The invention discloses a method for additive manufacturing of an aluminum flange on an ellipsoidal box bottom by means of electric-arc wire melting. The method comprises the following steps that theellipsoidal box bottom is mounted on a positioner; a lower neck part of the flange is accumulated in a multi-layer mode, a visual sensor is mounted on a gun body to acquire an electric arc image, an error between the height from an aluminum alloy surface point to a nozzle of the gun body and a preset height is calculated, and the height of the gun body is adjusted through a control system according to the error information; after accumulation of the lower neck part is completed, a workbench of the positioner is turned by 90 degrees, and an upper neck part of the flange is accumulated in a multi-layer and multi-pass mode; and after accumulation of the upper neck part is completed, bull heat treatment is conducted on the ellipsoidal box bottom and a near-net part, formed by accumulation, ofthe aluminum alloy flange, and machining is conducted finally according to product requirements. According to the method for additive manufacturing of the aluminum flange on the ellipsoidal box bottomby means of electric-arc wire melting, all weld joints of a storage box are eliminated, the physical performance of the box bottom and an aluminum alloy flange connector is improved, and the problemsthat the stability is poor and the gun body is likely to collide with a substrate or an accumulation layer during additive manufacturing of a flange on the ellipsoidal substrate by means of electric-arc wire melting are effectively solved.

Method for additive manufacturing of aluminum flange on ellipsoidal box bottom by means of electric-arc wire melting

The invention belongs to the field of manufacturing of arc wire feed additives, and discloses a weld bead modeling method, device and system for arc additive manufacturing According to the method, adynamic parameter method is adopted, and different welding process parameters are adopted in the same welding bead in the one-time arc additive manufacturing process, so that a welding bead of which the shape is synchronously and dynamically changed according to the dynamic change of the welding process parameters; and a line laser sensor is used for scanning to obtain the processed weld bead segmented shapes, and each segment of weld bead shape is in one-to-one correspondence with the welding process parameter of the same segment and used as training data to train the neural network so that aweld bead modeling model of the corresponding weld bead shape can be obtained according to the input welding process parameters According to the method, multiple sets of experimental data can be obtained through one-time processing experiment, sufficient training data are provided under the condition of reducing experiment times and experimental cost, and meanwhile, the technical problems that atraditional orthogonal experiment method and a corresponding curved surface method require complex regression equation selection and parameter optimization are solved

Weld bead modeling method, device and system for arc additive manufacturing

The invention discloses a laser metal additive deposition fusion state real-time prediction method and system, and the method comprises the following steps: (1), building a real-time prediction initial model, the real-time prediction initial model comprising a backGAN network used for converting a process parameter data set into image data, and a plurality of paths of parallel convolutional neuralnetworks connected with the backGAN network and used for extracting feature data in the image data, a feature fusion layer connected with the multiple paths of parallel convolutional neural networksand used for carrying out feature fusion on the feature data output by the multiple paths of convolutional neural networks, a full connection layer and a classification layer sequentially connected behind the feature fusion layer; (2), training the real-time prediction initial model to form a real-time prediction final model; and (3), inputting a process parameter data set into the real-time prediction final model to predict a laser metal additive deposition fusion state in real time. By constructing the prediction model of the backGAN network and the VGG19 network, the molten pool state can be predicted through process parameters, a neural network with supervised learning is formed, and the cost is low.

Laser metal additive deposition fusion state real-time prediction method and system

The invention relates to an excess weld metal and weld penetration collaborative prediction method based on a molten pool image and a deep residual error network. The invention belongs to the technical field of image analysis. The trend of weld penetration and excess weld metal of future development of a weld joint can be accurately predicted, and the quality of welding can be improved. The methodcomprises the following steps of 1, restoring the change condition of the excess weld metal and the weld penetration in the length direction of a weld joint; 2, building an image processing frame; 3,determining a basic network; 4, determining the input end of the network; 5, determining the output end of the network; 6, evaluating the learning ability of the network; and 7, predicting the excessweld metal and the weld penetration through the network. According to the invention, through weld penetration and excess weld metal cooperative prediction based on a molten pool image and a deep residual network, the molten pool edge and internal details can be clearly observed, changes of the weld penetration and the excess weld metal in the weld forming process are monitored in real time, the trend of the weld penetration and the excess weld metal developed in the future can be accurately predicted, the method is suitable for different welding parameters and different weldments, and the welding quality is regulated and controlled in real time.

