Enhancing surface quality of metal parts manufactured via LPBF: ANN classifier and bayesian learning approach
One of the metal additive manufacturing techniques, Laser Powder Bed Fusion (LPBF), is utilised to fabricate several metal composites, including S30 and AlSi10Mg, which are extensively utilised in the automotive and aerospace sectors. The main objective of this manufacturing is to achieve high surfa...
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| Published in | International journal on interactive design and manufacturing Vol. 18; no. 6; pp. 4093 - 4101 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Paris
Springer Paris
01.08.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1955-2513 1955-2505 |
| DOI | 10.1007/s12008-024-01942-8 |
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| Abstract | One of the metal additive manufacturing techniques, Laser Powder Bed Fusion (LPBF), is utilised to fabricate several metal composites, including S30 and AlSi10Mg, which are extensively utilised in the automotive and aerospace sectors. The main objective of this manufacturing is to achieve high surface quality for the complex dimensional parts specially heat exchangers and turbomachinery components. In this research, the 30 datasets of S30 alloy cube samples are collected from literature for analysis. The supervised classification algorithm (Bayesian learning) is used for the analysis and surface roughness prediction in term of current, Line offset and scans speed. The manual calculations of Bayesian leaning are performed to obtain the probability prediction for the objective function. Then the same input and output parameters are trained and modelled by ANN classifier using sklearn library from python. The performance metrics for classifier such as sensitivity, specificity, precision and accuracy are calculated for Bayesian learning and compared with ANN classifier. ANN classifier Prediction of performance characteristic gave accurate results which plays very important role in LPBF method because of high experimentation cost. |
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| AbstractList | One of the metal additive manufacturing techniques, Laser Powder Bed Fusion (LPBF), is utilised to fabricate several metal composites, including S30 and AlSi10Mg, which are extensively utilised in the automotive and aerospace sectors. The main objective of this manufacturing is to achieve high surface quality for the complex dimensional parts specially heat exchangers and turbomachinery components. In this research, the 30 datasets of S30 alloy cube samples are collected from literature for analysis. The supervised classification algorithm (Bayesian learning) is used for the analysis and surface roughness prediction in term of current, Line offset and scans speed. The manual calculations of Bayesian leaning are performed to obtain the probability prediction for the objective function. Then the same input and output parameters are trained and modelled by ANN classifier using sklearn library from python. The performance metrics for classifier such as sensitivity, specificity, precision and accuracy are calculated for Bayesian learning and compared with ANN classifier. ANN classifier Prediction of performance characteristic gave accurate results which plays very important role in LPBF method because of high experimentation cost. |
| Author | Suryawanshi, Vinayak P. Kharche, Anil W. Kapadani, Kaustubh R. Mhatre, Mitali S. Arunadevi, M. Sollapur, Shrishail B. Nalawade, Dattatraya Veeresha, G. |
| Author_xml | – sequence: 1 givenname: M. surname: Arunadevi fullname: Arunadevi, M. organization: Department of Mechanical Engineering, Dayananda Sagar College of Engineering – sequence: 2 givenname: G. surname: Veeresha fullname: Veeresha, G. email: veermech87@gmail.com organization: Department of Mechanical Engineering, New Horizon College of Engineering – sequence: 3 givenname: Anil W. surname: Kharche fullname: Kharche, Anil W. organization: Department of Civil Engineering, Padm. Dr. V. B. Kolte College of Engineering – sequence: 4 givenname: Vinayak P. surname: Suryawanshi fullname: Suryawanshi, Vinayak P. organization: Department of Mechanical Engineering, Pillai College of Engineering – sequence: 5 givenname: Shrishail B. orcidid: 0000-0002-3994-4610 surname: Sollapur fullname: Sollapur, Shrishail B. email: shrishail.sollapur@gmail.com organization: Department of IIAEM, Faculty of Engineering and Technology, JAIN (Deemed -to- be University) – sequence: 6 givenname: Mitali S. surname: Mhatre fullname: Mhatre, Mitali S. organization: Department of Mechanical Engineering, Saraswati College of Engineering Kharghar – sequence: 7 givenname: Kaustubh R. surname: Kapadani fullname: Kapadani, Kaustubh R. organization: Department of Mechanical Engineering, PES Modern COE – sequence: 8 givenname: Dattatraya surname: Nalawade fullname: Nalawade, Dattatraya organization: Department of Mechanical Engineering, Vishwakarma Institute of Information Technology |
