Purpose
Studies on titanium implants have shown that the mechanical properties of the parts are affected by the microstructure characteristic derived from the manufacturing process. The properties of different orientations of specimens under the same process parameters will be different, which should be considered in the application of bone implants. This paper aims to understand the influence of microstructure on micro-hardness, wear and corrosion resistance in different orientations.
Design/methodology/approach
The authors manufactured titanium parts and carried out micro-hardness, wear tests and electrochemical corrosion of different orientations under the same process conditions. Then, finally studied the evolution mechanism of the microstructure in different orientations and its influence mechanism on wear and corrosion mechanism.
Findings
The melting method makes the grains on the surface in XY orientation finer. The wear mechanism of XY orientation is abrasive wear, that of XZ and YZ orientations are adhesive wear. During corrosion, XY orientation forms a stable passivation film earlier. Compared with XZ and YZ orientations, XY orientation has higher micro-hardness, better wear and corrosion resistance.
Originality/value
In this paper, the microstructure, wear and corrosion resistance of selective laser melted parts were discussed and the differences in different orientations under the same experimental conditions were discussed. The evolution mechanism of the microstructure in different orientations and its influence mechanism on wear mechanism and corrosion mechanism was studied. The mechanical anisotropy of selective laser melted components was discussed.
Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.
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