Excess weld metal and weld penetration collaborative prediction method based on molten pool image and deep residual error network

The invention discloses an online monitoring and negative feedback state recognition method for a defocusing amount of a laser melting deposition powder flow, and belongs to the technical field of laser additive manufacturing. The method comprises the following steps: firstly, performing focusing on an image coaxial acquisition device by adopting a calibrating plate, performing additive printing,acquiring an image of a molten pool through the image coaxial acquisition device, and performing pre-processing on a visual image of the molten pool by means of an image processing unit; secondly, determining whether the visual image of the molten pool is in a scope of the depth of field, if the visual image of the molten pool is in the scope, continuing to perform next flow on the visual image ofthe molten pool, on the contrary, directly determining that the defocusing amount at the moment cannot guarantee the completion of processing or is not in a negative feedback state; and finally, inputting the acquired visual image of the molten pool into a neural network for image recognition, performing classified tagging on the acquired visual image of the molten pool, outputting tag values representing different defocusing amounts or feedback states in the neural network, and then obtaining the defocusing amount or the feedback state. According to the online monitoring and negative feedback state recognition method for the defocusing amount of the laser melting deposition powder flow, the defocusing amount and the feedback state can be detected; a good processing process is guaranteed;and the application range is wide.

Online monitoring and negative feedback state recognition method for defocusing amount of laser melting deposition powder flow

The invention discloses a laser metal additive manufacturing fusion depth prediction system based on deep learning and transfer learning, which comprises a printing workbench, an image acquisition device, a thermal imager, a man-machine interaction device, a display and a host; the image acquisition device, the thermal imager, the man-machine interaction device and the display are electrically connected with the host. Molten pool images and temperature images are continuously collected in a certain time sequence; firstly, normalization processing is carried out on an effective molten pool image and an effective temperature image; the parameters of the picture size and the pixel size of the molten pool image are kept consistent, other irrelevant features are eliminated during training of the deep learning convolutional neural network model, only key features are trained, and the system has the advantage that the training efficiency of the deep learning convolutional neural network modelis improved; and the fusion depth is predicted by adopting a deep learning convolutional neural network model, so that the precision of parameters can be effectively improved.

Metal additive forming fusion depth real-time prediction method based on depth and transfer learning

The invention discloses an additive manufacturing forming size prediction system based on deep learning and feature fusion, which comprises a printing workbench, an image acquisition device, a thermalimager, a man-machine interaction device, a display and a host. The image acquisition device, the thermal imager, the man-machine interaction device and the display are electrically connected with the host. Molten pool images and temperature images are continuously collected in a certain time sequence; firstly, normalization processing is carried out on an effective molten pool image and an effective temperature image; the parameters of the picture size and the pixel size of the molten pool image are kept consistent, other irrelevant features are eliminated during training of the deep learning convolutional neural network model, only key features are trained, and the method has the advantage that the training efficiency of the deep learning convolutional neural network model is improved;and the deep learning convolutional neural network model is adopted to predict the single-channel forming height, so that the precision of parameters can be effectively improved.

Metal additive forming size real-time prediction method based on depth feature fusion

The invention discloses a laser metal additive manufacturing forming precision prediction system based on deep learning, which comprises a printing workbench, an image acquisition device, a man-machine interaction device, a display and a host. The image acquisition device, the man-machine interaction device and the display are electrically connected with the host. Molten pool images and temperature images are continuously collected in a certain time sequence; firstly, normalization processing is carried out on an effective molten pool image and an effective temperature image; the parameters ofthe picture size and the pixel size of the molten pool image are kept consistent, other irrelevant features are eliminated during training of the deep learning convolutional neural network model, only key features are trained, and the method has the advantage that the training efficiency of the deep learning convolutional neural network model is improved; and the deep learning convolutional neural network model is adopted to predict the single-channel forming width, so that the precision of parameters can be effectively improved.

Metal additive manufacturing forming size real-time prediction method based on deep learning

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yan Hu

Author Tools

Chat about Author

Papers

Metal additive forming fusion depth real-time prediction method based on depth and transfer learning

Metal additive forming size real-time prediction method based on depth feature fusion

Metal additive manufacturing forming size real-time prediction method based on deep learning

Laser metal additive deposition fusion state real-time prediction method and system