Wire arc additive manufacturing of Invar parts: bead geometry and melt pool monitoring

Measurement ◽  
2021 ◽  
pp. 110452
Author(s):  
Fernando Veiga ◽  
Alfredo Suarez ◽  
Eider Aldalur ◽  
Teresa Artaza
Keyword(s):  
Additive Manufacturing ◽  
Bead Geometry ◽  
Melt Pool ◽  
Wire Arc Additive Manufacturing

Control of humping phenomenon and analyzing mechanical properties of Al–Si wire-arc additive manufacturing fabricated samples using cold metal transfer process

2021 ◽  
pp. 095440622199840
Author(s):  
Yashwant Koli ◽  
N Yuvaraj ◽  
Aravindan Sivanandam ◽  
Vipin
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Heat Input ◽  
Large Scale ◽  
High Deposition Rate ◽  
Bead Geometry ◽  
Layer By Layer ◽  
Cold Metal Transfer ◽  
Geometrical Shapes ◽  
Wire Arc Additive Manufacturing

Nowadays, rapid prototyping is an emerging trend that is followed by industries and auto sector on a large scale which produces intricate geometrical shapes for industrial applications. The wire arc additive manufacturing (WAAM) technique produces large scale industrial products which having intricate geometrical shapes, which is fabricated by layer by layer metal deposition. In this paper, the CMT technique is used to fabricate single-walled WAAM samples. CMT has a high deposition rate, lower thermal heat input and high cladding efficiency characteristics. Humping is a common defect encountered in the WAAM method which not only deteriorates the bead geometry/weld aesthetics but also limits the positional capability in the process. Humping defect also plays a vital role in the reduction of hardness and tensile strength of the fabricated WAAM sample. The humping defect can be controlled by using low heat input parameters which ultimately improves the mechanical properties of WAAM samples. Two types of path planning directions namely uni-directional and bi-directional are adopted in this paper. Results show that the optimum WAAM sample can be achieved by adopting a bi-directional strategy and operating with lower heat input process parameters. This avoids both material wastage and humping defect of the fabricated samples.

Using Unsupervised Learning for Regulating Deposition Speed During Robotic Wire Arc Additive Manufacturing

2021 ◽  
Author(s):  
Ashish Kulkarni ◽  
Prahar M. Bhatt ◽  
Alec Kanyuck ◽  
Satyandra K. Gupta
Keyword(s):  
Additive Manufacturing ◽  
Unsupervised Learning ◽  
Molten Metal ◽  
Length Variation ◽  
Bead Geometry ◽  
Layer By Layer ◽  
Arc Length ◽  
Regulation Scheme ◽  
Speed Regulation ◽  
Wire Arc Additive Manufacturing

Abstract Robotic Wire Arc Additive Manufacturing (WAAM) is the layer-by-layer deposition of molten metal to build a three-dimensional part. In this process, the fed metal wire is melted using an electric arc as a heat source. The process is sensitive to the arc conditions, such as arc length. While building WAAM parts, the metal beads overlap at corners causing material accumulation. Material accumulation is undesirable as it leads to uneven build height and process failures caused by arc length variation. This paper introduces a deposition speed regulation scheme to avoid the corner accumulation problem and build parts with uniform build height. The regulated speed has a complex relationship with the corner angle, bead geometry, and molten metal dynamics. So we need to train a model that can predict suitable speed regulations for corner angles encountered while building the part. We develop an unsupervised learning technique to characterize the uniformity of the bead profile of a WAAM built layer and check for anomalous bead profiles. We train a model using these results that can predict suitable speed regulation parameters for different corner angles. We test this model by building a WAAM part using our speed regulation scheme and validate if the built part has uniform build height and reduced corner defects.

scholarly journals Vision-Based Melt Pool Monitoring for Wire-Arc Additive Manufacturing Using Deep Learning Method

2021 ◽  
Author(s):  
Chunyang Xia ◽  
Zengxi Pan ◽  
Yuxing Li ◽  
Huijun Li
Keyword(s):  
Deep Learning ◽  
Additive Manufacturing ◽  
Large Scale ◽  
Deposition Process ◽  
Utilization Rate ◽  
High Deposition Rate ◽  
Promising Alternative ◽  
Visual Monitoring ◽  
Melt Pool ◽  
Wire Arc Additive Manufacturing

Abstract Wire-arc additive manufacturing (WAAM) technology has been widely recognized as a promising alternative for fabricating large-scale components, due to its advantages of high deposition rate and high material utilization rate. However, some anomalies may occur during the deposition process, such as humping, spattering, and robot suspend. this study proposed to apply Deep Learning in the visual monitoring to diagnose different anomalies during WAAM process. The melt pool images of different anomalies were collected for training and validation by a visual monitoring system. The classification performance of several representative CNN architectures, including ResNet, EfficientNet, VGG-16 and GoogLeNet, were investigated and compared. The classification accuracy of 97.62%, 97.45%, 97.15% and 97.25% was achieved by each model. The results proved that the CNN models are effective in classifying different types of melt pool images of WAAM. Our study is applicable beyond WAAM and should benefit other additive manufacturing or arc welding techniques.

Mapping of Bead Geometry in Wire Arc Additive Manufacturing Systems Using Passive Vision

2022 ◽  
Author(s):  
Marcus O. Couto ◽  
Arthur G. Rodrigues ◽  
Fernando Coutinho ◽  
Ramon R. Costa ◽  
Antonio C. Leite ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Bead Geometry ◽  
Wire Arc Additive Manufacturing

scholarly journals Evaluation of Bead Geometry for Aluminum Parts Fabricated Using Additive Manufacturing-Based Wire-Arc Welding

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1211
Author(s):  
Hee-keun Lee ◽  
Jisun Kim ◽  
Changmin Pyo ◽  
Jaewoong Kim
Keyword(s):  
Additive Manufacturing ◽  
Heat Input ◽  
Deposition Efficiency ◽  
Bead Geometry ◽  
Thin Wall ◽  
Cross Sectional ◽  
Voltage Ratio ◽  
Input Range ◽  
The Cross ◽  
Wire Arc Additive Manufacturing

The wire arc additive manufacturing (WAAM) process used to manufacture aluminum parts has a number of variables. This study focuses on the effects of the heat input and the current and voltage ratio on the deposition efficiency. The effects of the heat input and current and voltage ratio (V/A) on the bead geometry were analyzed, depending on the cross-sectional geometry of the deposition layers, for nine different deposition conditions. The deposition efficiency was also analyzed by analyzing the cross-sectional geometry of the thin-wall parts made of aluminum. The heat input range was about 2.7 kJ/cm to 4.5 kJ/cm; the higher the heat input, the higher the deposition efficiency. The maximum deposition efficiency achieved in this study was 76%. The current and voltage ratio was used to quantify the portion of voltage (V) in the total heat input (Q), and the effect on the bead geometry was analyzed. As the portion of voltage in the quasi heat input decreased by about 10%, it was found that the deposition efficiency was decreased by 1% to 3%.

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