Smart manufacturing applications for inspection and quality assurance processes
Smart manufacturing had a high impact in recent years within the inspection and quality assurance processes, providing innovative technologies in machine learning. Consequently, the article presents a systematic review of the applications of automation to statistical quality control in companies in...
- Autores:
-
Galindo Salcedo, Maremys
Pertúz Montero, Altagracia
Guzmán Castillo, Stefania
Gómez Charris, Yulineth
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9360
- Acceso en línea:
- https://hdl.handle.net/11323/9360
https://doi.org/10.1016/j.procs.2021.12.282
https://repositorio.cuc.edu.co/
- Palabra clave:
- Quality
Automation
Smart manufacturing
Machine learning
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Smart manufacturing applications for inspection and quality assurance processes |
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Smart manufacturing applications for inspection and quality assurance processes |
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Smart manufacturing applications for inspection and quality assurance processes Quality Automation Smart manufacturing Machine learning |
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Smart manufacturing applications for inspection and quality assurance processes |
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Smart manufacturing applications for inspection and quality assurance processes |
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Smart manufacturing applications for inspection and quality assurance processes |
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Smart manufacturing applications for inspection and quality assurance processes |
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Smart manufacturing applications for inspection and quality assurance processes |
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Galindo Salcedo, Maremys Pertúz Montero, Altagracia Guzmán Castillo, Stefania Gómez Charris, Yulineth |
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Galindo Salcedo, Maremys Pertúz Montero, Altagracia Guzmán Castillo, Stefania Gómez Charris, Yulineth |
| dc.subject.proposal.eng.fl_str_mv |
Quality Automation Smart manufacturing Machine learning |
| topic |
Quality Automation Smart manufacturing Machine learning |
| description |
Smart manufacturing had a high impact in recent years within the inspection and quality assurance processes, providing innovative technologies in machine learning. Consequently, the article presents a systematic review of the applications of automation to statistical quality control in companies in the industrial sector, deriving subtopics such as artificial vision, intelligent manufacturing, inspection in the different production processes, neural networks, automation through statistical process control techniques and finally quality assurance, in addition, a general analysis of them is shown. Additionally, it is shown that these technologies improve automated manufacturing processes, making them more efficient, with better performance and productivity, also contributing to the optimization of time, cost reduction, strengthening of inspection, and quality assurance. Finally, future research opportunities for industrial applications are identified. |
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2022 |
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2022-07-12T14:26:27Z |
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2022-01-26 |
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Maremys Galindo-Salcedo, Altagracia Pertúz-Moreno, Stefania Guzmán-Castillo, Yulineth Gómez-Charris, Alfonso R. Romero-Conrado, Smart manufacturing applications for inspection and quality assurance processes, Procedia Computer Science, Volume 198, 2022, Pages 536-541,ISSN 1877-0509, |
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18770509 |
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https://hdl.handle.net/11323/9360 |
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https://doi.org/10.1016/j.procs.2021.12.282 |
