Inline control of a strip bending process in mass production

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

The accuracy of a metal forming process is highly influenced by the variation of the process input, such as variation of friction and material properties. Therefore it may be required to decrease the input variation to meet the desired accuracy. However, this may increase the production costs, since stricter requirements generally come with a higher price tag. Other solutions may be to design the process in such a way that it becomes less sensitive to the input variation, or to implement a control scheme in the production line. Adding sensors to measure the state of the production process and actuators to change the process settings during production allows for a drastic increase of the production accuracy. In this study a numerical comparison is made between different methods to control a thin strip bending process with an over-bending and a back-bending stage. The aim is to implement the method in a mass production line with a production speed of 100 products per minute, which demands for fast measurement, processing and actuation. A discrete control scheme is used, meaning that the process settings can only be adapted in between the process stages. The adaptable control parameter is the amount of back-bending. In the case of the strip bending process, the angle of the measured strip may be used to adapt the angle of the following strip. However, the accuracy of such a control scheme is limited by product-to-product variation. Therefore the force of the over-bending stage is measured and used to construct a predictive model of the process based on measured process data. Hence, the final angle of the flap can be predicted by measuring the force at the first stage of the process. Different factors influence the effectiveness of the control methods: the size and autocorrelation of the input variation, the noise of the measurement system and the predictive ability of the predictive model. A qualitative study on the influence of these factors on different control methods is given in this paper.
Original languageEnglish
Title of host publicationIDDRG 2014 Conference Proceedings, Innovations for the sheet metal industry
Place of PublicationParis
Pages175-180
Publication statusPublished - 1 Jun 2014
EventIDDRG 2014: Innovations for the sheet metal industry - Paris, France
Duration: 1 Jun 20145 Jun 2014

Conference

ConferenceIDDRG 2014
CountryFrance
CityParis
Period1/06/145/06/14

Fingerprint

Metal forming
Autocorrelation
Byproducts
Materials properties
Actuators
Friction
Sensors
Processing
Costs

Keywords

  • METIS-303708
  • IR-91153

Cite this

Havinga, G. T., van den Boogaard, A. H., Dallinger, F. N., & Hora, P. (2014). Inline control of a strip bending process in mass production. In IDDRG 2014 Conference Proceedings, Innovations for the sheet metal industry (pp. 175-180). Paris.
Havinga, Gosse Tjipke ; van den Boogaard, Antonius H. ; Dallinger, F.N. ; Hora, P. / Inline control of a strip bending process in mass production. IDDRG 2014 Conference Proceedings, Innovations for the sheet metal industry. Paris, 2014. pp. 175-180
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Havinga, GT, van den Boogaard, AH, Dallinger, FN & Hora, P 2014, Inline control of a strip bending process in mass production. in IDDRG 2014 Conference Proceedings, Innovations for the sheet metal industry. Paris, pp. 175-180, IDDRG 2014, Paris, France, 1/06/14.

Inline control of a strip bending process in mass production. / Havinga, Gosse Tjipke; van den Boogaard, Antonius H.; Dallinger, F.N.; Hora, P.

IDDRG 2014 Conference Proceedings, Innovations for the sheet metal industry. Paris, 2014. p. 175-180.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - van den Boogaard, Antonius H.

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AU - Hora, P.

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Y1 - 2014/6/1

N2 - The accuracy of a metal forming process is highly influenced by the variation of the process input, such as variation of friction and material properties. Therefore it may be required to decrease the input variation to meet the desired accuracy. However, this may increase the production costs, since stricter requirements generally come with a higher price tag. Other solutions may be to design the process in such a way that it becomes less sensitive to the input variation, or to implement a control scheme in the production line. Adding sensors to measure the state of the production process and actuators to change the process settings during production allows for a drastic increase of the production accuracy. In this study a numerical comparison is made between different methods to control a thin strip bending process with an over-bending and a back-bending stage. The aim is to implement the method in a mass production line with a production speed of 100 products per minute, which demands for fast measurement, processing and actuation. A discrete control scheme is used, meaning that the process settings can only be adapted in between the process stages. The adaptable control parameter is the amount of back-bending. In the case of the strip bending process, the angle of the measured strip may be used to adapt the angle of the following strip. However, the accuracy of such a control scheme is limited by product-to-product variation. Therefore the force of the over-bending stage is measured and used to construct a predictive model of the process based on measured process data. Hence, the final angle of the flap can be predicted by measuring the force at the first stage of the process. Different factors influence the effectiveness of the control methods: the size and autocorrelation of the input variation, the noise of the measurement system and the predictive ability of the predictive model. A qualitative study on the influence of these factors on different control methods is given in this paper.

AB - The accuracy of a metal forming process is highly influenced by the variation of the process input, such as variation of friction and material properties. Therefore it may be required to decrease the input variation to meet the desired accuracy. However, this may increase the production costs, since stricter requirements generally come with a higher price tag. Other solutions may be to design the process in such a way that it becomes less sensitive to the input variation, or to implement a control scheme in the production line. Adding sensors to measure the state of the production process and actuators to change the process settings during production allows for a drastic increase of the production accuracy. In this study a numerical comparison is made between different methods to control a thin strip bending process with an over-bending and a back-bending stage. The aim is to implement the method in a mass production line with a production speed of 100 products per minute, which demands for fast measurement, processing and actuation. A discrete control scheme is used, meaning that the process settings can only be adapted in between the process stages. The adaptable control parameter is the amount of back-bending. In the case of the strip bending process, the angle of the measured strip may be used to adapt the angle of the following strip. However, the accuracy of such a control scheme is limited by product-to-product variation. Therefore the force of the over-bending stage is measured and used to construct a predictive model of the process based on measured process data. Hence, the final angle of the flap can be predicted by measuring the force at the first stage of the process. Different factors influence the effectiveness of the control methods: the size and autocorrelation of the input variation, the noise of the measurement system and the predictive ability of the predictive model. A qualitative study on the influence of these factors on different control methods is given in this paper.

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KW - IR-91153

M3 - Conference contribution

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EP - 180

BT - IDDRG 2014 Conference Proceedings, Innovations for the sheet metal industry

CY - Paris

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Havinga GT, van den Boogaard AH, Dallinger FN, Hora P. Inline control of a strip bending process in mass production. In IDDRG 2014 Conference Proceedings, Innovations for the sheet metal industry. Paris. 2014. p. 175-180