2022.10.018

Google Scholar

[17] Chapter 2 Metallurgical failure analysis. Practical Machinery Management for Process Plants, vol. 2, 1999

DOI: 10.1016/S1874-6942(99)80004-2

Google Scholar