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10.1016/j.procs.2021.12.282 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Maremys Galindo-Salcedo, Altagracia Pertúz-Moreno, Stefania Guzmán-Castillo, Yulineth Gómez-Charris, Alfonso R. Romero-Conrado, Smart manufacturing applications for inspection and quality assurance processes, Procedia Computer Science, Volume 198, 2022, Pages 536-541,ISSN 1877-0509, 18770509 10.1016/j.procs.2021.12.282 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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1] Sarivan IM, Greiner JN, Díez Álvarez D, Euteneuer F, Reichenbach M, Madsen O, et al. Enabling real-time quality inspection in smart manufacturing through wearable smart devices and deep learning. Procedia Manuf., vol. 51, Elsevier; 2020, p. 373–80. https://doi.org/10.1016/j.promfg.2020.10.053. [2] Galvis Leal L, Orozco De Alba L, Romero-Conrado AR. Desarrollo, tendencias, aplicaciones y herramientas de la industria 4.0 en el sector textil. Boletín Innovación, Logística y Operaciones - BILO 2020;2:2–5. https://doi.org/10.17981/bilo.02.01.2020.15. [3] Romero-Conrado AR, Coronado-Hernandez JR, Rius-Sorolla G, García-Sabater JP. A Tabu list-based algorithm for capacitated multilevel lot-sizing with alternate bills of materials and co-production environments. Appl Sci 2019;9:6–9. https://doi.org/10.3390/app9071464. [4] Lin YJ, Wei SH, Huang CY. Intelligent Manufacturing Control Systems: The Core of Smart Factory. Procedia Manuf 2019;39:389–97. https://doi.org/10.1016/J.PROMFG.2020.01.382. [5] Javaid M, Abid Haleem, Pratap Singh R, Suman R. Significance of Quality 4.0 towards comprehensive enhancement in manufacturing sector. Sensors Int 2021;2:100109. https://doi.org/10.1016/j.sintl.2021.100109. [6] Fahle S, Prinz C, Kuhlenkötter B. Systematic review on machine learning (ML) methods for manufacturing processes - Identifying artificial intelligence (AI) methods for field application. Procedia CIRP 2020;93:413–8. https://doi.org/10.1016/j.procir.2020.04.109. [7] Goldman C V., Baltaxe M, Chakraborty D, Arinez J. Explaining Learning Models in Manufacturing Processes. Procedia Comput. Sci., vol. 180, Elsevier; 2021, p. 259–68. https://doi.org/10.1016/j.procs.2021.01.163. [8] Schmitt J, Bönig J, Borggräfe T, Beitinger G, Deuse J. Predictive model-based quality inspection using Machine Learning and Edge [9] Fernández-Robles L, Azzopardi G, Alegre E, Petkov N. Machine-vision-based identification of broken inserts in edge profile milling heads. Robot Comput Integr Manuf 2017;44:276–83. https://doi.org/10.1016/J.RCIM.2016.10.004. [10] Stavropoulos P, Papacharalampopoulos A, Petridis D. A vision-based system for real-time defect detection: a rubber compound part case study. Procedia CIRP 2020;93:1230–5. https://doi.org/10.1016/J.PROCIR.2020.04.159. [11] Deshpande AM, Minai AA, Kumar M. One-Shot Recognition of Manufacturing Defects in Steel Surfaces. Procedia Manuf 2020;48:1064–71. https://doi.org/10.1016/J.PROMFG.2020.05.146. [12] Domański PD, Golonka S, Marusak PM, Moszowski B. Robust and Asymmetric Assessment of the Benefits from Improved Control – Industrial Validation. IFAC-PapersOnLine 2018;51:815–20. https://doi.org/10.1016/j.ifacol.2018.09.260. [13] Brito T, Queiroz J, Piardi L, Fernandes LA, Lima J, Leitão P. A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems. Procedia Manuf 2020;51:11–8. https://doi.org/10.1016/j.promfg.2020.10.003. [14] Popper J, Harms C, Ruskowski M. Enabling reliable visual quality control in smart factories through TSN. Procedia CIRP 2020;88:549–53. https://doi.org/10.1016/j.procir.2020.05.095. [15] Susto GA, Terzi M, Beghi A. Anomaly Detection Approaches for Semiconductor Manufacturing. Procedia Manuf 2017;11:2018–24. https://doi.org/10.1016/J.PROMFG.2017.07.353. [16] Struchtrup AS, Kvaktun D, Schiffers R. Adaptive quality prediction in injection molding based on ensemble learning. Procedia CIRP 2021;99:301–6. https://doi.org/10.1016/J.PROCIR.2021.03.045. [17] Kolosowski M, Duda J, Tomasiak J. Statistical process control in conditions of piece and small lot production. Ann DAAAM Proc Int DAAAM Symp 2015;2015-Janua:147–55. https://doi.org/10.2507/26th.daaam.proceedings.021. [18] Bottani E, Montanari R, Volpi A, Tebaldi L, Maria G Di. Statistical Process Control of assembly lines in a manufacturing plant: Process Capability assessment. Procedia Comput Sci 2021;180:1024–33. https://doi.org/10.1016/j.procs.2021.01.353. [19] Harris K, Triantafyllopoulos K, Stillman E, McLeay T. A Multivariate Control Chart for Autocorrelated Tool Wear Processes. Qual Reliab Eng Int 2016;32:2093–106. https://doi.org/10.1002/QRE.2032. [20] Schumacher A, Sihn W. Development of a monitoring system for implementation of industrial digitalization and automation using 143 key performance indicators. Procedia CIRP 2020;93:1310–5. https://doi.org/10.1016/j.procir.2020.03.012. [21] Venkatasubramanian V. Process Fault Detection and Diagnosis: Past, Present and Future. IFAC Proc Vol 