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

With the development of powder bed fusion (PBF) additive manufacturing technique for functional parts production, process monitoring and diagnosis is highly demanded to ensure its process reliability and repeatability. An off-axis vision monitoring method using high-speed camera is proposed in this paper. An optical filter with 350 nm–800 nm cut-off was used to enhance the image contrast between the plume and the melt pool. A new image processing method was designed to extract features from the melt pool, plume and spatters, respectively. Kalman filter tracking was used to pinpoint the exact melt pool position, and image segmentation algorithm was developed to segment the melt pool, plume and spatters from each other; a new tracking method was utilized to remove the spatters generated in the previous frame. After image processing, the features of melt pool intensity, plume area, plume orientation, spatter number, spatter area, spatter orientation and spatter velocity were extracted and their correlations with the scanning quality were investigated. The results indicated that these features were potential indicators for scanning quality assessment. The proposed method could be used to further study the characteristics of plume and spatter and to explore the diagnosis performance based on the fusion of melt pool, plume and spatter information. It provides a promising means for in-situ monitoring and control of PBF process.

In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches

Welding monitoring technology based on machine vision has been widely researched in academic and industry, especially in the background of Industry 4.0, in that it can contribute to welding quality and productivity improvement. This paper outlines the technical points of welding status monitoring based on machine vision, including hardware and software. First of all, in the hardware part, the active and passive vision systems are briefly introduced, as well as the key steps in experimental deployment, such as the configuration of optical sensors and optical filters based on different detection objects. Secondly, some related image processing techniques in welding monitoring are also comprehensively reviewed. Additionally, the observed objects and their morphological characteristics of vision-based welding process monitoring are enumerated. On this basis, a series of intelligent models as well as optimization methods for recognition and classification in visual monitoring are considered in detail. Finally, potential research challenges and open research issues of welding visual monitoring are discussed to present an insight into future research opportunities. The main purpose of this paper is to provide a reference source for the researchers involved in intelligent robot welding.

Research and prospect of welding monitoring technology based on machine vision

/pdf/progress-and-perspectives-of-in-situ-optical-monitoring-in-3g34b9wf.pdf

Progress and perspectives of in-situ optical monitoring in laser beam welding: Sensing, characterization and modeling

Defect detection during laser welding using electrical signals and high-speed photography

Using multi-characteristic information fusion based on a BP (back propagation) NN (neural network) of the plume and spatters to monitor the high-power disk laser welding of type 304 austenitic stainless steel is presented. An ultraviolet and visible sensitive highspeed video camera was used to capture the dynamic images of laser welding plume and spatters during laser welding. The number and area of spatters, and the area, height, tilt angle and centroid of plume were calculated by using image processing technology and defined as the characteristic parameters of plume and spatters, which were used as inputs of the neural network. The weld bead width was considered as a parameter reflecting the welding status, which was used as output of the neural network. Relations of plume and spatters with laser welding status was established by a BP neural network and experimental results showed that the proposed method could effectively estimate the high-power disk laser welding status when the laser power ranged from 2 kW to 10 kW.

/pdf/neural-network-of-plume-and-spatter-for-monitoring-high-2b6dxrhlcr.pdf

Neural network of plume and spatter for monitoring high-power disk laser welding

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Neural network of plume and spatter for monitoring high-power disk laser welding