An experimentally derived hybrid intelligent tool for analysing and optimising the clad height and melt-pool depth in laser solid freeform fabrication process

In this investigation, a comparative experimental study is conducted to obtain an efficient hybrid intelligent framework for analysing and optimising the operating parameters of the laser solid freeform fabrication (LSFF) process. Here, the experimental studies are conducted in two different stages. In the first stage, different concepts of machine learning systems are taken into account to find a simple yet accurate intelligent model for identifying the LSFF process. To do so, multi-layered neural network with different types of analytical, gradient-based and heuristic learning strategies, i.e. extreme learning machine, back-propagation and steepest descend gradient-based learning and Nelder–Mead simplex heuristic, respectively, are adopted and applied to the LSFF process. In the second stage, different types of swarm- and evolutionary-based metaheuristics, i.e. differential evolutionary algorithm, particle swarm optimisation, the great salmon run, firefly algorithm, bee algorithm, are used to simultaneously find the optimal values of melt-pool depth and clad height during the LSFF process. The statistical results of the simulation indicate that the conducted experiments can result in a fast, robust and accurate hybrid intelligent system which can easily cope with the nonlinearities and uncertainties of the resulting optimisation problem.