Improving surface roughness in barrel finishing process using supervised machine learning

Subject Areas : Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering

Mohammad Sajjad Mahdieh ¹ , Mehdi Bakhshi Zadeh ² , Amirhossein Zare Reisabadi ³

1 - Department of Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 - Department of Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 - Department of Materials Engineering, Isfahan University of Technology, Isfahan, Iran

Received: 2023-01-08 Accepted : 2023-06-15 Published : 2023-06-01

Keywords: Machine Learning, ANN, Surface Roughness, Barrel Finishing Process,

Abstract :

The Barrel finishing process is a finishing method applied for cleaning, polishing, improving surface quality, Deburring, and rounding corners of both metallic and non-metallic parts. There are Several factors affect the final surface integrity of the barrel finished samples such as initial surface roughness, piece length, operation time, and different abrasive materials (i.e. aluminum oxide, steel balls, and ceramic). On the other hand, each factor has different levels, and handling this amount of data to reach desired results is approximately impossible due to the “curse of dimensionality”. Machine learning is a promising method to pave this avenue for computing huge amounts of data and predicting the future state of the system. Accordingly, in this study, it is attempted to apply a supervised machine learning algorithm, an artificial neural network- to improve surface quality in the barrel finishing process. Python is used to code the program and extract several simulations and related graphs. Results show that time has the greatest effect on surface roughness, moreover, among the different abrasive media, steel balls have the best performance to improve surface roughness and the combination of 75% steel balls and 25% aluminum oxide has the effective effect. The simulation results have an acceptable compatibility with experimental ones.

References:

 

[1] Hunsaker, J. C. (1940). Friction and surface finish. Nature, 146(3691), 138-139.

[2] Jiang, H., Browning, R., Fincher, J., Gasbarro, A., Jones, S., & Sue, H. J. (2008). Influence of surface roughness and contact load on friction coefficient and scratch behavior of thermoplastic olefins. Applied Surface Science, 254(15), 4494-4499.

[3] Mahdieh, M. S. (2020). The surface integrity of ultra-fine grain steel, Electrical discharge machined using Iso-pulse and resistance–capacitance-type generator. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 234(4), 564-573.

[4] Mahdieh, M.S. (2020.) Recast layer and heat-affected zone structure of ultra-fined grained low-carbon steel machined by electrical discharge machining.  Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 234(5): 933-944.

[5] Mahdieh, M.S. and Mahdavinejad, R. (2016). Comparative study on electrical discharge machining of ultrafine-grain al, cu, and steel.  Metallurgical and Materials Transactions A. 47(12): 6237-6247.

[6] Mahdieh, M.S. and Mahdavinejad, R. (2018). Recast layer and micro-cracks in electrical discharge machining of ultra-fine-grained aluminum.  Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 232(3): 428-437.

[7] Mahdieh, M.S. and Mahdavinejad, R.A. (2017). A study of stored energy in ultra-fined grained aluminum machined by electrical discharge machining.  Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 231(23): 4470-4478.

[8] Mahdieh, M.S. and Zare-Reisabadi, S. (2019). Effects of electro-discharge machining process on ultra-fined grain copper.  Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 233(15): 5341-5349.

[9] Mahdieh, M.S., Rafati, E. and Kargar Sichani, S. (2013). Investigation of variance of roller burnishing parameters on surface quality by taguchi approach.  ADMT Journal. 6(3):

[10] Saraeian, P., Gholami, M., Behagh, A., Behagh, O., Javadinejad, H.R. and Mahdieh, M. (2016). Influence of vibratory finishing process by incorporating abrasive ceramics and glassy materials on surface roughness of ck45 steel.  ADMT Journal. 9(4): 1-6.

[11] Vakili Sohrforozani, A., Farahnakian, M., Mahdieh, M.S., Behagh, A.M. and Behagh, O. (2019). A study of abrasive media effect on deburring in barrel finishing process.  Journal of Modern Processes in Manufacturing and Production. 8(3): 27-39.

[12] Vakili Sohrforozani, A., Farahnakian, M., Mahdieh, M.S., Behagh, A.M. and Behagh, O. (2020). Effects of abrasive media on surface roughness in barrel finishing process.  ADMT Journal. 13(3): 75-82.

[13] Yang, S., Li, W. and Chen, H. (2018). Surface finishing theory and new technology. ed. Springer,

[14] Mahdieh, M.S. and Esteki, M.R. (2022). Feasibility investigation of hydroforming of dental drill body by fem simulation.  Journal of Modern Processes in Manufacturing and Production. 11(2): 71-83.

