首页|Researchers from Georgia Institute of Technology Provide Details of New Studies and Findings in the Area of Machine Learning (Bead Geometry Prediction and Optim ization for Corner Structures In Directed Energy Deposition Using Machine Learni ng)
Researchers from Georgia Institute of Technology Provide Details of New Studies and Findings in the Area of Machine Learning (Bead Geometry Prediction and Optim ization for Corner Structures In Directed Energy Deposition Using Machine Learni ng)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting originating in Atlanta, Geo rgia, by NewsRx journalists, research stated, "Wire Arc Additive Manufacturing a s a Directed Energy Deposition technology has disruptive potential for modern ma nufacturing. The technology comes with the flexibility and material efficiency o f additive manufacturing processes while mitigating the disadvantages through hi gh material deposition rates and high energy efficiency." Financial support for this research came from Morris M. Bryan, Jr. Professorship . The news reporters obtained a quote from the research from the Georgia Institute of Technology, "However, the prevalence of the technology is inhibited by its g eometrical inaccuracy and the large induced residual stresses. This work tackles the former problem by capturing the process parametergeometry relationship usin g Machine Learning. To do so, multiple mild steel welding beads with varying sha pe features like corner angles are printed using a Metal Inert Gas welding machi ne attached to an industrial robot. The cross-sectional profile of the printed b eads is measured using a point laser sensor and correlated through a Multilayer Perceptron to input features such as travel speed, wire feed speed, and shape fe atures. By incorporating varying corner angles, a holistic model, not limited to geometry prediction of straight beads only, is trained. Using the model, excess material at the inner angle of corners determined by the overlapping regions of the two adjacent beads can be predicted."
AtlantaGeorgiaUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningTechnologyGeorgia Institute of Technology
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