Smart tool-related faults monitoring system using process simulation-based machine learning algorithms

• Autorzy: Ebrahimi Araghizad Arash , Tehranizadeh Faraz , Kilic Kemal , Budak Erhan

Identyfikatory

DOI

10.36897/jme/174018

• Języki publikacji: EN

Abstrakty

EN

In this paper a novel approach for monitoring tool-related faults in milling processes by utilizing process simulation-based machine learning algorithms, specifically Random Forest algorithms, for fault detection is presented. In order to train machine learning models in tool condition monitoring, laboratory tests have traditionally been required. This method eliminates the need for costly, time-consuming laboratory tests. The training process has been simplified by utilizing analytical simulation data and provides a more cost-effective solution by leveraging analytical simulation data. Based on the results of this study, the proposed approach has been demonstrated to be 94% accurate at predicting tool-related faults, demonstrating its potential to serve as an efficient and viable alternative to conventional methods. These findings have been supported by actual measurement data, with a notable accuracy rate of 93% in the predictions. Furthermore, the results indicate that process simulation-based machine learning algorithms will have a significant impact on the tools condition monitoring and the efficiency of manufacturing processes more generally. To further enhance the capabilities of the proposed fault monitoring system, process-related and machine-related faults will be investigated in future research. Several machine learning algorithms will be explored as well as additional data sources will be integrated in order to enhance the accuracy and reliability of fault detection.

Słowa kluczowe

EN

Industry 4.0; machining; machine learning; monitoring

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2023
• Tom: Vol. 23, No. 4
• Strony: 18--32
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Ebrahimi Araghizad Arash