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| References | ArunadeviMShreeramPBThanoj KumarKUdayMGowdaPerformance enhancement of CNC Milling Process using different machine learning techniquesJ. Mines Met. Fuels2023712149156 Lv Zhaoa, J.G.S., Macíasb, A., Dolimontc, A., Simarb, E., Rivière-Lorphèvrec: Comparison of residual stresses obtained by the crack compliance method for parts produced by different metal additive manufacturing techniques and after friction stir processing, Additive Manufacturing 36 101499 (2020) Hung Dang NguyenAPramanikAKBasakYDongCPrakashSDebnathSShankarISJawahirSaurav Dixit, Dharam Buddhi, a critical review on additive manufacturing of Ti-6Al-4V alloy: Microstructure and mechanical propertiesJ. Mater. Res. Technol.2022184641466110.1016/j.jmrt.2022.04.055 Yunwei GuiKAoyagiHBianAChibaDetection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beamAdditive Manuf.20225410273610.1016/j.addma.2022.102736 L, Y., Arunadevi, M., Prakash, C.P.S.: Predicton of MRR & Surface Roughness in Wire EDM Machining using Decision Tree and Naive Bayes Algorithm, 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, pp. 527–532, doi: 10.1109/ESCI50559.2021.9396857.ons. Advances in Sustainability Science and Technology. Springer, Singapore. (2021). https://doi.org/10.1007/978-981-16-4321-7_29 Hassena, A.A., Noakesa, M., Nandwanaa, P.,b, Kima, S., Kunca, V., Vaidyac, U., Lovea, L., Nycza, A.: Scaling Up metal additive manufacturing process to fabricate molds for composite manufacturing, Additive Manufacturing 32 101093 (2020) GhamarianIBallSGhayoorMPasebaniSTabeiAStatistical analysis of spatial distribution of pores in metal additive manufacturingAdditive Manuf.20214710226410.1016/j.addma.2021.102264 ZhangBSeedeRXueLAtliKCZhangCWhittAKaramanIArroyaveRElwanyAAn efficient framework for printability assessment in laser powder Bed Fusion metal additive manufacturingAdditive Manuf.20214610201810.1016/j.addma.2021.102018 EthanMParsonsSZShaikAdditive manufacturing of aluminum metal matrix composites: Mechanical alloying of composite powders and single track consolidation with laser powder bed fusionAdditive Manuf.20225010245010.1016/j.addma.2021.102450 ArunadeviMKoppalYHVasistaPMKollurSPatilSLakshminarayanaCHameedAElimination of Experimentation cost and time by data Analysis in Mechanical Property Prediction of Aluminum Alloys2023Materials TodayProceedi ngs10.1016/j.matpr.2023.09.115 Arunadevi, Y.L.M., Prakash, C.P.S., International Conference on Emerging Smart, Computing, Informatics: Predicton of MRR & Surface Roughness in Wire EDM Machining using Decision Tree and Naive Bayes Algorithm, (ESCI), Pune, India, 2021, pp. 527–532, (2021). https://doi.org/10.1109/ESCI50559.2021.9396857 Arunadevi, M., Prakash, C.P.S.: Predictive analysis and multi objective optimization of wire-EDM process using ANN, Materials Today: Proceedings, Volume 46, Part 13, Pages 6012–6016. (2021) Filippo ZaniniMSorgatoESavioSCarmignatoDimensional verification of metal additively manufactured lattice structures by X-ray computed tomography: Use of a newly developed calibrated artefact to achieve metrological traceabilityAdditive Manuf.20214710222910.1016/j.addma.2021.102229 LeiYChenlYLiouFAdditive manufacturing of functionally graded metallic materials using laser metal depositionAdditive Manuf.20203110090110.1016/j.addma.2019.100901 ArunadeviMPatilCKapadaniKROptimization process to develop Tungsten Carbide Reinforced with Aluminium MMCs using surface plots and ANNJ. Inst. Eng. India Ser. D202410.1007/s40033-024-00693-w Devi, M.A., Prakash, C.P.S., Chinnannavar, R.P., Joshi, V.P., Palada, R.S., Dixit, R.: An InformaticApproach to Predict the Mechanical Properties of Aluminum Alloys using Machine Learning Techniques, 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, pp. 536–541, (2020). https://doi.org/10.1109/ICOSEC49089.2020.9215277 