A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing

Ahmed Abukar; Mozafar Saadat

doi:10.3233/ATDE220601

A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing

Ahmed Abukar^*, Mozafar Saadat

^*Corresponding author for this work

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Downloads (Pure)

Abstract

Currently, expert knowledge is sometimes the only way to predict the duration of the manufacturing process in relation to conformity. While expert knowledge is difficult to come by and often limited to experience. This paper aims to predict the duration of the manufacturing process by utilizing machine learning and big data. This paper utilizes a real industrial case with the use of neural network regression model that aims to expand the body of knowledge available in the area of predictive manufacturing research. The result of the model suggests the neural network regression model can result in a feasible outcome, while in the meantime overfitting did occur. Mean-squared error, relative squared error, relative absolute error and the correlation coefficient was computed. The paper also addresses the limitations and limited scope of findings and makes suggesting moving forwards.

Original language	English
Title of host publication	Advances in Manufacturing Technology XXXV
Subtitle of host publication	Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research, 6-8 September 2022, University of Derby, UK
Editors	Mahmoud Shafik, Keith Case
Publisher	IOS Press BV
Pages	261-268
Number of pages	8
ISBN (Electronic)	9781643683317
ISBN (Print)	9781643683300
DOIs	https://doi.org/10.3233/ATDE220601
Publication status	Published - 8 Nov 2022
Event	19th International Conference on Manufacturing Research, ICMR 2022 - Derby, United Kingdom Duration: 6 Sept 2022 → 8 Sept 2022

Publication series

Name	Advances in Transdisciplinary Engineering
Volume	25
ISSN (Print)	2352-751X
ISSN (Electronic)	2352-7528

Conference

Conference	19th International Conference on Manufacturing Research, ICMR 2022
Country/Territory	United Kingdom
City	Derby
Period	6/09/22 → 8/09/22

Bibliographical note

Funding Information:
This project is supported by an Italian company, Gruppo Fabbricazione Meccanica (GFM) SrlGroup. They provided data for this research as raw data; they work with many small independent machine shops. For each order that GFM receives from customers, a decision must be made on how these orders are allocated to these workshops. A very important task of GFM is to decide how to distribute these tasks in order to achieve the best performance indicators such as shortest lead time, cost and quality. The dataset used in this study contains information on 100,000 orders, including requested delivery date, actual production start date, machine code, product code, work location, etc. This data includes all GFM orders from 2015 to 2017.

Publisher Copyright:
© 2022 The authors and IOS Press.

Keywords

Machine Learning
Neural Network
Predictive Manufacturing
Predictive Scheduling

ASJC Scopus subject areas

Computer Science Applications
Industrial and Manufacturing Engineering
Software
Algebra and Number Theory
Strategy and Management

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Cite this

Abukar, A., & Saadat, M. (2022). A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing. In M. Shafik, & K. Case (Eds.), Advances in Manufacturing Technology XXXV : Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research, 6-8 September 2022, University of Derby, UK (pp. 261-268). (Advances in Transdisciplinary Engineering; Vol. 25). IOS Press BV. https://doi.org/10.3233/ATDE220601

Abukar, Ahmed ; Saadat, Mozafar. / A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing. Advances in Manufacturing Technology XXXV : Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research, 6-8 September 2022, University of Derby, UK. editor / Mahmoud Shafik ; Keith Case. IOS Press BV, 2022. pp. 261-268 (Advances in Transdisciplinary Engineering).

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title = "A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing",

abstract = "Currently, expert knowledge is sometimes the only way to predict the duration of the manufacturing process in relation to conformity. While expert knowledge is difficult to come by and often limited to experience. This paper aims to predict the duration of the manufacturing process by utilizing machine learning and big data. This paper utilizes a real industrial case with the use of neural network regression model that aims to expand the body of knowledge available in the area of predictive manufacturing research. The result of the model suggests the neural network regression model can result in a feasible outcome, while in the meantime overfitting did occur. Mean-squared error, relative squared error, relative absolute error and the correlation coefficient was computed. The paper also addresses the limitations and limited scope of findings and makes suggesting moving forwards.",

keywords = "Machine Learning, Neural Network, Predictive Manufacturing, Predictive Scheduling",

author = "Ahmed Abukar and Mozafar Saadat",

note = "Funding Information: This project is supported by an Italian company, Gruppo Fabbricazione Meccanica (GFM) SrlGroup. They provided data for this research as raw data; they work with many small independent machine shops. For each order that GFM receives from customers, a decision must be made on how these orders are allocated to these workshops. A very important task of GFM is to decide how to distribute these tasks in order to achieve the best performance indicators such as shortest lead time, cost and quality. The dataset used in this study contains information on 100,000 orders, including requested delivery date, actual production start date, machine code, product code, work location, etc. This data includes all GFM orders from 2015 to 2017. Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.; 19th International Conference on Manufacturing Research, ICMR 2022 ; Conference date: 06-09-2022 Through 08-09-2022",

