Polymeric matrix composites are important to the advancement of industries such as the automobile and medicine industries. In this study, the silicon carbide (SiC) particle-reinforced polyamide12 (PA12) matrix composites were fabricated by selective laser sintering system as well as the pure PA12. The surface topographies, mechanical, and tribological properties were further examined. The results indicated that the friction and wear resistance of the composite were improved compared with the PA12 matrix. The compressive strength increased about 8.5%, shore D hardness increased about 6%. The friction coefficient decreased about 10%, the specific wear rate decreased 20% after adding silicon carbide 10% weight to PA12. The wear mechanisms were also discussed. The deformed asperities on the worn surface can withstand more tangential load, and therefore resulted in lower specific wear rate. It was found that the content of SiC particles on the surface were reduced after friction tests. According to the analysis of SEM, EDS, and FTIR results, the wear mechanisms were considered to be the abrasive and fatigue mode. This type of PA12 matrix composite might be a promising potential in marine and energy applications.

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Previous researches have shown that hybrid differential evolution (DE) algorithms incorporated with chaotic sequences are effective in solving single objective optimization problem. Based on these pioneering efforts, this paper extends the hybrid chaotic DE to solve multi-objective optimization problems (MOPs). First, various application of chaotic sequence in DE are studied in detail, and different hybrid chaotic DE algorithms are compared and analyzed in order to find one general chaotic DE. Then, the performances and effectiveness of various hybrid chaotic DE algorithms existed in related literature are examined based upon five benchmark constraint MOPs. The comparative study shows that the hybrid DE with chaotic migration outperformed the hybrid DE with chaotic self-adaptive control parameters F and CR setting. The results also demonstrated that the hybrid chaotic DE is not effective for solving MOPs, while it is successful and competitive for solving single objective optimization problem.