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Modeling And Control Of Weld Geometry For Welding Rapid Prototyping Based On TIG Building-up Technology

Posted on:2009-07-12Degree:MasterType:Thesis
Country:ChinaCandidate:G Y ZhangFull Text:PDF
GTID:2121360245971067Subject:Mechanical and electrical engineering
Abstract/Summary:PDF Full Text Request
Welding rapid prototyping is a rapid prototyping (RP) technology which can manufacture metal parts directly from 3D figure. A part prototyped by welding rapid prototyping is composed of pure welds, so it is high dense and can fulfil the application requirements. Using welding technology, the cost is very low, so it has the potential to be widely used in the industry. As to the welding rapid prototyping technology based on TIG building-up, in order to solve the problems during the deposition process, the relationship between welding parameters and weld geometry is studied, and the goal to improve the deposition accuracy is achieved. The research works of the paper are as follows:1. The TIG welding RP system is developed. The welding equipments can be monitored during the deposition process using this system. It can fulfil the weld deposited-based RP technology requirements.2. Two BP Neural Networks models for the relationship between welding parameters and the weld geometry are established. The welding parameters can be correctly selected and the weld geometry can be exactly predicted by this system.3. For multi-track deposition process, the effect of the weld path increment on the weld seam geometry, that is weld width and weld reinforcement, is analyzed. A proper weld path increment is obtained in the experiments.4. A weld geometry fuzzy control system based on arc voltage sampling is developed. In this system, the welding parameters, including the welding current, travel speed and wire feeding rate, can be easily controlled to acquire the 3D parts. Results show that deposition accuracy can be improved and good quality components can be prototyped.
Keywords/Search Tags:TIG welding rapid prototyping, welding parameters, BP Neural Networks, fuzzy control
PDF Full Text Request
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