Overlapping optimization of hybrid deposited and micro-rolling additive manufacturing

Purpose This paper aims to summarize the influence law of hybrid deposited and micro-rolling (HDMR) technology on the bead morphology and overlapping coefficient. A better bead topology positively supports the overlapping deposited in multi-beads between layers while actively assisting the subsequent layer's deposition in the wire and arc additive manufacturing (WAAM). Hybrid-deposited and micro-rolling (HDMR) additive manufacturing (AM) technology can smooth the weld bead for improved surface quality. However, the micro-rolling process will change the weld bead profile fitting curve to affect the overlapping coefficient. Design/methodology/approach Weld bead contours for WAAM and HDMR were extracted using line lasers. A comparison of bead profile curves was conducted to determine the influence law of micro-zone rolling on the welding bead contour and fitting curve. Aiming at the optimized overlapping coefficient of weld bead in HDMR AM, the optimal HDMR overlapping coefficient curve was proposed which varies with the reduction based on the best surface flatness. The mathematical model for overlapping in HDMR was checked by comparing the HDMR weld bead contours under different rolling reductions. Findings A fitting function of the bead forming by HDMR AM was proposed based on the law of conservation of mass. The change rule of the HDMR weld bead overlapping spacing with the degree of weld bead rolling reduction was generated using the flat-top transition calculation for this model. Considering the damming-up impact of the first bead, the overlapping coefficient was examined for its effect on layer surface flatness. Originality/value Using the predicted overlapping model, the optimal overlapping coefficients for different rolling reductions can be achieved without experiments. These conclusions can encourage the development of HDMR technology.

Correction: Wang et al. Parametric Formula for Stress Concentration Factor of Fillet Weld Joints with Spline Bead Profile. Materials 2020, 13, 4639

The authors wish to revise the following from pages 16–18 in the text of Appendix B [...]

A Study on the Machine Learning Framework for the Geometric Modelling of Wire Arc Bead Profile

Wire Arc Additive Manufacturing (WAAM) is a manufacturing process that deposits weld beads layer-by-layer in a planar fashion, leading to a final part. Thus, the accuracy of the printed geometry is largely dependent on the knowledge of the bead profile employed, which by itself is dependent on a variety of process parameters, such as wire feedrate and torch speed. Existing models for modelling bead profile are based on its width and height, which do not necessarily capture the geometry of the weld bead accurately. This could affect the step over increment strategy, which dictates the geometry of the resulting overlapping valley. In this paper, we formulate and evaluate the performance of a variety of machine learning framework for predicting the bead cross-sectional profiles. To model the geometry of a bead, we explored direct cartesian representations using polynomials and vertical coordinates, as well as a higher dimensional representation using planar quaternions for supervised learning. Experiments are conducted on single bead SS316L material to compare the various framework performance. We found that among these, the planar quaternion representation with a non-linear neural network framework captures and retains the curvature characteristics of the bead during the learning and prediction process most accurately with a mean Chi-Square goodness of fit of 0.026.