Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens

Document Type : Special Issue: Mechanics of Advanced Fiber Reinforced Composite Structures

Authors

1 School of Industrial and Information Engineering, Politecnico Di Milano, Milan, Italy

2 Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, Maharashtra, India

Abstract

One of the most important factors affecting the functioning and performance of additively produced components is surface roughness. Precise estimation of surface roughness is essential for streamlining production procedures and guaranteeing product quality. Recently, quantum computing has drawn interest as a possible way to solve challenging issues and produce accurate prediction models. For the first time, we compare three quantum algorithms in-depth in this research paper for surface roughness prediction in additively manufactured specimens: the Quantum Neural Network (QNN), Quantum Forest (Q-Forest), and Variational Quantum Classifier (VQC) modified for regression. Mean Squared Error (MSE), Mean Absolute Error (MAE), and Explained Variance Score (EVS) are the assessment metrics we use to evaluate the algorithms' performance. With an MSE of 56.905, an MAE of 7.479, and an EVS of 0.2957, the Q-Forest algorithm outperforms the other algorithms, according to our data. On the other hand, the QNN method shows a negative EVS of -0.444 along with a higher MSE of 60.840 and MAE of 7.671, suggesting that it might not be the best choice for surface roughness prediction in this application. The regression-adapted VQC has an MSE of 59.121, an MAE of 7.597, and an EVS of -0.0106, indicating that it performs inferior to the Q-Forest approach as well.

Keywords

Main Subjects

References

[1]    Steane, A., 1998. Quantum computing. Reports on Progress in Physics, 61(2), p.117.
[2]    National Academies of Sciences, Engineering and Medicine, 2019. Quantum computing: progress and prospects.
[3]    Rawat, B., Mehra, N., Bist, A.S., Yusup, M. and Sanjaya, Y.P.A., 2022. Quantum computing and ai: Impacts & possibilities. ADI Journal on Recent Innovation, 3(2), pp.202-207.
[4]    Lubinski, T., Johri, S., Varosy, P., Coleman, J., Zhao, L., Necaise, J., Baldwin, C.H., Mayer, K. and Proctor, T., 2023. Application-oriented performance benchmarks for quantum computing. IEEE Transactions on Quantum Engineering.
[5]    Suzuki, Y., Endo, S., Fujii, K. and Tokunaga, Y., 2022. Quantum error mitigation as a universal error reduction technique: applications from the nisq to the fault-tolerant quantum computing eras. PRX Quantum, 3(1), p.010345.
[6]    Guijo, D., Onofre, V., Del Bimbo, G., Mugel, S., Estepa, D., De Carlos, X., Adell, A., Lojo, A., Bilbao, J. and Orus, R., 2022. Quantum artificial vision for defect detection in manufacturing. arXiv preprint arXiv:2208.04988.
[7]    Yonaga, K., Miyama, M., Ohzeki, M., Hirano, K., Kobayashi, H. and Kurokawa, T., 2022. Quantum optimization with Lagrangian decomposition for multiple-process scheduling in steel manufacturing. Isij International, 62(9), pp.1874-1880.
[8]    Permin, E., Borgard, S., Castillo Velasquez, L. and Pyschny, N., 2022. A Simple Approach for Complexity Reduction in Job Shop Scheduling Using Quantum Computers. Available at SSRN 4259131.
[9]    Zinner, M., Dahlhausen, F., Boehme, P., Ehlers, J., Bieske, L. and Fehring, L., 2022. Toward the institutionalization of quantum computing in pharmaceutical research. Drug Discovery Today, 27(2), pp.378-383.
[10]    Nakano, A., 2023. Exascale Simulations of Quantum Materials Guided by AI and Quantum Computing. Bulletin of the American Physical Society.
[11]    Sennane, W., Rancic, M., Greene-Diniz, G., Zsolt Manrique, D., Magnin, Y., Cordier, P., Llewellyn, P., Krompiec, M., Muñoz Ramo, D. and Shishenina, E., 2023. Modelling carbon capture on metal-organic frameworks with quantum computing. Bulletin of the American Physical Society.
[12]    Orth, P., Mukherjee, A., Yao, Y.X., Huynh, A. and Trevisan, T., 2023. Quantum computing simulation of nonlinear optical response in Hubbard models. Bulletin of the American Physical Society.
[13]    Matsuo, S. and Souma, S., 2023. A proposal of quantum computing algorithm to solve Poisson equation for nanoscale devices under Neumann boundary condition. Solid-State Electronics, 200, p.108547.
[14]    Guo, N., and Leu, M. C., 2013. Additive manufacturing: technology, applications and research needs. Frontiers of Mechanical Engineering, 8(3), pp. 215-243.
[15]    Mahesh, M., and Basavarajappa, S., 2015. Surface roughness prediction in turning of titanium alloy by machine vision system. Procedia Engineering, 97, pp.369-376.
[16]    Kumar, P., Singh, R., and Ahuja, I. P. S., 2012. Investigations on surface roughness in fused deposition modeling (FDM) using predictive modeling techniques. Journal of Manufacturing Processes, 14(3), pp.393-402.
[17]    Singh, S., and Prakash, C., 2020. Optimization of process parameters to minimize surface roughness in FDM: A review and analysis. Journal of Manufacturing Processes, 49, pp. 92-107.
[18]    Panda, B. N., Sahoo, S., Sahu, P. K., and Mahapatra, S. S., 2010. Optimization of fused deposition modelling (FDM) process parameters using Taguchi method. International Journal of Manufacturing Technology and Management, 21(1-2), pp.100-112.
[19]    Potnis, M.S., Singh, A., Jatti, V.S. et al. Part quality investigation in fused deposition modelling using machine learning classifiers. Int J Interact Des Manuf (2023). https://doi.org/10.1007/s12008-023-01493-4
[20]    Jatti, V. S., et al, 2022. Mechanical Properties of 3D-printed Components Using Fused Deposition Modeling: Optimization Using the Desirability Approach and Machine Learning Regressor. Applied System Innovation, 5(6). https://www.mdpi.com/2571-5577/5/6/112
[21]    Mishra, A., Jatti, V. S., 2023, Neurosymbolic artificial intelligence (NSAI) based algorithm for predicting the impact strength of additive manufactured polylactic acid (PLA) specimens. Engineering Research Express, 5(3). https://iopscience.iop.org/article/10.1088/2631-8695/ace610 
[22]    Mishra, A., Jatti, V.S., 2023. Novel Coupled Genetic Algorithm–Machine Learning Approach for Predicting Surface Roughness in Fused Deposition Modeling of Polylactic Acid Specimens. Jounral of Materials Engineering and Performance. https://doi.org/10.1007/s11665-023-08379-2 
[23]    Mishra, A. Jatti, V. S.,  Sefene, E.M., Paliwal, S., 2023. Explainable Artificial Intelligence (XAI) and Supervised Machine Learning-based Algorithms for Prediction of Surface Roughness of Additively Manufactured Polylactic Acid (PLA) Specimens. Applied Mechanics, 4, pp. 668-698. doi: https://doi.org/10.3390/applmech4020034 
Mechanics of Advanced Composite Structures
Volume 12, Special Issue 2: Mechanics of Advanced Fiber-Reinforced Composite Structures - Serial Number 25
Celebrating the 50th Anniversary of Semnan University, Handled by the Esteemed Journal Editor, Prof. Dr. Mavinkere Rangappa Sanjay - In Progress
August 2025
Pages 425-433

Files

Share

How to cite

Statistics