RESUMEN
Titanium nitride (TiN) thin film coatings were grown over silicon (p-type) substrate using the atmospheric pressure chemical vapour deposition (APCVD) technique. The synthesis process was carried out to evaluate the effect of deposition time on the physical and mechanical characteristics of TiN coating. Thin films grown over Si substrate were further characterised to evaluate the morphological properties, surface roughness and mechanical properties using a scanning electrode microscope (SEM), atomic force microscopy (AFM) and nanoindentation, respectively. EDS equipped with SEM showed the presence of Ti and N elements in considerable amounts. TiN morphology obtained from the SEM test showed small-sized particles on the surface along with cracks and pores. AFM results revealed that by increasing the deposition time, the surface roughness of the coating also increased. The nanomechanical properties such as nanohardness (H) and Young's modulus (E), etc., evaluated using the nanoindentation technique showed that higher deposition time led to an increase in H and E. Overall, it was observed that deposition time plays a vital role in the TiN coating deposition using the CVD technique.
RESUMEN
The popularity of micro-machining is rapidly increasing due to the growing demands for miniature products. Among different micro-machining approaches, micro-turning and micro-milling are widely used in the manufacturing industry. The various cutting parameters of micro-turning and micro-milling has a significant effect on the machining performance. Thus, it is essential that the cutting parameters are optimized to obtain the most from the machining process. However, it is often seen that many machining objectives have conflicting parameter settings. For example, generally, a high material removal rate (MRR) is accompanied by high surface roughness (SR). In this paper, metaheuristic multi-objective optimization algorithms are utilized to generate Pareto optimal solutions for micro-turning and micro-milling applications. A comparative study is carried out to assess the performance of non-dominated sorting genetic algorithm II (NSGA-II), multi-objective ant lion optimization (MOALO) and multi-objective dragonfly optimization (MODA) in micro-machining applications. The complex proportional assessment (COPRAS) method is used to compare the NSGA-II, MOALO and MODA generated Pareto solutions.
RESUMEN
In this article, an improved variant of the cuckoo search (CS) algorithm named Coevolutionary Host-Parasite (CHP) is used for maximizing the metal removal rate in a turning process. The spindle speed, feed rate and depth of cut are considered as the independent parameters that describe the metal removal rate during the turning operation. A data-driven second-order polynomial regression approach is used for this purpose. The training dataset is designed using an L16 orthogonal array. The CHP algorithm is effective in quickly locating the global optima. Furthermore, CHP is seen to be sufficiently robust in the sense that it is able to identify the optima on independent reruns. The CHP predicted optimal solution presents ±10% deviations in the optimal process parameters, which shows the robustness of the optimal solution.
