Investigation, modeling and optimization of abnormal areas of weld beads in wire and arc additive manufacturing

Tang, Shangyong; Wang, Guilan; Huang, Cheng; Li, Runsheng; Zhou, Siyu; Zhang, Haiou

doi:10.1108/rpj-08-2019-0229

Cited by 18 publications

(9 citation statements)

References 21 publications

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“…Even though it has strong ability of deoxidizing and slagging, it still leads to a certain degree of oxidation on the surface of the material when it is formed in an open environment. The forming process is optimized by adjusting the length and width, and a similar forming accuracy is obtained (Chen et al, 2018;Tang et al, 2020). At the same time, in the hybrid process, the surface undulation at the joining can also be peeled off, so that the surface tends to be smooth, which can effectively improve the surface roughness and accuracy.…”

Section: Discussionmentioning

confidence: 92%

“…The CT device is a micron X-ray CT scanner system with type Y. CHEETAH. The hybrid process parameters used in the experiment were optimized in the early stage (Zhang et al, 2020). The optimized process parameters can not only ensure that the shot peening point is located in the stress relaxation zone of the material, but also ensure the forming process of laser cladding.…”

Section: Experimental Scheme and Process Parametersmentioning

confidence: 99%

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An approach to reduce stress and defects: a hybrid process of laser cladding deposition and shot peening

Zhang

Geng

2021

RPJ

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Purpose Laser cladding deposition is limited in industrial application by the micro-defects and residual tensile stress for the thermal forming process, leading to lower fatigue strength compared with that of the forging. The purpose of this paper is to develop an approach to reduce stress and defects. Design/methodology/approach A hybrid process of laser cladding deposition and shot peening is presented to transform surface strengthening technology to the overall strengthening technology through layer-by-layer forming and achieve enhancement. Findings The results show that the surface stress of the sample formed by the hybrid process changed from tensile stress to compressive stress, and the surface compressive stress introduced could reach more than four times the surface tensile stress of the laser cladding sample. At the same time, internal micro-defects such as pores were reduced. The porosity of the sample formed by the hybrid process was reduced by 90.12% than that of the laser cladding sample, and the surface roughness was reduced by 43.16%. Originality/value The authors believe that the hybrid process proposed in this paper can significantly expand the potential application of laser cladding deposition by solving its limitations, promoting its efficiency and applicability in practical cases.

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Section: Discussionmentioning

confidence: 92%

Section: Experimental Scheme and Process Parametersmentioning

confidence: 99%

An approach to reduce stress and defects: a hybrid process of laser cladding deposition and shot peening

Zhang

Geng

2021

RPJ

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“…The RSM method was used to model, evaluate, and optimize a multi-factor response problem through appropriately structured experiments and optimize objectives. Additionally, the technique represents the non-linear relationship between the input and response variables [23]. A box Behnken design was employed in this study to minimize the number of experiments and to determine the interaction between the specified factor combination.…”

Section: Experimental Designmentioning

confidence: 99%

Parametric Optimisation of Micro Plasma Welding for Wire Arc Additive Manufacturing by Response Surface Methodology

Rosli¹,

Alkahari²,

Ramli³

et al. 2022

Manufacturing Technology

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High deposition rate with minimal heat input is one of the primary emphasis in wire arc additive manufacturing. This study aims to determine the optimal input parameters of micro plasma welding for single-layer deposition. The stability of a single layer is crucial as it serves as the foundation to the multi-layers deposition in producing 3D additively manufactured structure. The study focuses on wire feeding speed, welding speed, and pulse and their interaction between the input and response variables. Based on the study, the regression equation between the three key parameters and the response using the Box-Behnken Design response surface methodology was proposed. The outcome demonstrates that the optimized sample deposition produces a smooth surface appearance with no apparent defects. additive manufacturing 3D printing wire arc additive manufacturing micro plasma arc welding response surface methodology

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“…A is the first point (x 1 =0), Figure 3 The schematic diagram of the temperature stacking algorithm Figure 4 The natural images and profile images of deposition layers with various defects and B is the last point (x 2 = 0). The deposition layer pixel width is x 2 À x 1 , which will be sequentially scanned to obtain a curve of the pixel width from the arc striking point to the arcing extinguishing point (Tang et al, 2020).…”

Section: Extraction Of the Deposition Layer Widthmentioning

confidence: 99%

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

Chen

Kong³

et al. 2021

RPJ

Self Cite

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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.

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Investigation, modeling and optimization of abnormal areas of weld beads in wire and arc additive manufacturing

Cited by 18 publications

References 21 publications

An approach to reduce stress and defects: a hybrid process of laser cladding deposition and shot peening

An approach to reduce stress and defects: a hybrid process of laser cladding deposition and shot peening

Parametric Optimisation of Micro Plasma Welding for Wire Arc Additive Manufacturing by Response Surface Methodology

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

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