2001;34:1–13. https://doi.org/10.1016/s1474-6670(17)33563-2. [22] Bici M, Broggiato GB, Campana F, Dughiero A. Computer Aided Inspection Procedures to Support Smart Manufacturing of Injection Moulded Components. Procedia Manuf 2017;11:1184–92. https://doi.org/10.1016/J.PROMFG.2017.07.243. [23] Xia Q, Jiang C, Yang C, Zheng X, Pan X, Shuai Y, et al. A method towards smart manufacturing capabilities and performance measurement. Procedia Manuf 2019;39:851–8. https://doi.org/10.1016/j.promfg.2020.01.415. [24] Crawford B, Sourki R, Khayyam H, S. Milani A. A machine learning framework with dataset-knowledgeability pre-assessment and a local decision-boundary crispness score: An industry 4.0-based case study on composite autoclave manufacturing. Comput Ind 2021;132:103510. https://doi.org/10.1016/j.compind.2021.103510. [25] Date K, Tanaka Y. Quality-Oriented Statistical Process Control Utilizing Bayesian Modeling. IEEE Trans Semicond Manuf 2021;34:307–11. https://doi.org/10.1109/TSM.2021.3073954. [26] Batabyal A, Sagar S, Zhang J, Dube T, Yang X, Zhang J. Gaussian Process-Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling. ASCE-ASME J Risk Uncertain Eng Syst 2022;8. https://doi.org/https://doi.org/10.1115/1.4051745. [27] Schippers WAJ. An integrated approach to process control. Int J Prod Econ 2001;69:93–105. https://doi.org/10.1016/S0925- 5273(98)00243-6. |
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Galindo Salcedo, Maremys Pertúz Montero, Altagracia Guzmán Castillo, StefaniaGómez Charris, Yulineth2022-07-12T14:26:27Z2022-07-12T14:26:27Z2022-01-26Maremys Galindo-Salcedo, Altagracia Pertúz-Moreno, Stefania Guzmán-Castillo, Yulineth Gómez-Charris, Alfonso R. Romero-Conrado, Smart manufacturing applications for inspection and quality assurance processes, Procedia Computer Science, Volume 198, 2022, Pages 536-541,ISSN 1877-0509,18770509https://hdl.handle.net/11323/9360https://doi.org/10.1016/j.procs.2021.12.28210.1016/j.procs.2021.12.282Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Smart manufacturing had a high impact in recent years within the inspection and quality assurance processes, providing innovative technologies in machine learning. Consequently, the article presents a systematic review of the applications of automation to statistical quality control in companies in the industrial sector, deriving subtopics such as artificial vision, intelligent manufacturing, inspection in the different production processes, neural networks, automation through statistical process control techniques and finally quality assurance, in addition, a general analysis of them is shown. Additionally, it is shown that these technologies improve automated manufacturing processes, making them more efficient, with better performance and productivity, also contributing to the optimization of time, cost reduction, strengthening of inspection, and quality assurance. Finally, future research opportunities for industrial applications are identified.6 páginasapplication/pdfengElsevier BVNetherlandsAtribución 4.0 Internacional (CC BY 4.0)© 2021 The Authors. Published by Elsevier B.V.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Smart manufacturing applications for inspection and quality assurance processesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.sciencedirect.com/science/article/pii/S1877050921025217?via%3DihubProcedia Computer Science1] Sarivan IM, Greiner JN, Díez Álvarez D, Euteneuer F, Reichenbach M, Madsen O, et al. Enabling real-time quality inspection in smart manufacturing through wearable smart devices and deep learning. Procedia Manuf., vol. 51, Elsevier; 2020, p. 373–80. https://doi.org/10.1016/j.promfg.2020.10.053.[2] Galvis Leal L, Orozco De Alba L, Romero-Conrado AR. Desarrollo, tendencias, aplicaciones y herramientas de la industria 4.0 en el sector textil. Boletín Innovación, Logística y Operaciones - BILO 2020;2:2–5. https://doi.org/10.17981/bilo.02.01.2020.15.[3] Romero-Conrado AR, Coronado-Hernandez JR, Rius-Sorolla G, García-Sabater JP. A Tabu list-based algorithm for capacitated multilevel lot-sizing with alternate bills of materials and co-production environments. Appl Sci 2019;9:6–9. https://doi.org/10.3390/app9071464.[4] Lin YJ, Wei SH, Huang CY. Intelligent Manufacturing Control Systems: The Core of Smart Factory. Procedia Manuf 2019;39:389–97. https://doi.org/10.1016/J.PROMFG.2020.01.382.[5] Javaid M, Abid Haleem, Pratap Singh R, Suman R. Significance of Quality 4.0 towards comprehensive enhancement in manufacturing sector. Sensors Int 2021;2:100109. https://doi.org/10.1016/j.sintl.2021.100109.