On adaptive intelligent welding: technique feasibility in weld quality assurance for advanced steels
Gyasi, Emmanuel Afrane (2018-08-28)
Väitöskirja
Gyasi, Emmanuel Afrane
28.08.2018
Lappeenranta University of Technology
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:978-952-335-252-0
https://urn.fi/URN:ISBN:978-952-335-252-0
Tiivistelmä
The welding industry has a need to utilize lightweight steels in welded applications while maintaining acceptable weld quality. This goal can be achieved through effective quality assurance, efficient weld parameter decision-making systems, and careful choice of steel grades. Consequently, there is a need for: i) knowledge enabling manufacturers to select appropriate lightweight material to meet the requirement for reduced structural weight and attain cost reduction in welding manufacturing; and ii) new ways to assure weld quality in real-time, ideally with welding systems that are self-adjusting and able to eliminate welding flaws during welding. Concurrent with this evolution towards lighter structures, the next phase in modern manufacturing, Industry 4.0, is also driving welding industries to digitization of manufacturing using advanced welding technologies. The Industry 4.0 concept aims to harness emerging digital technologies for enhanced quality, connectivity, productivity, and environmental and economic gain via improved reliability in manufacturing and production. Greater integration of evolving technologies in automation, digitization and artificial intelligence (AI) are required in welding manufacturing to realize a sustainable future industry.
The objectives of this thesis are to provide an overview of weld integrity aspects of advanced steels, that is, high strength steels (HSS) and ultra high strength steels (UHSS), and through experimental study to explore the applicability of adaptive intelligent robotic gas metal arc welding (GMAW) of UHSS for structural applications. Additionally, the study aims to utilize the findings from experimental work in the design of a new weld quality assurance model based on adaptive intelligence. This aspect is grounded on the concept of Industry 4.0 and the “big data” involved in systems integration processes (automation, robotics, sensory, monitoring and artificial intelligent systems).
The thesis is an article-based study comprising the outcome of five research articles. Research methods used include both review of previous work and experimental study. Review of the weldability of HSS and UHSS showed that these steels have high susceptibility to heat affected zone (HAZ) softening when they are welded at elevated temperatures and with imprecisely controlled heat input. Additionally, a risk of cold cracking and a propensity to weld integrity problems arises when the steels are welded with filler materials having high hydrogen content and varying strength. The experimental studies indicated the feasibility of effectively welding these steels with accurately modelled and controlled welding heat conditions. The welding of direct-quenched UHSS S960QC material in fillet joint configurations in different welding positions with an adaptive intelligence welding system demonstrated the possibility of real-time process monitoring, process outcome prediction, and control of welding parameters and variables in robot welding with the aim of achieving desired weld quality. The behaviour of welding parameter control and adaptation and their effects correlate with the consequential changes in the macrostructure, mechanical properties and microstructure of the weldments.
A new weld quality assurance model based on adaptive intelligence systems is examined and presented with detailed steps. The model aims to assist welding companies, large and small and medium-sized enterprises (SMEs), in their decision-making, and to contribute to efforts to integrate and advance the implementation of adaptive welding systems in manufacturing and production networks. The new weld quality assurance model facilitates digitization of weld quality and quality assurance processes to improve weld quality, eliminate or reduce already at the commissioning stage weldments with defects, maintain a digital history of the welding operation for optimization and development purposes, reduce rework, trace weld defects digitally and in real-time, and define and approve welding procedure specification (WPS) in digital formats. In addition to the fundamental aim of weld quality assurance, additional benefits for welding companies include opportunities to network with other robot cells in other companies and firms and synchronize adaptive welding systems on a global level for common welding production throughput.
The objectives of this thesis are to provide an overview of weld integrity aspects of advanced steels, that is, high strength steels (HSS) and ultra high strength steels (UHSS), and through experimental study to explore the applicability of adaptive intelligent robotic gas metal arc welding (GMAW) of UHSS for structural applications. Additionally, the study aims to utilize the findings from experimental work in the design of a new weld quality assurance model based on adaptive intelligence. This aspect is grounded on the concept of Industry 4.0 and the “big data” involved in systems integration processes (automation, robotics, sensory, monitoring and artificial intelligent systems).
The thesis is an article-based study comprising the outcome of five research articles. Research methods used include both review of previous work and experimental study. Review of the weldability of HSS and UHSS showed that these steels have high susceptibility to heat affected zone (HAZ) softening when they are welded at elevated temperatures and with imprecisely controlled heat input. Additionally, a risk of cold cracking and a propensity to weld integrity problems arises when the steels are welded with filler materials having high hydrogen content and varying strength. The experimental studies indicated the feasibility of effectively welding these steels with accurately modelled and controlled welding heat conditions. The welding of direct-quenched UHSS S960QC material in fillet joint configurations in different welding positions with an adaptive intelligence welding system demonstrated the possibility of real-time process monitoring, process outcome prediction, and control of welding parameters and variables in robot welding with the aim of achieving desired weld quality. The behaviour of welding parameter control and adaptation and their effects correlate with the consequential changes in the macrostructure, mechanical properties and microstructure of the weldments.
A new weld quality assurance model based on adaptive intelligence systems is examined and presented with detailed steps. The model aims to assist welding companies, large and small and medium-sized enterprises (SMEs), in their decision-making, and to contribute to efforts to integrate and advance the implementation of adaptive welding systems in manufacturing and production networks. The new weld quality assurance model facilitates digitization of weld quality and quality assurance processes to improve weld quality, eliminate or reduce already at the commissioning stage weldments with defects, maintain a digital history of the welding operation for optimization and development purposes, reduce rework, trace weld defects digitally and in real-time, and define and approve welding procedure specification (WPS) in digital formats. In addition to the fundamental aim of weld quality assurance, additional benefits for welding companies include opportunities to network with other robot cells in other companies and firms and synchronize adaptive welding systems on a global level for common welding production throughput.
Kokoelmat
- Väitöskirjat [1037]