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Abukar, A & Saadat, M 2022, A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing. in M Shafik & K Case (eds), Advances in Manufacturing Technology XXXV : Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research, 6-8 September 2022, University of Derby, UK. Advances in Transdisciplinary Engineering, vol. 25, IOS Press BV, pp. 261-268, 19th International Conference on Manufacturing Research, ICMR 2022, Derby, United Kingdom, 6/09/22. https://doi.org/10.3233/ATDE220601

A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing. / Abukar, Ahmed; Saadat, Mozafar.
Advances in Manufacturing Technology XXXV : Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research, 6-8 September 2022, University of Derby, UK. ed. / Mahmoud Shafik; Keith Case. IOS Press BV, 2022. p. 261-268 (Advances in Transdisciplinary Engineering; Vol. 25).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing

AU - Abukar, Ahmed

AU - Saadat, Mozafar

N1 - Funding Information: This project is supported by an Italian company, Gruppo Fabbricazione Meccanica (GFM) SrlGroup. They provided data for this research as raw data; they work with many small independent machine shops. For each order that GFM receives from customers, a decision must be made on how these orders are allocated to these workshops. A very important task of GFM is to decide how to distribute these tasks in order to achieve the best performance indicators such as shortest lead time, cost and quality. The dataset used in this study contains information on 100,000 orders, including requested delivery date, actual production start date, machine code, product code, work location, etc. This data includes all GFM orders from 2015 to 2017. Publisher Copyright: © 2022 The authors and IOS Press.

PY - 2022/11/8

Y1 - 2022/11/8

N2 - Currently, expert knowledge is sometimes the only way to predict the duration of the manufacturing process in relation to conformity. While expert knowledge is difficult to come by and often limited to experience. This paper aims to predict the duration of the manufacturing process by utilizing machine learning and big data. This paper utilizes a real industrial case with the use of neural network regression model that aims to expand the body of knowledge available in the area of predictive manufacturing research. The result of the model suggests the neural network regression model can result in a feasible outcome, while in the meantime overfitting did occur. Mean-squared error, relative squared error, relative absolute error and the correlation coefficient was computed. The paper also addresses the limitations and limited scope of findings and makes suggesting moving forwards.

AB - Currently, expert knowledge is sometimes the only way to predict the duration of the manufacturing process in relation to conformity. While expert knowledge is difficult to come by and often limited to experience. This paper aims to predict the duration of the manufacturing process by utilizing machine learning and big data. This paper utilizes a real industrial case with the use of neural network regression model that aims to expand the body of knowledge available in the area of predictive manufacturing research. The result of the model suggests the neural network regression model can result in a feasible outcome, while in the meantime overfitting did occur. Mean-squared error, relative squared error, relative absolute error and the correlation coefficient was computed. The paper also addresses the limitations and limited scope of findings and makes suggesting moving forwards.

KW - Machine Learning

KW - Neural Network

KW - Predictive Manufacturing

KW - Predictive Scheduling

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M3 - Conference contribution

AN - SCOPUS:85145612159

SN - 9781643683300

T3 - Advances in Transdisciplinary Engineering

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EP - 268

BT - Advances in Manufacturing Technology XXXV

A2 - Shafik, Mahmoud

A2 - Case, Keith

PB - IOS Press BV

T2 - 19th International Conference on Manufacturing Research, ICMR 2022

Y2 - 6 September 2022 through 8 September 2022

ER -

Abukar A, Saadat M. A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing. In Shafik M, Case K, editors, Advances in Manufacturing Technology XXXV : Proceedings of the 19th International Conference on Manufacturing Research, Incorporating the 36th National Conference on Manufacturing Research, 6-8 September 2022, University of Derby, UK. IOS Press BV. 2022. p. 261-268. (Advances in Transdisciplinary Engineering). doi: 10.3233/ATDE220601

A Neural Network Approach to Predict Duration in Conformity for Predictive Manufacturing

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this