Torayev, Agajan
(2024)
A holistic methodology for manufacturing systems configuration.
PhD thesis, University of Nottingham.
Abstract
The manufacturing sector is undergoing rapid transformations driven by global economic factors, technological advancements, and fluctuating market demands. These dynamics necessitate continuous innovation and adaptability in manufacturing systems to maintain competitiveness.
Therefore, this research addresses the manufacturing systems configuration (MSC) problem, aiming to develop a methodology to make manufacturing systems resilient and adaptable that balances resource management, production efficiency, and costs.
Despite the critical nature of the MSC problem, existing solutions are fragmented and suffer from significant limitations, including underutilisation of data models, software integration issues, and the inadequacy of traditional optimisation and decision-making methods in dealing with uncertainties and multiple objectives.
Therefore, this research formulates the problem as ``developing a holistic solution for addressing the MSC problem for adapting manufacturing systems to rapidly changing manufacturing requirements" to address these gaps. In this context, a holistic solution synergistically combines data modelling, software integration, adaptive optimisation and decision-making algorithms.
The research objectives include the development of adaptable data models that encapsulate the complexities of manufacturing systems, plug-and-produce manufacturing software solutions that address system scalability and adaptability, and adaptive optimisation algorithms capable of navigating complex solution spaces.
The research employs a multi-staged validation approach, initially testing the proposed methodologies in two distinct manufacturing processes with unique challenges: sorting cylinders and bin-picking parts of industrial pipe couplers. These processes serve as a comprehensive testing ground for the proposed solutions. Three research hypotheses were sequentially assessed, focusing on the adaptability of object-oriented data models, the effectiveness of manufacturing apps in achieving interoperability, and the efficiency of optimisation and decision-making algorithms in managing multiple objectives and uncertainties. Each hypothesis was successfully validated, confirming the research contributions.
Subsequently, empirical validation was extended to real-world industrial settings, focusing on aerospace and custom product manufacturing sectors. In the aerospace sector, the task was to find optimal manufacturing system configurations for changing and multiple conflicting manufacturing costs for assembling a generic hinged product. In the custom product manufacturing sector, the task involved planning a machining process that required balancing multiple manufacturing costs. These validations substantiate the research hypotheses and demonstrate the proposed methodology's generalisability and adaptability.
By developing a holistic approach, this research contributes significantly to the field. It addresses the limitations of existing fragmented solutions and provides a robust, adaptable, and holistic framework for manufacturing systems. The research has practical implications for manufacturing entities aiming to be agile and responsive to market changes, fulfilling the main aim of developing a holistic solution to the MSC problem.
| Item Type: |
Thesis (University of Nottingham only)
(PhD)
|
| Supervisors: |
Ratchev, Svetan
MartÃnez-Arellano, Giovanna
Chaplin, Jack C.
Sanderson, David
Popov, Atanas |
| Keywords: |
digital manufacturing, industry 4.0, machine learning, reinforcement learning, industrial robotics, optimisation, decision-making |
| Subjects: |
T Technology > TS Manufactures |
| Faculties/Schools: |
UK Campuses > Faculty of Engineering > Department of Mechanical, Materials and Manufacturing Engineering |
| Item ID: |
77296 |
| Depositing User: |
Torayev, Agajan
|
| Date Deposited: |
19 Apr 2024 08:23 |
| Last Modified: |
19 Apr 2024 08:23 |
| URI: |
https://eprints.nottingham.ac.uk/id/eprint/77296 |
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