Subject Area
Mechanical Power Engineering
Article Type
Original Study
Abstract
This paper aims to provide an approach to predict the performance of parts produced after multi-stages manufacturing processes, as well as 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 will perform based on the system inputs. The work 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. Each of manufacturing stages is separate and affects to the functionality of the end product. 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
performance prediction; Six Sigma; Artificial Neural Networks; quality control; Hermetic reciprocating compressor manufacturing
Recommended Citation
El-Midany, T.; El-Baz, M.; and Abd-Elwahed, M.
(2020)
"A Proposed Performance Prediction Approach for Manufacturing Process Using Artifical Neural Networks.,"
Mansoura Engineering Journal: Vol. 36
:
Iss.
4
, Article 11.
Available at:
https://doi.org/10.21608/bfemu.2020.122176
