This work presents the multi-objective optimization results of three experimental cases involving the laser sintering/melting operation and obtained by a virus evolutionary genetic algorithm. From these three experimental cases, the first one is formulated as a single-objective optimization problem aimed at maximizing the density of Ti6Al4V specimens, with layer thickness, linear energy density, hatching space and scanning strategy as the independent process parameters. The second one refers to the formulation of a two-objective optimization problem aimed at maximizing both the hardness and tensile strength of Ti6Al4V samples, with laser power, scanning speed, hatch spacing, scan pattern angle and heat treatment temperature as the independent process parameters. Finally, the third case deals with the formulation of a three-objective optimization problem aimed at minimizing mean surface roughness, while maximizing the density and hardness of laser-melted L316 stainless steel powder. The results obtained by the proposed algorithm are statistically compared to those obtained by the Greywolf (GWO), Multi-verse (MVO), Antlion (ALO), and dragonfly (DA) algorithms. Algorithm-specific parameters for all the algorithms including those of the virus-evolutionary genetic algorithm were examined by performing systematic response surface experiments to find the beneficial settings and perform comparisons under equal terms. The results have shown that the virus-evolutionary genetic algorithm is superior to the heuristics that were tested, at least on the basis of evaluating regression models as fitness functions.

This work deals with the experimental investigation and multi-objective optimization of mean kerf angle (A) and mean surface roughness (Ra) in laser cutting (LC) fused filament fabrication (FFF) 3D-printed (3DP), 4 mm-thick polylactic acid (PLA) plates by considering laser feed (F) and power (P) as the independent control parameters. A CO₂ laser apparatus was employed to conduct machining experiments on 27 rectangular workpieces. An experimental design approach was adopted to establish the runs according to full-combinatorial design with three repetitions, resulting in 27 independent experiments. A customized response surface experiment was formulated to proceed with regression equations to predict the responses and examine the solution domain continuously. After examining the impact of F and P on mean A and mean Ra, two reliable prediction models were generated to model the process. Furthermore, since LC is a highly intricate, non-conventional machining process and its control variables affect the responses in a nonlinear manner, A and Ra were also predicted using an artificial neural network (NN), while its resulting performance was compared to the predictive regression models. Finally, the regression models served as objective functions for optimizing the responses with an intelligent algorithm adopted from the literature.

This paper presents simulation results obtained by a set of modern algorithms adhering to swarm intelligence for minimising wear rate in the case of A356/Al2O3 nanocomposites produced using a compocasting process. Grey wolf optimisation (GWO) algorithm, moth-flame optimisation (MFO) algorithm, dragonfly algorithm (DA) and whale optimisation algorithm (WOA) were the algorithms under examination. A full quadratic regression equation that predicts wear rate, as the optimisation objective by considering reinforcement content, sliding speed, normal load and reinforcement size as the independent process parameters, was utilised as the objective function. Simulation results obtained by the selected algorithms were quite promising in terms of fast convergence and global optimum result arrival, thus prompting to further investigation of applying swarm intelligence to general problem-solving aspects related to tribology.

Machinability of engineering materials is crucial for industrial manufacturing processes since it affects all the essential aspects involved, e.g. work­load, resources, surface integrity and part quality. Two basic ma­chin­ability para­meters are the surface roughness, closely associated with the functional and tribological performance of components, and the cutting forces acting on the tool. Knowledge of the cutting forces is needed for estimation of power re­quirements and for the design of machine tool elements, tool-holders and fix­tures, adequately rigid and free from vibration. This work in­ve­stigates the in­flu­ence of cutting conditions on machinability indicators such as the main cutting force Fc and surface roughness parameters Ra and Rt when longitudinally turning CuZn39Pb3 brass alloy. Full quadratic regression models were de­veloped to correlate the machining conditions with the imparted machinability characteristics. Further on, an advanced artificial grey wolf optimization algorithm was implemented to optimize the aforementioned responses with great success in finding the final optimal values of the turning parameters.