Energy-saving Optimized Parameters for 3D Selective Laser Melting using Dimensionless Model and Machine Learning

In selective laser melting (SLM), determining the optimal processing parameters, such as laser power, scanning speed, hatching space, etc., often relies on trial-and-error experiments. In this study, the final optimal parameters for SLM of Ti6Al4V were effectively characterized using the dimensionless model, melting pool geometry model, energy-saving index, and maximum hatching space. First, by combining the criteria of an optimization zone from a dimensionless model, the ratio of melting pool, and energy saving, the optimal two parameters in laser power and scanning speed were defined that not only provided excellent mechanical properties in as-built samples but also achieved a green design in manufacturing. As a result, for Ti6Al4V with the hatching space of 80 µm, an impressive relative density of 99.977% was achieved, with the tensile strength reaching approximately 1201 MPa, the yield strength reaching 1171 MPa, and the elongation rate reaching 8.2%. Subsequently, the corresponding maximum hatching space of up to 120 µm was achieved by a novel methodology proposed by our group. As a result, the energy saving was much improved to 33.4%, and the relative density of 99.948% was still held compared to the case with the hatching space of 80 µm.It is concluded that this novel approach, with an optimal parameter set in laser power and scanning speed based upon the dimensionless model, melting pool geometry, energy saving, and its corresponding maximum hatching space, can effectively minimize energy consumption and maximize throughput while still maintaining the superior mechanical qualities of the as-built samples.