Predicting product quality with backpropagation: A thermoplastic injection moulding case study
Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Smith, Alice E. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1993 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Limited 1993 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer-Verlag, 1985, 8(1993), 4 vom: Juli, Seite 252-257 |
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| Übergeordnetes Werk: |
volume:8 ; year:1993 ; number:4 ; month:07 ; pages:252-257 |
| Links: |
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| DOI / URN: |
10.1007/BF01748635 |
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| Katalog-ID: |
OLC2025984820 |
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| 520 | |a Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. | ||
| 650 | 4 | |a Artificial neural networks | |
| 650 | 4 | |a Backpropagation neural networks | |
| 650 | 4 | |a Injection moulding | |
| 650 | 4 | |a Process control | |
| 650 | 4 | |a Quality control | |
| 650 | 4 | |a Statistical process control | |
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10.1007/BF01748635 doi (DE-627)OLC2025984820 (DE-He213)BF01748635-p DE-627 ger DE-627 rakwb eng 670 VZ Smith, Alice E. verfasserin aut Predicting product quality with backpropagation: A thermoplastic injection moulding case study 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1993 Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. Artificial neural networks Backpropagation neural networks Injection moulding Process control Quality control Statistical process control Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 8(1993), 4 vom: Juli, Seite 252-257 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:8 year:1993 number:4 month:07 pages:252-257 https://doi.org/10.1007/BF01748635 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 8 1993 4 07 252-257 |
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10.1007/BF01748635 doi (DE-627)OLC2025984820 (DE-He213)BF01748635-p DE-627 ger DE-627 rakwb eng 670 VZ Smith, Alice E. verfasserin aut Predicting product quality with backpropagation: A thermoplastic injection moulding case study 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1993 Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. Artificial neural networks Backpropagation neural networks Injection moulding Process control Quality control Statistical process control Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 8(1993), 4 vom: Juli, Seite 252-257 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:8 year:1993 number:4 month:07 pages:252-257 https://doi.org/10.1007/BF01748635 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 8 1993 4 07 252-257 |
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10.1007/BF01748635 doi (DE-627)OLC2025984820 (DE-He213)BF01748635-p DE-627 ger DE-627 rakwb eng 670 VZ Smith, Alice E. verfasserin aut Predicting product quality with backpropagation: A thermoplastic injection moulding case study 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1993 Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. Artificial neural networks Backpropagation neural networks Injection moulding Process control Quality control Statistical process control Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 8(1993), 4 vom: Juli, Seite 252-257 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:8 year:1993 number:4 month:07 pages:252-257 https://doi.org/10.1007/BF01748635 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 8 1993 4 07 252-257 |
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10.1007/BF01748635 doi (DE-627)OLC2025984820 (DE-He213)BF01748635-p DE-627 ger DE-627 rakwb eng 670 VZ Smith, Alice E. verfasserin aut Predicting product quality with backpropagation: A thermoplastic injection moulding case study 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1993 Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. Artificial neural networks Backpropagation neural networks Injection moulding Process control Quality control Statistical process control Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 8(1993), 4 vom: Juli, Seite 252-257 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:8 year:1993 number:4 month:07 pages:252-257 https://doi.org/10.1007/BF01748635 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 8 1993 4 07 252-257 |
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10.1007/BF01748635 doi (DE-627)OLC2025984820 (DE-He213)BF01748635-p DE-627 ger DE-627 rakwb eng 670 VZ Smith, Alice E. verfasserin aut Predicting product quality with backpropagation: A thermoplastic injection moulding case study 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 1993 Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. Artificial neural networks Backpropagation neural networks Injection moulding Process control Quality control Statistical process control Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 8(1993), 4 vom: Juli, Seite 252-257 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:8 year:1993 number:4 month:07 pages:252-257 https://doi.org/10.1007/BF01748635 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_21 GBV_ILN_23 GBV_ILN_32 GBV_ILN_70 GBV_ILN_132 GBV_ILN_136 GBV_ILN_150 GBV_ILN_161 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2241 GBV_ILN_2333 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4319 AR 8 1993 4 07 252-257 |
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Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. © Springer-Verlag London Limited 1993 |
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Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. © Springer-Verlag London Limited 1993 |
| abstract_unstemmed |
Abstract This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling. © Springer-Verlag London Limited 1993 |
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Predicting product quality with backpropagation: A thermoplastic injection moulding case study |
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