A Proposed Performance Prediction Approach for Manufacturing Processes using ANNs
this paper aims to provide an approach to predict the
performance of the product produced after multi-stages of
manufacturing processes, as well as the assembly. Such approach
aims to control and subsequently identify the relationship between
the process inputs and outputs so that a process engineer can more
accurately predict how the process output shall perform based on the
system inputs. The approach is guided by a six-sigma methodology to
obtain improved performance.
In this paper a case study of the manufacture of a hermetic
reciprocating compressor is presented. The application of artificial
neural networks (ANNs) technique is introduced to improve
performance prediction within this manufacturing environment. The
results demonstrate that the approach predicts accurately and
effectively.
[1] D.S. Chang, and S.-T. Jiang, "Assessing quality performance based on
the on-line sensor measurements using neural networks," Computers &
Industrial Engineering, vol. 42: pp. 417-424, 2002.
[2] M. T. Kuo, Y. H. Chung, and W.C. Lo, "A study of the effects of
process parameters for injection molding on surface quality of optical
lenses," journal of materials processing technology, vol. 209: pp. 3469
- 3477, 2009.
[3] M. Paliwal, and U.A. Kumar, "Neural networks and statistical
techniques: A review of applications," Expert Systems with
Applications, vol. 36: p. 2-17, 2009.
[4] S. M. Bajimaya, S. Park, and G.-N. Wang, "Predicting extrusion process
parameters using neural networks," International Journal of
Mechanical Systems Science and Engineering, vol. 1, no.3, 2008.
[5] Y.K. Yousif, K.M. Daws, and B.I. Kazem, "Prediction of friction stir
Welding characteristic using neural network," Jordan Journal of
Mechanical and Industrial Engineering, vol. 2, no.3, 2008.
[6] K. Kadirgama, and K.A. Abo-El-Hossein, "Prediction of cutting force
model by using neural network," Journal of Applied Sciences, vol. 6,
no. 1, pp. 31-34, 2006.
[7] A. M. Zain, H. Haron, and S. Sharif, "Prediction of surface roughness in
the end milling machining using Artificial Neural Network," Expert
Systems with Applications, vol. 37, pp. 1755-1768, 2010.
[8] W. C. Chen, G.-L., Fu, P.-H. Tai, and W.-J. Deng, "Process parameter
optimization for MIMO plastic injection molding via soft computing,"
Expert Systems with Applications, vol. 36, pp. 114-1122, 2009.
[9] W.-C. Chen, P.-H. Tai, M.-W. Wang, W.-J. Deng and C.-T. Chen, "A
neural network-based approach for dynamic quality prediction in a
plastic injection molding process," Expert Systems with Applications,
vol. 35, pp. 843-849, 2008.
[10] C. Cho, D. D. Kim, J. Kim, D. Lim, and S. Cho, "Early prediction of
product performance and yield via technology benchmark," IEEE
"Custom Intergrated Circuits Conference (CICC)", pp. 205-208, 2008.
[11] Z. Zhang, R. Peng, and N. Chen, "Artificial neural network prediction
of the band gap and melting point of binary and ternary compound
semiconductors," Material Science Engineering, vol. B54, pp. 49-52,
1998.
[12] A. B. Johnston, L. B. Maguire, and T. M. McGinnity, "Downstream
performance prediction for a manufacturing system using neural
networks and six-sigma improvement techniques," Robotics and
Computer-Integrated Manufacturing, Vol. 25, Iss. 3, pp. 513-521,
2009.
[13] J. Rigola, C. D. Pe'rez-Segarra, and A. Oliva, "Parametric studies on
hermetic reciprocating compressors," International Journal of
Refrigeration, Vol. 28, pp. 253-266, 2005.
[14] Y. Yu, L. Chen, F. Sun, and C. Wu, "Neural-network based analysis and
prediction of a compressor-s characteristic performance map," Applied
Energy, vol. 84, pp. 48-55, 2007.
[15] K. Ghorbanian, and M. Gholamrezaei, "An artificial neural network
approach to compressor performance prediction," Applied Energy, vol.
86, pp. 1210-1221, 2009.
[16] T. Turunen-Saaresti, P. Roytta, J. Honkatukia, and J. Backman,
"Predicting off-design range and performance of refrigeration cycle with
two-stage centrifugal compressor and flash intercooler," international
journal of refrigeration, vol. 33, pp. 1152-1160, 2010.
[17] M. L. Huang, and Y. H. Hung, "Combining radial basis function neural
network and genetic algorithm to improve HDD driver IC chip scale
package assembly yield," Expert Systems with Applications, vol. 34, pp.
588-595, 2008.
[18] D. C. S. Summers, Six Sigma Basic Tools and Technique. 1st ed.
Prentice Hall, 2007.
[19] S. H. Park, Six Sigma for quality and productivity promotion. Tokyo:
Asian Productivity Organization, 2003.
[20] M. J. Harry, The Vision of Six Sigma. in Tri Star Publishing. Arizona:
1998.
[21] A. D. Sleeper, Design for Six Sigma Statistics. McGraw-Hill, 2006.
[22] M. P. Groover, Automation production Systems, and Computer-
Integrated Manufacturing. ed. 3, Pearson, 2008.
[1] D.S. Chang, and S.-T. Jiang, "Assessing quality performance based on
the on-line sensor measurements using neural networks," Computers &
Industrial Engineering, vol. 42: pp. 417-424, 2002.
