A Quantitative Prediction Method of Cladding Layer Offset in Additive Manufacturing

Abstract

The invention relates to a method for quantitatively predicting the deviation of cladding layers in additive manufacturing, and belongs to the technical field of precision welding. After completing the bottoming work of the first four layers of additive materials, use the molten pool visual sensor to collect the molten pool image of the offset cladding layer of the fifth layer of additive material, and select the ROI area of ​​the molten pool image; calculate each The offset corresponding to a collected melt pool image is made into a data set, and the arc starting point and arc extinguishing point of the fifth layer of the additive are determined according to the data set; different cladding layers are classified under the offset In the experiment, a deep residual network was used for classification; a prediction experiment was performed on the linearly changing cladding layer offset, and the classification task of different offsets of the weld was converted into a regression task; the generalization ability of the network was verified. The invention uses the visual information of the molten pool to quantitatively predict the offset of the cladding layer in the process of adding materials, and adjusts the actual position of the welding torch in the process of adding materials, thereby obtaining a good shape of the molten pool and improving the welding quality.

Description

technical field [0001] The invention relates to a method for quantitatively predicting cladding layer offset in additive manufacturing, and belongs to the technical field of precision welding. Background technique [0002] Additive manufacturing technology is a new manufacturing technology based on the principle of layer-by-layer discrete and layer-by-layer accumulation to realize material forming through "bottom-up". This technology can quickly and accurately manufacture objects with complex shapes, and has the advantages of high material utilization rate, good forming effect, low technical cost and high production efficiency. It solves the process bottleneck of rapid manufacturing of complex structural parts. It has been widely used in various industrial fields such as automobiles, aerospace, and medical treatment. This technology greatly improves the production capacity of welding, and can effectively guarantee the welding quality of the welded parts. [0003] In actua...

AI Technical Summary

Problems solved by technology

For passive vision technology, since it directly monitors the weld centerline and welding torch at the arc position, it will not produce the problem of advanced detection error similar to active vision because it misses the field of view. Some scholars have done some work on passive weld seam tracking. However, there are few reports on seam tracking during the additive process

[0005] With the development of computer technology and the rise of big data, deep learning has been widely used in various industrial fields, including welding, but there are few reports on the use of deep learning to monitor passive weld seam offset in additive manufacturing.

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