Erfan MalekiSBBandiniMGuaglianoMSurface post-treatments for metal additive manufacturing: Progress, challenges, and opportunitiesAdditive Manuf.20213710161910.1016/j.addma.2020.101619 VermaSYangC-KLinC-HJengJYAdditive manufacturing of lattice structures for high strength mechanical interlocking of metal and resin during injection moldingAdditive Manuf.20224910246310.1016/j.addma.2021.102463 Guangchao LiuJXiongLTangMicrostructure and mechanical properties of 2219 aluminum alloy fabricated by double-electrode gas metal arc additive manufacturingAdditive Manuf.20203510137510.1016/j.addma.2020.101375 Yunwei Gui, K., Aoyagi, H., Bian, A., Chiba: Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam. Additive Manuf., 54, (2022) Arunadevi, M., Rani, M., Sibinraj, R., Chandru, M.K., Durga Prasad, C.: Comparison of k-nearest Neighbor & Artificial Neural Network prediction in the mechanical properties of aluminum alloys, Materials Today: Proceedings, ISSN 2214–7853. (2023) I Ghamarian (1942_CR12) 2021; 47 1942_CR15 M Ethan (1942_CR3) 2022; 50 1942_CR16 1942_CR17 B Zhang (1942_CR5) 2021; 46 M Arunadevi (1942_CR19) 2023 A Hung Dang Nguyen (1942_CR13) 2022; 18 1942_CR14 M Filippo Zanini (1942_CR8) 2021; 47 1942_CR7 1942_CR21 1942_CR1 S Verma (1942_CR4) 2022; 49 K Yunwei Gui (1942_CR6) 2022; 54 1942_CR9 J Guangchao Liu (1942_CR10) 2020; 35 Y Lei (1942_CR11) 2020; 31 M Arunadevi (1942_CR18) 2024 M Arunadevi (1942_CR20) 2023; 71 SB Erfan Maleki (1942_CR2) 2021; 37 |
| References_xml | – reference: Arunadevi, M., Prakash, C.P.S.: Predictive analysis and multi objective optimization of wire-EDM process using ANN, Materials Today: Proceedings, Volume 46, Part 13, Pages 6012–6016. (2021) – reference: Guangchao LiuJXiongLTangMicrostructure and mechanical properties of 2219 aluminum alloy fabricated by double-electrode gas metal arc additive manufacturingAdditive Manuf.20203510137510.1016/j.addma.2020.101375 – reference: Erfan MalekiSBBandiniMGuaglianoMSurface post-treatments for metal additive manufacturing: Progress, challenges, and opportunitiesAdditive Manuf.20213710161910.1016/j.addma.2020.101619 – reference: Yunwei GuiKAoyagiHBianAChibaDetection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beamAdditive Manuf.20225410273610.1016/j.addma.2022.102736 – reference: Devi, M.A., Prakash, C.P.S., Chinnannavar, R.P., Joshi, V.P., Palada, R.S., Dixit, R.: An InformaticApproach to Predict the Mechanical Properties of Aluminum Alloys using Machine Learning Techniques, 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, pp. 536–541, (2020). https://doi.org/10.1109/ICOSEC49089.2020.9215277 – reference: Filippo ZaniniMSorgatoESavioSCarmignatoDimensional verification of metal additively manufactured lattice structures by X-ray computed tomography: Use of a newly developed calibrated artefact to achieve metrological traceabilityAdditive Manuf.20214710222910.1016/j.addma.2021.102229 – reference: LeiYChenlYLiouFAdditive manufacturing of functionally graded metallic materials using laser metal depositionAdditive Manuf.20203110090110.1016/j.addma.2019.100901 – reference: GhamarianIBallSGhayoorMPasebaniSTabeiAStatistical analysis of spatial distribution of pores in metal additive manufacturingAdditive Manuf.20214710226410.1016/j.addma.2021.102264 – reference: Hassena, A.A., Noakesa, M., Nandwanaa, P.,b, Kima, S., Kunca, V., Vaidyac, U., Lovea, L., Nycza, A.: Scaling Up metal additive manufacturing process to fabricate molds for composite manufacturing, Additive Manufacturing 32 101093 (2020) – reference: Lv Zhaoa, J.G.S., Macíasb, A., Dolimontc, A., Simarb, E., Rivière-Lorphèvrec: Comparison of residual stresses obtained by the crack compliance method for parts produced by different metal additive manufacturing techniques and after friction stir processing, Additive Manufacturing 36 101499 (2020) – reference: ArunadeviMShreeramPBThanoj KumarKUdayMGowdaPerformance enhancement of CNC Milling Process using different machine learning techniquesJ. Mines Met. Fuels2023712149156 – reference: Yunwei Gui, K., Aoyagi, H., Bian, A., Chiba: Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam. Additive Manuf., 54, (2022) – reference: EthanMParsonsSZShaikAdditive manufacturing of aluminum metal matrix composites: Mechanical alloying of composite powders and single track consolidation with laser powder bed fusionAdditive Manuf.20225010245010.1016/j.addma.2021.102450 – reference: Hung Dang