• Manufacturing Research Laboratory, Sabanci University, Turkey

• https://orcid.org/0000-0003-4117-1773

autor

Tehranizadeh Faraz

• Manufacturing Research Laboratory, Sabanci University, Turkey

autor

Kilic Kemal

• Faculty of Engineering and Natural Sciences, Sabanci University, Turkey

autor

Budak Erhan

• Manufacturing Research Laboratory, Sabanci University, Turkey

Bibliografia

• [1] BUDAK E., ALTINTAS Y., ARMAREGO E.J.A., 1996, Prediction of Milling Force Coefficients from Orthogonal Cutting Data, J. Manuf. Sci. Eng., 118/2, 216–224, https://doi.org/10.1115/1.2831014.
• [2] BALAZINSKI M., CZOGALA E., JEMIELNIAK K., LESKI J., 2002, Tool Condition Monitoring Using Artificial Intelligence Methods, Eng. Appl. Artif. Intell., 15, 73–80.
• [3] ELBESTAWI M.A., PAPAZAFIRIOU T.A., DU R.X., 1991, In-Process Monitoring of Tool Wear in Milling Using Cutting Force Signature, Int. J. Mach. Tools Manuf., 31, 55–73.
• [4] TANSEL I.N., ARKAN T.T., BAO W.Y., MAHENDRAKAR N., SHISLER B., SMITH D., Mc COOL M., 2000, Tool Wear Estimation in Micro-Machining, Part I: Tool Usage–Cutting Force Relationship, Int. J. Mach. Tools Manuf., 40, 599–608.
• [5] TANSEL I.N., ARKAN T.T., BAO W.Y., MAHENDRAKAR N., SHISLER B., SMITH D., McCOOL M., 2000, Tool Wear Estimation in Micro-Machining, Part II: Neural-Network-Based Periodic Inspector for Non-Metals, Int. J. Mach. Tools Manuf., 40, 609–620.
• [6] SAGLAM H., UNUVAR A., 2003, Tool Condition Monitoring in Milling Based on Cutting Forces by a Neural Network, Int. J. Prod. Res. 41, 1519–1532.
• [7] KULJANIC E., SORTINO M., 2005, Twem, a Method Based on Cutting Forces—Monitoring Tool Wear in Face Milling, Int. J. Mach. Tools Manuf., 45, 29–34.
• [8] LI H.Z., ZENG H., CHEN X.Q., 2006, An Experimental Study of Tool Wear and Cutting Force Variation in the end Milling of Inconel 718 with Coated Carbide Inserts, J. Mater. Process. Technol., 180, 296–304, https://doi.org/10.1016/j. jmatprotec.2006.07.009.
• [9] WANG M., WANG J., 2012, CHMM for Tool Condition Monitoring and Remaining Useful Life Prediction, Int. J. Adv. Manuf. Technol., 59, 463–471.
• [10] NOURI M., FUSSELL B.K., ZINITI B.L., LINDER E., 2015, Real-Time Tool Wear Monitoring in Milling Using a Cutting Condition Independent Method, Int. J. Mach. Tools Manuf., 89, 1–13.
• [11] AZMI A.I., 2015, Monitoring of Tool Wear Using Measured Machining Forces and Neuro-Fuzzy Modelling Approaches During Machining of GFRP Composites, Adv. Eng. Softw., 82, 53–64.
• [12] DA SILVA R.H.L., DA SILVA M.B., Hassui A., 2016, A Probabilistic Neural Network Applied in Monitoring Tool Wear in the end Milling Operation via Acoustic Emission and Cutting Power Signals, Mach. Sci. Technol., 20, 386–405.
• [13] SMOLA A.J., SCHÖLKOPF B., 2004, A Tutorial On Support Vector Regression, Statistics and computing, 14, 199–222.
• [14] BENKEDJOUH T., MEDJAHER K., ZERHOUNI N., RECHAK S., 2015, Health Assessment and Life Prediction of Cutting Tools Based on Support Vector Regression, J. Intell. Manuf., 26, 213–223.
• [15] HONG G.S., RAHMAN M., ZHOU Q., 1996, Using Neural Network for Tool Condition Monitoring Based on Wavelet Decomposition, Int. J. Mach. Tools Manuf., 36, 551–566.
• [16] SHANKAR S., MOHANRAJ T., PRAMANIK A., 2019, Tool Condition Monitoring While Using Vegetable Based Cutting Fluids During Milling of Inconel 625, J. Adv. Manuf. Syst., 18, 563–581.
• [17] LI X., LI H.-X., GUAN X.-P., DU R., 2004, Fuzzy Estimation of Feed-Cutting Force from Current Measurement-a Case Study on Intelligent Tool Wear Condition Monitoring, IEEE Trans. Syst. Man, Cybern., Part C Applications Rev., 34/4, 506–512.
• [18] SHANKAR S., MOHANRAJ T., 2015, Tool Condition Monitoring in Milling Using Sensor Fusion Technique, Proc. Malaysian Int. Tribol. Conf., 322–323.
• [19] KAYA B., OYSU C., ERTUNC H.M., OCAK H., 2012, A Support Vector Machine-Based online Tool Condition Monitoring for Milling Using Sensor Fusion and a Genetic Algorithm, Proc. Inst. Mech. Eng., Part B, J. Eng. Manuf., 226, 1808–1818.
• [20] ELANGOVAN M., RAMACHANDRAN K.I., SUGUMARAN V., 2010, Studies on Bayes Classifier for Condition Monitoring of Single Point Carbide Tipped Tool Based on Statistical and Histogram Features, Expert Syst. Appl., 37, 2059–2065.
• [21] TEHRANIZADEH F., KOCA R., BUDAK E., 2019, Investigating Effects of Serration Geometry on Milling Forces and Chatter Stability For Their Optimal Selection, Int. J. Mach. Tools Manuf., 144, 103425.
• [22] BUDAK E., OZLU E., BAKIOGLU H., BARZEGAR Z., 2016, Thermo-Mechanical Modeling of the Third Deformation Zone in Machining for Prediction of Cutting Forces, CIRP Ann., 65, 121–124.
• [23] ÖZLÜ E., EBRAHIMI ARAGHIZAD A., BUDAK E., 2020, Broaching Tool Design Through Force Modelling and Process Simulation, CIRP Ann., https://doi.org/10.1016/j.cirp.2020.04.035.
• [24] TEHRANIZADEH F., BERENJI K.R., BUDAK E., 2021, Dynamics and Chatter Stability of Crest-Cut end Mills, Int. J. Mach. Tools Manuf., 171, 103813.
• [25] KEGG R.L., 1984, One-Line Machine and Process Diagnostics, CIRP Ann., 33, 469–473.
• [26] ARMAREGO E.J.A., SMITH A.J.R., GONG Z.J., 1990, Four Plane Facet Point Drills – Basic Design and Cutting Model Predictions, CIRP Ann., 39, 41–45.
• [27] HO T.K., 1995, Random Decision Forests, Proc. 3rd Int. Conf. Doc. Anal. Recognit., IEEE, 278–282.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).