A Proposed Performance Prediction Approach for Manufacturing Processes using ANNs
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
A Proposed Performance Prediction Approach for Manufacturing Processes using ANNs

Authors: M. S. Abdelwahed, M. A. El-Baz, T. T. El-Midany

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

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074847

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1785

References:


[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.