A split-optimization approach for obtaining multiple solutions in single-objective process parameter optimization

Abstract

It can be observed from the experimental data of different processes that different process parameter combinations can lead to the same performance indicators, but during the optimization of process parameters, using current techniques, only one of these combinations can be found when a given objective function is specified. The combination of process parameters obtained after optimization may not always be applicable in actual production or may lead to undesired experimental conditions. In this paper, a split-optimization approach is proposed for obtaining multiple solutions in a single-objective process parameter optimization problem. This is accomplished by splitting the original search space into smaller sub-search spaces and using GA in each sub-search space to optimize the process parameters. Two different methods, i.e., cluster centers and hill and valley splitting strategy, were used to split the original search space, and their efficiency was measured against a method in which the original search space is split into equal smaller sub-search spaces. The proposed approach was used to obtain multiple optimal process parameter combinations for electrochemical micro-machining. The result obtained from the case study showed that the cluster centers and hill and valley splitting strategies were more efficient in splitting the original search space than the method in which the original search space is divided into smaller equal sub-search spaces.

Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process

“…To initialize the tuning parameter boundary, a trial-and-error based approach was implemented to determine the basic structure of the deep convolution network. The structure of the network refers to the previous work in [11,27,28] with an added convolutional layer and dropout layer to add nonlinearity and prevent overfitting. The network was initialized with a convolutional layer and one dense layer with 20 neurons.…”

“…The experimental data collected in [11,27,28] were adopted to validate the proposed deep convolutional network. The experimental setup of the ECM process is shown in Figure 2.…”

“…The electrolyte is pumped to a multiple-electrode cell and exits through the small nozzles directed towards the anode workpiece. [11,27,28].…”

Physics-Embedded Machine Learning: Case Study with Electrochemical Micro-Machining

“…The four desired intermediate outputs described by Equations (2), (4), (6) and (8) were calculated using the experimental data obtained from [7]. The relationships developed in Equations (2), (4), (6), and (8) were embedded in the NN as linear connections between the inputs and the hidden neurons of the first hidden layers.…”

“…Teimouri and Baseri [6] used a combination of the artificial bee colony (ABC) algorithm and fuzzy logic (FL) to create a forward prediction map from the input process parameters to the KPIs of friction stir welding (FSW). Rajora et al [7] used a generalized regression neural network (GRNN) to map from the input process parameters to the KPIs of µ-ECM. Lu et al [8] used a NN for predicting the surface roughness in the micro-milling of the Inconel 718.…”