NguyenAPramanikAKBasakYDongCPrakashSDebnathSShankarISJawahirSaurav Dixit, Dharam Buddhi, a critical review on additive manufacturing of Ti-6Al-4V alloy: Microstructure and mechanical propertiesJ. Mater. Res. Technol.2022184641466110.1016/j.jmrt.2022.04.055 – reference: Arunadevi, Y.L.M., Prakash, C.P.S., International Conference on Emerging Smart, Computing, Informatics: Predicton of MRR & Surface Roughness in Wire EDM Machining using Decision Tree and Naive Bayes Algorithm, (ESCI), Pune, India, 2021, pp. 527–532, (2021). https://doi.org/10.1109/ESCI50559.2021.9396857 – reference: ZhangBSeedeRXueLAtliKCZhangCWhittAKaramanIArroyaveRElwanyAAn efficient framework for printability assessment in laser powder Bed Fusion metal additive manufacturingAdditive Manuf.20214610201810.1016/j.addma.2021.102018 – reference: ArunadeviMPatilCKapadaniKROptimization process to develop Tungsten Carbide Reinforced with Aluminium MMCs using surface plots and ANNJ. Inst. Eng. India Ser. D202410.1007/s40033-024-00693-w – reference: ArunadeviMKoppalYHVasistaPMKollurSPatilSLakshminarayanaCHameedAElimination of Experimentation cost and time by data Analysis in Mechanical Property Prediction of Aluminum Alloys2023Materials TodayProceedi ngs10.1016/j.matpr.2023.09.115 – reference: Arunadevi, M., Rani, M., Sibinraj, R., Chandru, M.K., Durga Prasad, C.: Comparison of k-nearest Neighbor & Artificial Neural Network prediction in the mechanical properties of aluminum alloys, Materials Today: Proceedings, ISSN 2214–7853. (2023) – reference: L, Y., Arunadevi, M., Prakash, C.P.S.: Predicton of MRR & Surface Roughness in Wire EDM Machining using Decision Tree and Naive Bayes Algorithm, 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, pp. 527–532, doi: 10.1109/ESCI50559.2021.9396857.ons. Advances in Sustainability Science and Technology. Springer, Singapore. (2021). https://doi.org/10.1007/978-981-16-4321-7_29 – reference: VermaSYangC-KLinC-HJengJYAdditive manufacturing of lattice structures for high strength mechanical interlocking of metal and resin during injection moldingAdditive Manuf.20224910246310.1016/j.addma.2021.102463 – ident: 1942_CR9 doi: 10.1016/j.addma.2020.101499 – volume: 31 start-page: 100901 year: 2020 ident: 1942_CR11 publication-title: Additive Manuf. doi: 10.1016/j.addma.2019.100901 – ident: 1942_CR17 doi: 10.1109/ESCI50559.2021.9396857 – ident: 1942_CR1 doi: 10.1007/978-981-16-4321-7_29 – volume: 71 start-page: 149 issue: 2 year: 2023 ident: 1942_CR20 publication-title: J. Mines Met. Fuels – volume-title: Elimination of Experimentation cost and time by data Analysis in Mechanical Property Prediction of Aluminum Alloys year: 2023 ident: 1942_CR19 doi: 10.1016/j.matpr.2023.09.115 – volume: 49 start-page: 102463 year: 2022 ident: 1942_CR4 publication-title: Additive Manuf. doi: 10.1016/j.addma.2021.102463 – ident: 1942_CR15 doi: 10.1109/ICOSEC49089.2020.9215277 – volume: 37 start-page: 101619 year: 2021 ident: 1942_CR2 publication-title: Additive Manuf. doi: 10.1016/j.addma.2020.101619 – volume: 47 start-page: 102229 year: 2021 ident: 1942_CR8 publication-title: Additive Manuf. doi: 10.1016/j.addma.2021.102229 – volume: 46 start-page: 102018 year: 2021 ident: 1942_CR5 publication-title: Additive Manuf. doi: 10.1016/j.addma.2021.102018 – ident: 1942_CR16 doi: 10.1016/j.matpr.2020.12.830 – volume: 50 start-page: 102450 year: 2022 ident: 1942_CR3 publication-title: Additive Manuf. doi: 10.1016/j.addma.2021.102450 – year: 2024 ident: 1942_CR18 publication-title: J. Inst. Eng. India Ser. D doi: 10.1007/s40033-024-00693-w – volume: 18 start-page: 4641 year: 2022 ident: 1942_CR13 publication-title: J. Mater. Res. Technol. doi: 10.1016/j.jmrt.2022.04.055 – ident: 1942_CR14 doi: 10.1016/j.matpr.2023.09.111 – volume: 54 start-page: 102736 year: 2022 ident: 1942_CR6 publication-title: Additive Manuf. doi: 10.1016/j.addma.2022.102736 – ident: 1942_CR21 doi: 10.1016/j.addma.2022.102736 – ident: 1942_CR7 doi: 10.1016/j.addma.2020.101093 – volume: 35 start-page: 101375 year: 2020 ident: 1942_CR10 publication-title: Additive Manuf. doi: 10.1016/j.addma.2020.101375 – volume: 47 start-page: 102264 year: 2021 ident: 1942_CR12 publication-title: Additive Manuf. doi: 10.1016/j.addma.2021.102264 |
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| Title | Enhancing surface quality of metal parts manufactured via LPBF: ANN classifier and bayesian learning approach |
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