Fused Deposition Modeling (FDM) can be used to manufacture any complex geometry and internal structures, and it has been widely applied in many industries, such as the biomedical, manufacturing, aerospace, automobile, industrial, and building industries. The purpose of this research is to characterize the polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) materials of FDM under four loading conditions (tension, compression, bending, and thermal deformation), in order to obtain data regarding different printing temperatures and speeds. The results indicated that PLA and PETG materials exhibit an obvious tensile and compression asymmetry. It was observed that the mechanical properties (tension, compression, and bending) of PLA and PETG are increased at higher printing temperatures, and that the effect of speed on PLA and PETG shows different results. In addition, the mechanical properties of PLA are greater than those of PETG, but the thermal deformation is the opposite. The above results will be a great help for researchers who are working with polymers and FDM technology to achieve sustainability.

In order to optimize the efficiency of the Fused deposition modeling (FDM) process, this study used polylactic acid (PLA) material under different parameters (the printing angle and the raster angle) to fabricate specimens and to explore its tensile properties. The effect of the ultraviolet (UV) curing process on PLA materials was also investigated. The results showed that the printing and raster angles have a high impact on the tensile properties of PLA materials. The UV curing process enhanced the brittleness and reduced the elongation of PLA material. Different effects were observed on tensile strength and modulus of specimens printed with different parameters after UV curing. The above results will be a great help for researchers who are working to achieve sustainability of PLA materials and FDM technology.

Additive manufacturing (AM) has the advantages of providing materials with lightweight microporous structures and customized features, and being environmentally safe. It is widely used in medical sciences, the aerospace industry, biological research, engineering applications, and other fields. Among the many additive manufacturing methods, fused deposition modeling (FDM) is relatively low-cost, wastes less raw material and has a lower technical threshold. This paper presents a study on 3D printing based on FDM by changing two printing parameters, namely the printing temperature and filling percentage. The produced polylactic acid (PLA) material was analyzed through tensile and Shore D hardness tests and the differences in mechanical properties before and after the UV curing process were analyzed. The results show that increasing the filling percentage or increasing the printing temperature can effectively improve the tensile Young’s modulus, ultimate tensile strength, elongation, and Shore hardness of the material. The UV curing process could enhance the rigidity and hardness of the material significantly but reduced the strength and toughness of the material. These findings could benefit researchers studying FDM with the goal of achieving sustainable manufactured materials.

This paper investigated the hardness property of the fused deposition modeling (FDM)-printed PLA samples via different process parameters of printing and raster angles. The hardness data were sampled from the flat and edge surfaces of the samples. In addition, the effect of hardness characters after the ultraviolet (UV) curing process was analyzed. Furthermore, this research found that the printing and raster angles significantly affected the hardness value of the PLA part, which slightly increased after the UV irradiation. Moreover, the results of this study will provide a reference for the field of FDM application.

The antlion optimization algorithm (ALO) is one of the most effective algorithms to solve combinatorial optimization problems, but it has some disadvantages, such as a long runtime. As a result, this problem impedes decision makers. In addition, due to the nature of the problem, the speed of convergence is a critical factor. As the size of the problem dimension grows, the convergence speed of the optimizer becomes increasingly significant. Many modified versions of the ALO have been developed in the past. Nevertheless, there are only a few research articles that discuss better boundary strategies that can increase the diversity of ants walking around an antlion to accelerate convergence. A novel exponential-weighted antlion optimization algorithm (EALO) is proposed in this paper to address slow convergence rates. The algorithm uses exponential functions and a random number in the interval 0, 1 to increase the diversity of the ant’s random walks. It has been demonstrated that by optimizing twelve classical objective functions of benchmark functions, the novel method has a higher convergence rate than the ALO. This is because it has the most powerful search capability and speed. In addition, the proposed method has also been compared to other existing methods, and it has obtained superior experimental results relative to compared methods. Therefore, the proposed EALO method deserves consideration as a possible optimization tool for solving combinatorial optimization problems, due to its highly competitive results.