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In metal additive manufacturing, moving heat sources cause spatial and time-dependent variations of temperature and strain that can lead to part distortions. Distortion prediction and optimized deposition parameters can increase the dimensional accuracy of the generated components. In this study, an analytical approach for modeling the effect of clad height and substrate thickness is experimentally validated. Additionally, the influence of the scanning pattern as a function of clad height and substrate thickness is determined experimentally. The analytical model is based on the cool-down phase mechanism and assumes the formation of constant thermal shrinking forces for each deposited layer. The model accurately predicts longitudinal cantilever distortion after experimental calibration when compared with similar experimental conditions. For multi-layer deposition, the scanning pattern has the largest influence on distortion for thin-walled substrates. An optimized deposition strategy with longitudinal scanning vectors leads to a distortion reduction of up to 86%. The results highlight the potential of mechanical modeling and scanning strategy optimizations to increase the shape accuracy for industrial applications in the field of additive manufacturing.

Effect of clad height, substrate thickness and scanning pattern on cantilever distortion in direct metal deposition