Artificial intelligence : a modern approach to increasing productivity and improving weld quality in TIG welding
Kesse, Martin Appiah (2021-07-29)
Väitöskirja
29.07.2021
Lappeenranta-Lahti University of Technology LUT
Acta Universitatis Lappeenrantaensis
School of Energy Systems
School of Energy Systems, Konetekniikka
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Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-335-687-0
https://urn.fi/URN:ISBN:978-952-335-687-0
Tiivistelmä
Recent years have seen the welding industry facing demands for improved productivity and efficiency together with simultaneous enhancement of the quality of welded structures. The welding industry has met these challenges by developing novel alloys, increasing the level of automation, and expanding the use of dissimilar welding. The utilization of materials with complicated chemical composition necessitates a detailed understanding of material behaviour and how the materials can be combined while ensuring structural integrity. Suitable joining methods for both thick and thin plates are required, as is effective control of joining processes and related technology. A key aspect of welding control is understanding of the dynamics and interactions of the various parameters associated with welding processes and procedures.
Recent developments in artificial intelligence (AI) modelling tools have led to a vision of AI removing the element of human mechanical effort from welding operations. Various AI-based methods have been developed and applied with the aim of attaining good mechanical properties and improving weld quality. These approaches include design of experiment (DoE) techniques and algorithms, conventional regression analysis and the use of computational networks, including neural networks and fuzzy logic. In welding technology, these methods have primarily been used to optimise different welding parameters. Although researchers have found neural networks to be a better approach for optimisation than other available alternatives, it is, however, a black box approach. Consequently, it is difficult to ascertain how the algorithm arrives at a decision, which is knowledge of importance for human welders and future development of welding techniques and technology. The question then becomes: Can an AI model be developed that overcomes this deficiency?
This PhD dissertation aims to contribute to the state-of-the-art in terms of knowledge of the applicability of AI in welding technology by developing an AI framework using an ANFIS and fuzzy deep neural network from which it is possible to ascertain the underlying decision-making logic as an alternative method to predict welding parameters for optimisation of the welding process.
To meet the objective of the work, an in-depth understanding of different welding and optimisation processes is first required. Methodologically, a comprehensive literature review approach and experimental work are used as the basis for suggesting the proposed AI framework. The AI framework for welding technology was designed using a fuzzy deep neural network, which is a combination of fuzzy logic and a deep neural network. The fuzzy logic and deep neural network are incorporated into the framework with a Likert scaling strategy. In normal practice, AI decision-making tools using deep learning techniques require big data from which to learn. For welding applications, obtaining this big data is challenging, because of the laborious and costly nature of welding experiments, and limited experimental data is thus available. The added value of the work in this study is that the AI approach used overcomes the limitation of the big data requirement. Where big data is not available for the algorithm to learn from, the system can mathematically manipulate the small data using its inference engine and extract its own big data from the available small data using the technique of data augmentation.
The AI framework was developed, validated and tested with the TIG welding process to predict weld bead geometry. The results showed a predictive accuracy of 92.59% when compared to results from a real experimental welding data set.
It is expected in the future that this created model will help the welder to bypass trial and error during the selection of welding parameters when welding. This model can be part of the standard welding procedure document to help the welder when performing welding works. This tool will also be useful for industries in the welding sector and can be used for educational purposes.
Recent developments in artificial intelligence (AI) modelling tools have led to a vision of AI removing the element of human mechanical effort from welding operations. Various AI-based methods have been developed and applied with the aim of attaining good mechanical properties and improving weld quality. These approaches include design of experiment (DoE) techniques and algorithms, conventional regression analysis and the use of computational networks, including neural networks and fuzzy logic. In welding technology, these methods have primarily been used to optimise different welding parameters. Although researchers have found neural networks to be a better approach for optimisation than other available alternatives, it is, however, a black box approach. Consequently, it is difficult to ascertain how the algorithm arrives at a decision, which is knowledge of importance for human welders and future development of welding techniques and technology. The question then becomes: Can an AI model be developed that overcomes this deficiency?
This PhD dissertation aims to contribute to the state-of-the-art in terms of knowledge of the applicability of AI in welding technology by developing an AI framework using an ANFIS and fuzzy deep neural network from which it is possible to ascertain the underlying decision-making logic as an alternative method to predict welding parameters for optimisation of the welding process.
To meet the objective of the work, an in-depth understanding of different welding and optimisation processes is first required. Methodologically, a comprehensive literature review approach and experimental work are used as the basis for suggesting the proposed AI framework. The AI framework for welding technology was designed using a fuzzy deep neural network, which is a combination of fuzzy logic and a deep neural network. The fuzzy logic and deep neural network are incorporated into the framework with a Likert scaling strategy. In normal practice, AI decision-making tools using deep learning techniques require big data from which to learn. For welding applications, obtaining this big data is challenging, because of the laborious and costly nature of welding experiments, and limited experimental data is thus available. The added value of the work in this study is that the AI approach used overcomes the limitation of the big data requirement. Where big data is not available for the algorithm to learn from, the system can mathematically manipulate the small data using its inference engine and extract its own big data from the available small data using the technique of data augmentation.
The AI framework was developed, validated and tested with the TIG welding process to predict weld bead geometry. The results showed a predictive accuracy of 92.59% when compared to results from a real experimental welding data set.
It is expected in the future that this created model will help the welder to bypass trial and error during the selection of welding parameters when welding. This model can be part of the standard welding procedure document to help the welder when performing welding works. This tool will also be useful for industries in the welding sector and can be used for educational purposes.
Kokoelmat
- Väitöskirjat [1027]