Neural Computing Approach to Shape Change Estimation in Hot Isostatic Pressing
A neural network approach is presented for the estimation of shape change during a Hot Isostatic Pressing (HIP) process of nickel-based superalloys for near net-shape manufacture. For the HIP process, shrinkage must be estimated accurately; otherwise, the finished piece will need excessive machining and expensive nickelbased alloy powder will be wasted (if overestimated) or the part will be scrapped (if underestimated). Estimating shape change has been a very difficult task in the powder metallurgy industry and approaches range from rules of thumb to sophisticated finite element models. However, the industry still lacks a reliable and general way to accurately estimate final shape. This paper demonstrates that a neural network approach is promising to estimate post-HIP dimensions from a combination of pre-HIP dimensions, powder characteristics and processing information.
KeywordsNeural Network Approach Solid Cylinder Shape Distortion Container Dimension Validation Network
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