[6] Fahle S, Prinz C, Kuhlenkötter B. Systematic review on machine learning (ML) methods for manufacturing processes - Identifying artificial intelligence (AI) methods for field application. Procedia CIRP 2020;93:413–8. https://doi.org/10.1016/j.procir.2020.04.109.[7] Goldman C V., Baltaxe M, Chakraborty D, Arinez J. Explaining Learning Models in Manufacturing Processes. Procedia Comput. Sci., vol. 180, Elsevier; 2021, p. 259–68. https://doi.org/10.1016/j.procs.2021.01.163.[8] Schmitt J, Bönig J, Borggräfe T, Beitinger G, Deuse J. Predictive model-based quality inspection using Machine Learning and Edge[9] Fernández-Robles L, Azzopardi G, Alegre E, Petkov N. Machine-vision-based identification of broken inserts in edge profile milling heads. Robot Comput Integr Manuf 2017;44:276–83. https://doi.org/10.1016/J.RCIM.2016.10.004.[10] Stavropoulos P, Papacharalampopoulos A, Petridis D. A vision-based system for real-time defect detection: a rubber compound part case study. Procedia CIRP 2020;93:1230–5. https://doi.org/10.1016/J.PROCIR.2020.04.159.[11] Deshpande AM, Minai AA, Kumar M. One-Shot Recognition of Manufacturing Defects in Steel Surfaces. Procedia Manuf 2020;48:1064–71. https://doi.org/10.1016/J.PROMFG.2020.05.146.[12] Domański PD, Golonka S, Marusak PM, Moszowski B. Robust and Asymmetric Assessment of the Benefits from Improved Control – Industrial Validation. IFAC-PapersOnLine 2018;51:815–20. https://doi.org/10.1016/j.ifacol.2018.09.260.[13] Brito T, Queiroz J, Piardi L, Fernandes LA, Lima J, Leitão P. A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems. Procedia Manuf 2020;51:11–8. https://doi.org/10.1016/j.promfg.2020.10.003.[14] Popper J, Harms C, Ruskowski M. Enabling reliable visual quality control in smart factories through TSN. Procedia CIRP 2020;88:549–53. https://doi.org/10.1016/j.procir.2020.05.095.[15] Susto GA, Terzi M, Beghi A. Anomaly Detection Approaches for Semiconductor Manufacturing. Procedia Manuf 2017;11:2018–24. https://doi.org/10.1016/J.PROMFG.2017.07.353.[16] Struchtrup AS, Kvaktun D, Schiffers R. Adaptive quality prediction in injection molding based on ensemble learning. Procedia CIRP 2021;99:301–6. https://doi.org/10.1016/J.PROCIR.2021.03.045.[17] Kolosowski M, Duda J, Tomasiak J. Statistical process control in conditions of piece and small lot production. Ann DAAAM Proc Int DAAAM Symp 2015;2015-Janua:147–55. https://doi.org/10.2507/26th.daaam.proceedings.021.[18] Bottani E, Montanari R, Volpi A, Tebaldi L, Maria G Di. Statistical Process Control of assembly lines in a manufacturing plant: Process Capability assessment. Procedia Comput Sci 2021;180:1024–33. https://doi.org/10.1016/j.procs.2021.01.353.[19] Harris K, Triantafyllopoulos K, Stillman E, McLeay T. A Multivariate Control Chart for Autocorrelated Tool Wear Processes. Qual Reliab Eng Int 2016;32:2093–106. https://doi.org/10.1002/QRE.2032.[20] Schumacher A, Sihn W. Development of a monitoring system for implementation of industrial digitalization and automation using 143 key performance indicators. Procedia CIRP 2020;93:1310–5. https://doi.org/10.1016/j.procir.2020.03.012.[21] Venkatasubramanian V. Process Fault Detection and Diagnosis: Past, Present and Future. IFAC Proc Vol 2001;34:1–13. https://doi.org/10.1016/s1474-6670(17)33563-2.[22] Bici M, Broggiato GB, Campana F, Dughiero A. Computer Aided Inspection Procedures to Support Smart Manufacturing of Injection Moulded Components. Procedia Manuf 2017;11:1184–92. https://doi.org/10.1016/J.PROMFG.2017.07.243.[23] Xia Q, Jiang C, Yang C, Zheng X, Pan X, Shuai Y, et al. A method towards smart manufacturing capabilities and performance measurement. Procedia Manuf 2019;39:851–8. https://doi.org/10.1016/j.promfg.2020.01.415.[24] Crawford B, Sourki R, Khayyam H, S. Milani A. A machine learning framework with dataset-knowledgeability pre-assessment and a local decision-boundary crispness score: An industry 4.0-based case study on composite autoclave manufacturing. Comput Ind 2021;132:103510. https://doi.org/10.1016/j.compind.2021.103510.[25] Date K, Tanaka Y. Quality-Oriented Statistical Process Control Utilizing Bayesian Modeling. IEEE Trans Semicond Manuf 2021;34:307–11. https://doi.org/10.1109/TSM.2021.3073954.[26] Batabyal A, Sagar S, Zhang J, Dube T, Yang X, Zhang J. Gaussian Process-Based Model to Optimize Additively Manufactured Powder Microstructures From Phase Field Modeling. ASCE-ASME J Risk Uncertain Eng Syst 2022;8. https://doi.org/https://doi.org/10.1115/1.4051745.[27] Schippers WAJ. An integrated approach to process control. 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