[2] M. T. Kuo, Y. H. Chung, and W.C. Lo, "A study of the effects of
process parameters for injection molding on surface quality of optical
lenses," journal of materials processing technology, vol. 209: pp. 3469
- 3477, 2009.
[3] M. Paliwal, and U.A. Kumar, "Neural networks and statistical
techniques: A review of applications," Expert Systems with
Applications, vol. 36: p. 2-17, 2009.
[4] S. M. Bajimaya, S. Park, and G.-N. Wang, "Predicting extrusion process
parameters using neural networks," International Journal of
Mechanical Systems Science and Engineering, vol. 1, no.3, 2008.
[5] Y.K. Yousif, K.M. Daws, and B.I. Kazem, "Prediction of friction stir
Welding characteristic using neural network," Jordan Journal of
Mechanical and Industrial Engineering, vol. 2, no.3, 2008.
[6] K. Kadirgama, and K.A. Abo-El-Hossein, "Prediction of cutting force
model by using neural network," Journal of Applied Sciences, vol. 6,
no. 1, pp. 31-34, 2006.
[7] A. M. Zain, H. Haron, and S. Sharif, "Prediction of surface roughness in
the end milling machining using Artificial Neural Network," Expert
Systems with Applications, vol. 37, pp. 1755-1768, 2010.
[8] W. C. Chen, G.-L., Fu, P.-H. Tai, and W.-J. Deng, "Process parameter
optimization for MIMO plastic injection molding via soft computing,"
Expert Systems with Applications, vol. 36, pp. 114-1122, 2009.
[9] W.-C. Chen, P.-H. Tai, M.-W. Wang, W.-J. Deng and C.-T. Chen, "A
neural network-based approach for dynamic quality prediction in a
plastic injection molding process," Expert Systems with Applications,
vol. 35, pp. 843-849, 2008.
[10] C. Cho, D. D. Kim, J. Kim, D. Lim, and S. Cho, "Early prediction of
product performance and yield via technology benchmark," IEEE
"Custom Intergrated Circuits Conference (CICC)", pp. 205-208, 2008.
[11] Z. Zhang, R. Peng, and N. Chen, "Artificial neural network prediction
of the band gap and melting point of binary and ternary compound
semiconductors," Material Science Engineering, vol. B54, pp. 49-52,
1998.
[12] A. B. Johnston, L. B. Maguire, and T. M. McGinnity, "Downstream
performance prediction for a manufacturing system using neural
networks and six-sigma improvement techniques," Robotics and
Computer-Integrated Manufacturing, Vol. 25, Iss. 3, pp. 513-521,
2009.
[13] J. Rigola, C. D. Pe'rez-Segarra, and A. Oliva, "Parametric studies on
hermetic reciprocating compressors," International Journal of
Refrigeration, Vol. 28, pp. 253-266, 2005.
[14] Y. Yu, L. Chen, F. Sun, and C. Wu, "Neural-network based analysis and
prediction of a compressor-s characteristic performance map," Applied
Energy, vol. 84, pp. 48-55, 2007.
[15] K. Ghorbanian, and M. Gholamrezaei, "An artificial neural network
approach to compressor performance prediction," Applied Energy, vol.
86, pp. 1210-1221, 2009.
[16] T. Turunen-Saaresti, P. Roytta, J. Honkatukia, and J. Backman,
"Predicting off-design range and performance of refrigeration cycle with
two-stage centrifugal compressor and flash intercooler," international
journal of refrigeration, vol. 33, pp. 1152-1160, 2010.
[17] M. L. Huang, and Y. H. Hung, "Combining radial basis function neural
network and genetic algorithm to improve HDD driver IC chip scale
package assembly yield," Expert Systems with Applications, vol. 34, pp.
588-595, 2008.
[18] D. C. S. Summers, Six Sigma Basic Tools and Technique. 1st ed.
Prentice Hall, 2007.
[19] S. H. Park, Six Sigma for quality and productivity promotion. Tokyo:
Asian Productivity Organization, 2003.
[20] M. J. Harry, The Vision of Six Sigma. in Tri Star Publishing. Arizona:
1998.
[21] A. D. Sleeper, Design for Six Sigma Statistics. McGraw-Hill, 2006.
[22] M. P. Groover, Automation production Systems, and Computer-
Integrated Manufacturing. ed. 3, Pearson, 2008.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:59292", author = "M. S. Abdelwahed and M. A. El-Baz and T. T. El-Midany", title = "A Proposed Performance Prediction Approach for Manufacturing Processes using ANNs", abstract = "this paper aims to provide an approach to predict the
performance of the product produced after multi-stages of
manufacturing processes, as well as the assembly. Such approach
aims to control and subsequently identify the relationship between
the process inputs and outputs so that a process engineer can more
accurately predict how the process output shall perform based on the
system inputs. The approach is guided by a six-sigma methodology to
obtain improved performance.
In this paper a case study of the manufacture of a hermetic
reciprocating compressor is presented. The application of artificial
neural networks (ANNs) technique is introduced to improve
performance prediction within this manufacturing environment. The
results demonstrate that the approach predicts accurately and
effectively.", keywords = "Artificial neural networks, Reciprocating compressor
manufacturing, Performance prediction, Quality improvement", volume = "6", number = "1", pages = "226-6", }
