ABSTRACT
The use of titanium and titanium-based alloys in the human body due to their resistance to corrosion, implant ology and dentistry has led to significant progress in promoting new technologies. Regarding their excellent mechanical, physical and biological performance, new titanium alloys with non-toxic elements and long-term performance in the human body are described today. The main compositions of Ti-based alloys and properties comparable to existing classical alloys (C.P. TI, Ti-6Al-4V, Co-Cr-Mo, etc.) are used for medical applications. The addition of non-toxic elements such as Mo, Cu, Si, Zr and Mn also provides benefits, such as reducing the modulus of elasticity, increasing corrosion resistance and improving biocompatibility. In the present study, when choosing Ti-9Mo alloy, aluminum and copper (Cu) elements were added to it. These two alloys were chosen because one element is considered a favorable element for the body (copper) and the other element is harmful to the body (aluminum). By adding the copper alloy element to the Ti-9Mo alloy, the elastic modulus decreases to a minimum value of 97 GPa, and the aluminum alloy element increases the elastic modulus up to 118 GPa. Due to their similar properties, Ti-Mo-Cu alloys are found to be a good optional alloy to use.
ABSTRACT
Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.
