Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature

Cai, Wei; Wang, Jianzhuang; Jiang, Ping; Cao, Longchao; Mi, Gaoyang; Zhou, Qi

doi:10.1016/j.jmsy.2020.07.021

Cited by 113 publications

(25 citation statements)

References 248 publications

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“…A particularly interesting feature is the combination of multiple sensor coaxial or paraxially to collect multiple welding signals to enhance the interpretation of welding quality. Fusing several source data has been challenging to date but research has shown that data can be simultaneously used to complement each other and to clarify specific welding situations [60]. By merging the high-speed photography and image processing technology, for instance, laser-induced metallic plasma and keyhole size were quantified which in turn indicates information about the weld depth.…”

Section: A Hybrid Data Collectionmentioning

confidence: 99%

Development of an Intelligent Quality Management System for Micro Laser Welding: An Innovative Framework and Its Implementation Perspectives

Hoz

Cheng

2021

Machines

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Laser micro-welding manufacturers face substantial challenges in verifying weldment quality, as the industry and applications are requiring increasingly the miniaturization and compactness of products. The problem is compounded by new stringent demands for personalized products at competitive, low costs and the highest quality levels. High-pressure equipment manufacturers, in particular, rely on ISO 3834:2021 to assure and demonstrate best welding practices but also to manage risks associated with liability issues. ISO 3834:2021, like all conventional quality management systems, offers a one-dimensional, quasi-static overview of welding quality that may fail to deal with these new challenges and underlying complexities required to deal effectively with process variability. This paper presents a framework for welding companies to integrate horizontally their suppliers and customers with their processes and products, which are also integrated vertically in the context of Smart Manufacturing or Industry 4.0. It is focused on the development of a smart quality management system for intelligent digitization of all company manufacturing and business processes. Furthermore, an innovative data-based welding quality management framework is described for laser micro-welding applications and their implementation perspectives. The research is driven by an inductive methodology and based on a seamless integration of engineering-oriented heuristic and empirical approaches that is appropriate for intelligent and autonomous quality management, given the lack of research in this niche, but increasingly important topic area.

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Section: A Hybrid Data Collectionmentioning

confidence: 99%

Development of an Intelligent Quality Management System for Micro Laser Welding: An Innovative Framework and Its Implementation Perspectives

Hoz

Cheng

2021

Machines

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“…The welding quality can be influenced by various factors such as the internal defects of materials and the complex manufacturing environment. Therefore, monitoring of the laser welding process is essential for making high quality production [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Output Monitoring of High-Speed Laser Welding State Based on Deep Learning

Xue

Chang

2021

Sensors

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In order to ensure the production quality of high-speed laser welding, it is necessary to simultaneously monitor multiple state properties. Monitoring methods combining vision sensing and deep learning models are popular but most models used can only make predictions on single welding state property. In this contribution, we propose a multi-output model based on a lightweight convolutional neural network (CNN) architecture and introduce the particle swarm optimization (PSO) technique to optimize the loss function of the model, to simultaneously monitor multiple state properties of high-speed laser welding of AISI 304 austenitic stainless steel. High-speed imaging is performed to capture images of the melt pool and the dataset is built. Test results of different models show that the proposed model can achieve monitoring of multiple welding state properties accurately and efficiently. In addition, we make an interpretation and discussion on the prediction of the model through a visualization method, which can help to deepen our understanding of the relationship between the melt pool appearance and welding state. The proposed method can not only be applied to the monitoring of high-speed laser welding but also has the potential to be used in other procedures of welding state monitoring.

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“…Laser welding is considered a preferable method to achieve the joining of medium plates, given its advantages of high power density, low heat input, deep penetration, narrow heat affected zone, and excellent mechanical properties [3,4]. However, some weld defects such as underfill, crack, and porosity easily occur in laser welding [5,6].…”

Section: Introductionmentioning

confidence: 99%

Penetration Depth Prediction of Infinity Shaped Laser Scanning Welding Based on Latin Hypercube Sampling and the Neuroevolution of Augmenting Topologies

2021

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This paper builds an infinity shaped (“∞”-shaped) laser scanning welding test platform based on a self-developed motion controller and galvanometer scanner control gateway, takes the autogenous bead-on-plate welding of 304SS with 3 mm thick specimens as the experimental objects, designs the experimental parameters by the Latin hypercube sampling method for obtaining different penetration depth welded joints, and presents a methodology based on the neuroevolution of augmenting topologies for predicting the penetration depth of “∞”-shaped laser scanning welding. Laser power, welding speed, scanning frequency, and scanning amplitude are set as the input parameters of the model, and welding depth (WD) as the output parameter of the model. The model can accurately reflect the nonlinear relationship between the main welding parameters and WD by validation. Moreover, the normalized root mean square error (NRMSE) of the welding depth is about 6.2%. On the whole, the proposed methodology and model can be employed for guiding the actual work in the main process parameters’ preliminary selection and lay the foundation for the study of penetration morphology control of “∞”-shaped laser scanning welding.

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Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature

Cited by 113 publications

References 248 publications

Development of an Intelligent Quality Management System for Micro Laser Welding: An Innovative Framework and Its Implementation Perspectives

Development of an Intelligent Quality Management System for Micro Laser Welding: An Innovative Framework and Its Implementation Perspectives

Multi-Output Monitoring of High-Speed Laser Welding State Based on Deep Learning

Penetration Depth Prediction of Infinity Shaped Laser Scanning Welding Based on Latin Hypercube Sampling and the Neuroevolution of Augmenting Topologies

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