[15] Mahdieh, M.S. and Monjezi, A. (2022). Investigation of an innovative cleaning method for the vertical oil storage tank by FEM simulation.  Iranian Journal of Materials Forming.):

[16] Chiancola, M. (1995). Choosing the right media to meet mass finishing goals.  Metal Finishing. 93(12): 37-39.

[17] Boschetto, A. and Veniali, F. (2009). Workpiece and media tracking in barrel finishing.  International Journal of Machining and Machinability of Materials. 6(3-4): 305-321.

[18] Dong, H. and Moys, M. 2001. A technique to measure velocities of a ball moving in a tumbling mill and its applications.  Minerals engineering. 14(8): 841-850.

[19] Steiner, H. 1996. Characterization of laboratory-scale tumbling mills.  International Journal of Mineral Processing. 44: 373-382.

[20] Van Puyvelde, D., Young, B., Wilson, M. and Schmidt, S. (1999). Experimental determination of transverse mixing kinetics in a rolling drum by image analysis.  Powder Technology. 106(3): 183-191.

[21] Boschetto, A., Bottini, L. and Veniali, F. (2013). Microremoval modeling of surface roughness in barrel finishing.  The International Journal of Advanced Manufacturing Technology. 69(9): 2343-2354.

[22] Bbosa, L., Govender, I., Mainza, A. and Powell, M. (2011). Power draw estimations in experimental tumbling mills using pept.  Minerals engineering. 24(3-4): 319-324.

[23] Boschetto, A., Ruggiero, A. and Veniali, F. (2007). Deburring of sheet metal by barrel finishing. Proc. Key Engineering Materials.  193-200.

[24] Li, X., Li, W., Yang, S. and Shi, H. (2018). Experimental investigation into the surface integrity and tribological property of aisi 1045 steel specimen for barrel finishing.  Procedia Cirp. 71: 47-52.

[25] Fan, G., A.S, E.-S., Eftekhari, S.A., Hekmatifar, M., Toghraie, D., Mohammed, A.S. and Khan, A. (2022). A well-trained artificial neural network (ann) using the trainlm algorithm for predicting the rheological behavior of water – ethylene glycol/wo3 – mwcnts nanofluid.  International Communications in Heat and Mass Transfer. 131: 105857.

[26] Xia, J.S., Khaje Khabaz, M., Patra, I., Khalid, I., Alvarez, J.R.N., Rahmanian, A., Eftekhari, S.A. and Toghraie, D. (2023). Using feed-forward perceptron artificial neural network (ann) model to determine the rolling force, power and slip of the tandem cold rolling.  ISA Transactions. 132: 353-363.

[27] Esfe, M. H., Esmaily, R., Khabaz, M. K., Alizadeh, A. A., Pirmoradian, M., Rahmanian, A., & Toghraie, D. (2023). A novel integrated model to improve the dynamic viscosity of MWCNT-Al2O3 (40: 60)/Oil 5W50 hybrid nano-lubricant using artificial neural networks (ANNs). Tribology International, 178, 108086.

[28] Azimi, M., Kolahdooz, A. and Eftekhari, S.A. (2017). An optimization on the din1. 2080 alloy in the electrical discharge machining process using ann and ga.  Journal of Modern Processes in Manufacturing and Production. 6(1): 33-47.

[29] Baghoolizadeh, M., Nasajpour-Esfahani, N., Pirmoradian, M., & Toghraie, D. (2023). Using different machine learning algorithms to predict the rheological behavior of oil SAE40-based nano-lubricant in the presence of MWCNT and MgO nanoparticles. Tribology International, 108759.

 [30] Tian, C., Tang, Z., Zhang, H., Gao, X. and Xie, Y. (2022). Operating condition recognition based on temporal cumulative distribution function and adaboost-extreme learning machine in zinc flotation process.  Powder Technology. 395: 545-555.

[31] Li, R., Jin, M. and Paquit, V.C. (2021). Geometrical defect detection for additive manufacturing with machine learning models.  Materials & Design. 206: 109726.

[32] Castro, B.M., Elbadawi, M., Ong, J.J., Pollard, T., Song, Z., Gaisford, S., Pérez, G., Basit, A.W., Cabalar, P. and Goyanes, A. (2021). Machine learning predicts 3d printing performance of over 900 drug delivery systems.  Journal of Controlled Release. 337: 530-545.

[33] Ahmed, T., Sharma, P., Karmaker, C.L. and Nasir, S. (2020). Warpage prediction of injection-molded pvc part using ensemble machine learning algorithm.  Materials Today: Proceedings.):

[34] Song, C.-H., Cao, J.-X. and Yang, S.-C. (2008. Quality prediction of centrifugal barrel finishing using genetic neural network.