Optimization of tile manufacturing process using particle swarm optimization

In this paper, an integrated optimization approach using an artificial neural network and a bidirectional particle swarm is proposed. The artificial neural network is used to obtain the relationships between decision variables and the performance measures of interest, while the bidirectional particle swarm is used to perform the optimization with multiple objectives. Finally, the proposed approach is used to solve a process parameter design problem in cement roof-tile manufacturing. The results showed that the bidirectional particle swarm is an effective method for solving multi-objective optimization problems, and that an integrated approach using an artificial neural network and a bidirectional particle swarm can be used to solve complex process parameter design problems.

► We present a novel approach that integrates ANN with BPSO for solving process parameter design problems.
► We apply the approach to the real-life ceramic tile pressing process where multiple responses are considered.
► Results from experiments indicate superior performance of the proposed approach over the conventional ones.