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122176

A Proposed Performance Prediction Approach for Manufacturing Process Using Artifical Neural Networks.

Article

Last updated: 22 Jan 2023

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Tags

Mechanical Power Engineering

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. 

DOI

10.21608/bfemu.2020.122176

Keywords

performance prediction, Six Sigma, Artificial Neural Networks, quality control, Hermetic reciprocating compressor manufacturing

Authors

First Name

T.

Last Name

El-Midany

MiddleName

T.

Affiliation

Professor of Mechanical Power Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.

Email

-

City

Mansoura

Orcid

-

First Name

M.

Last Name

El-Baz

MiddleName

A.

Affiliation

Department of Mechanical Power Engineering , Faculty of Engineering , Zagazig University , Zagazig , Egypt

Email

-

City

Zagazig

Orcid

-

First Name

M.

Last Name

Abd-Elwahed

MiddleName

S.

Affiliation

Specialized Studies Academy., Workers University., Egypt.

Email

-

City

-

Orcid

-

Volume

36

Article Issue

4

Related Issue

17858

Issue Date

2011-12-01

Receive Date

2020-11-08

Publish Date

2020-11-08

Page Start

39

Page End

49

Print ISSN

1110-0923

Online ISSN

2735-4202

Article File

BFEMU_Volume 36_Issue 4_Pages 39-49.pdf

PDF . 16.8MB

Link

https://bfemu.journals.ekb.eg/article_122176.html

Detail API

https://bfemu.journals.ekb.eg/service?article_code=122176

Order

11

Type

Research Studies

Type Code

1,205

Publication Type

Journal

Publication Title

MEJ. Mansoura Engineering Journal

Publication Link

https://bfemu.journals.ekb.eg/

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-

Details

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Article

Created At

22 Jan 2023