Towards intelligent setting of process parameters for layered manufacturing

W. L. Wang; J. G. Conley; Y. N. Yan; J. Y.H. Fuh

doi:10.1023/A:1008904108676

Towards intelligent setting of process parameters for layered manufacturing

W. L. Wang^*, J. G. Conley, Y. N. Yan, J. Y.H. Fuh

^*Corresponding author for this work

Managerial Economics, Decision Sciences and Operations

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

The performance of a layered manufacturing (LM) process is determined by the appropriate setting of process parameters. The study of the relationship between performance and process parameters is therefore an important area of LM process planning research. The trend in modern industry is to move from conventional automation to intelligent automation. LM technology is essentially an automated manufacturing technology that is evolving towards an intelligent automation technology. Slicing solid manufacturing (SSM) is a LM technique using paper as the working material and a CO₂ laser as the cutting tool. In this manuscript, a back propagation (BP) learning algorithm of an artificial neural network (ANN) is used to determine appropriate process parameters for the SSM method. Key process parameters affecting accuracy are investigated. Quantitative relationships between the input parameters and output accuracy are established by developing the BP neural network.

Original language	English (US)
Pages (from-to)	65-74
Number of pages	10
Journal	Journal of Intelligent Manufacturing
Volume	11
Issue number	1
DOIs	https://doi.org/10.1023/A:1008904108676
State	Published - 2000

Funding

This project was supported by National Science Foundation of China under the program ``Research on layered manufacturing principle and practice'', No. 59375216. Senior lecturer Y. S. Wong in National University of Singapore helped to correct the English. Dr Wei Feng in Tsinghua University helped to do the experiments.

ASJC Scopus subject areas

Software
Industrial and Manufacturing Engineering
Artificial Intelligence

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title = "Towards intelligent setting of process parameters for layered manufacturing",

abstract = "The performance of a layered manufacturing (LM) process is determined by the appropriate setting of process parameters. The study of the relationship between performance and process parameters is therefore an important area of LM process planning research. The trend in modern industry is to move from conventional automation to intelligent automation. LM technology is essentially an automated manufacturing technology that is evolving towards an intelligent automation technology. Slicing solid manufacturing (SSM) is a LM technique using paper as the working material and a CO2 laser as the cutting tool. In this manuscript, a back propagation (BP) learning algorithm of an artificial neural network (ANN) is used to determine appropriate process parameters for the SSM method. Key process parameters affecting accuracy are investigated. Quantitative relationships between the input parameters and output accuracy are established by developing the BP neural network.",

author = "Wang, {W. L.} and Conley, {J. G.} and Yan, {Y. N.} and Fuh, {J. Y.H.}",

note = "Funding Information: This project was supported by National Science Foundation of China under the program ``Research on layered manufacturing principle and practice'', No. 59375216. Senior lecturer Y. S. Wong in National University of Singapore helped to correct the English. Dr Wei Feng in Tsinghua University helped to do the experiments. Copyright: Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.",

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AU - Yan, Y. N.

AU - Fuh, J. Y.H.

N1 - Funding Information: This project was supported by National Science Foundation of China under the program ``Research on layered manufacturing principle and practice'', No. 59375216. Senior lecturer Y. S. Wong in National University of Singapore helped to correct the English. Dr Wei Feng in Tsinghua University helped to do the experiments. Copyright: Copyright 2004 Elsevier Science B.V., Amsterdam. All rights reserved.

PY - 2000

Y1 - 2000

N2 - The performance of a layered manufacturing (LM) process is determined by the appropriate setting of process parameters. The study of the relationship between performance and process parameters is therefore an important area of LM process planning research. The trend in modern industry is to move from conventional automation to intelligent automation. LM technology is essentially an automated manufacturing technology that is evolving towards an intelligent automation technology. Slicing solid manufacturing (SSM) is a LM technique using paper as the working material and a CO2 laser as the cutting tool. In this manuscript, a back propagation (BP) learning algorithm of an artificial neural network (ANN) is used to determine appropriate process parameters for the SSM method. Key process parameters affecting accuracy are investigated. Quantitative relationships between the input parameters and output accuracy are established by developing the BP neural network.

AB - The performance of a layered manufacturing (LM) process is determined by the appropriate setting of process parameters. The study of the relationship between performance and process parameters is therefore an important area of LM process planning research. The trend in modern industry is to move from conventional automation to intelligent automation. LM technology is essentially an automated manufacturing technology that is evolving towards an intelligent automation technology. Slicing solid manufacturing (SSM) is a LM technique using paper as the working material and a CO2 laser as the cutting tool. In this manuscript, a back propagation (BP) learning algorithm of an artificial neural network (ANN) is used to determine appropriate process parameters for the SSM method. Key process parameters affecting accuracy are investigated. Quantitative relationships between the input parameters and output accuracy are established by developing the BP neural network.

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Towards intelligent setting of process parameters for layered manufacturing

Abstract

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ASJC Scopus subject areas

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