An in-process multi-feature data fusion nondestructive testing approach for wire arc additive manufacturing

Abstract

Purpose The major problem that limits the widespread use of WAAM technology is the forming quality. However, most of the current research focuses on post-process detections that are time-consuming, expensive and destructive. This paper aims to achieve the on-line detection and classification of the common defects, including hump, deposition collapse, deviation, internal pore and surface slag inclusion. Design/methodology/approach This paper proposes an in-process multi-feature data fusion nondestructive testing method based on the temperature field of the WAAM process. A thermal imager is used to collect the temperature data of the deposition layer in real-time. Efficient processing methods are proposed in this paper, such as the temperature stack algorithm, width extraction algorithm and a classification model based on a residual neural network. Some features closely related to the forming quality were extracted, containing the profile image and width curve of the deposition layer and abnormal temperature features in longitudinal and cross-sections. These features are used to achieve the detection and classification of defects. Findings Thermal non-destructive testing is a potentially superior technology for in-process detection in the industrial field. Based on the temperature field, extracting the most relevant features of the defect information is crucial. This paper pushes current infrared (IR) monitoring methods toward real-time detection and proposes an in-process multi-feature data fusion non-destructive testing method based on the temperature field of the WAAM process. Originality/value In this paper, the single-layer and multi-layer WAAM samples are preset with various defects, such as hump, deposition collapse, deviation, pore and slag inclusion. A multi-feature nondestructive testing methodology is proposed to realize the in-process detection and classification of the defects. A temperature stack algorithm is proposed, which improves the detection accuracy of profile change and solves the problem of uneven temperature from arc striking to arc extinguishing. The combination of residual neural network greatly improves the accuracy and efficiency of detection.

Active and Passive Thermal Management in Wire Arc Additive Manufacturing

“…The process conditions and distortion relation appeared to be highly nonlinear. The box-cox transformation revealed that the −1/2 power of distortion (d) could be linearly expressed as a function of parameters under study, as shown in Equations ( 1) and (2). The significant main factors and interactions were identified with stepwise regression.…”

“…One such additive manufacturing method is the use of a gas metal arc welding (GMAW) process, known as Wire-Direct Energy Deposition (W-DED) or Wire Arc Additive Manufacturing (WAAM). Low capital and operating costs, high deposition rates and material efficiency are some of the benefits of WAAM, making it ideal for manufacturing large structural components widely used in marine, aerospace, defence and transportation [2,3]. WAAM has successfully fabricated super alloys, which are functionally graded materials that have tailored properties for various industrial applications [4].…”

Forming accuracy improvement in wire arc additive manufacturing (WAAM): a review

“…Optical sensors are used to determine the geometric parameters of deposited layers and to establish a database for the corresponding process parameters. Based on such a database, a regression network of current vs the deposited layer geometry can be established (Wang et al , 2022; Zeng et al , 2021; Chen et al , 2021). An active disturbance rejection control is applied to adjust the current in real time and improve the precision (Wang et al , 2021; Wang et al , 2021).…”

Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography

“…It has been shown that the repaired components are prone to generate surface cracks, which can seriously affect the repair quality and even leads to the mechanical failure of the repaired components. Therefore, the nondestructive testing of surface cracks is required to guarantee the quality and structural integrity of repaired components, a challenge which receives much attention from industry and academia [7][8][9][10].…”