Explore the vast range of titles available.

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

Although intelligent machine learning techniques have been used for input-output modeling of many different manufacturing processes, these techniques map directly from the input process parameters to the outputs and do not take into consideration any partial knowledge available about the mechanisms and physics of the process. In this paper, a new approach is presented for taking advantage of the partial knowledge available about the mechanisms of the process and embedding it into the neural network structure. To validate the proposed approach, it is used to create a forward prediction model for the process of electrochemical micro-machining (μ-ECM). The prediction accuracy of the proposed approach is compared to the prediction accuracy of pure neural structure models with different structures and the results show that the Neural Network (NN) models with embedded knowledge have better prediction accuracy over pure NN models.

A tremendous amount of work has been done in the recent years in the optimization of input parameters, however, current optimization techniques can only provide a single optimal input process parameter combination. Although alternative techniques have been developed to provide multiple solutions with identical objective values, these techniques have low efficiency when searching for multiple solutions. In this paper, a two-stage filter split-optimization approach is proposed to obtain multiple solutions, at a higher efficiency than for a single-objective optimization problem. The aforementioned tasks are accomplished by first performing an initial split-optimization and then performing a second optimization after excluding input parameters from having their range split into sub-ranges based on the results of the initial optimization. The proposed approach enables the algorithm to explore input parameters that have a more significant impact on the objective function, thereby enabling it to find multiple optimal solutions more efficiently. The proposed approach was validated by using it to optimize the input process parameters of an electrochemical machining problem with five input parameters. The results from the case study show that though the proposed approach provided fewer optimal solutions it was able to obtain them at twice the efficiency when compared to the original method.

Hierarchically coupled constraint optimization (HCCO) problems are omnipresent, both in theoretical problems and in real-life scenarios; however, there is no clear definition to identify these problems. Numerous techniques have been developed for some typical HCCO problems, such as assembly job-shop scheduling problems (AJSSPs); however, these techniques are not universally applicable to all HCCO problems. In this paper, an abstract definition and common principles amongst different HCCO problems are first established. Next, based on the definitions and principles, a new optimization algorithm based on evolutionary computation is developed for HCCO. The new optimization algorithm has three key new features: a new initial solution generator, a level barrier-based crossover operator, and a level barrier-based mutation operator. In the initial solution generator, a partial solution is created in the first step that satisfies the lowest level hierarchically coupled constraint (HCC) and each consecutive step afterwards adds on to the partial solution to satisfy the next higher level of HCC. In the level barrier-based operators, the operations are only performed between genes satisfying the same level of HCCs to ensure feasibility of the new solutions. The developed optimization algorithm is used to solve a variety of AJSSPs and the results obtained using the proposed algorithm are compared to other methods used to solve AJSSPs.

Hierarchically coupled constrained optimization problems (HCCOPs) are commonly encountered in the manufacturing industries, however, they haven’t been categorized as such. Due to a lack of clear definition to identify these problems, numerous techniques have been developed for the optimization of HCCOPs but these techniques are not universally applicable to all HCCOPs and are unable to cope with large scale problems. Furthermore, current techniques used for the optimization of these HCOOPs only provide a single optimal solution upon execution. Though the single optimal solution may theoretically satisfy the objective function, it might not be applicable in real life scenario.This research will first focus on establishing an abstract definition and identifying the common principles amongst different HCCOPs. Next, based on the established definition and common principles, a new optimization technique, based on evolutionary computation, will be developed. The proposed algorithm will be developed in a way such that only feasible solutions are generated during the iterations of the algorithm thereby reducing its computational complexity. To validate the proposed algorithm, it will be utilized to optimize HCCOPs commonly encountered in the manufacturing industry such as assembly job-shop scheduling problem (AJSSP) and the simultaneous optimization of Neural Network (NN) structure and weight values. The research will also focus on developing a technique for the multimodal optimization (MMO) of HCCOPs i.e. obtain multiple solutions with the same objective value. To validate the proposed MMO approach, it will first be utilized for the MMO of benchmark job-shop scheduling problem (JSSP) and permutation flow-shop scheduling problem (PFSSP) followed by the MMO of AJSSP.This research would aid in the identification and MMO of HCCOP. The proposed algorithm could be easily applied to any HCCOP while requiring minimal changes.

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.

Producing good surface integrity is one of the main challenges of the machining industry. The increase of the utilization of minimum quantity lubrication (MQL) in order to reduce the amount of lubrication induced a lack of understanding of the physics behind the residual stress generation. Residual stress in the machined surface and subsurface is affected by materials, machining conditions, and tool geometry. These residual stresses could affect the service qualify and component life significantly. Residual stress can be determined by empirical or numerical experiments for selected configurations, even if both are expensive procedures. This paper presents a hybrid neural network that is trained using Simulated Annealing (SA) and Levenberg-Marquardt Algorithm (LM) in order to predict the values of residual stresses in cutting and radial direction after the MQL face turning process accurately. First, SA is used to train the weight and bias values of the ANN after which LM is used to fine tune the values trained by SA. Then, based on the predictions, an optimization procedure, using Genetic Algorithm (GA), is applied in order to find the best cutting conditions. At each generation, GA suggests a population of inputs that are then sent to the trained ANN in order to predict the residual stresses. The objective is to find the optimal inputs that minimize the tensile stress on the machined surface.

Surface roughness is an important outcome in the machining process and it plays a major role in the manufacturing system. Prediction of surface roughness has been a challenge to researchers because it is impacted by different machining parameters and the inherent uncertainties in the machining process. Prediction of surface roughness will benefit the manufacturing process to be more productive and competitive at the same time to reduce any pre-processing of the machined workpiece in order to meet the technical specifications. In this study, a hybrid GA-LM ANN is proposed for the prediction of surface roughness during roughing process in turning operation. To verify the performance of the proposed approach, the results are compared with the results obtained by training an ANN using GA or LM. The results have shown that the hybrid ANN outperformed the other two algorithms.

Surface integrity, such as surface roughness and residual stress, is an aspect of surface quality on machined parts. Residual stress in the machined surface and subsurface is affected by materials, machining conditions, and tool geometry. These residual stresses could affect the service qualify and component life significantly. Residual stress can be determined by empirical or numerical experiments for selected configurations, even if both are expensive procedures. This paper presents a hybrid neural network that is trained using Simulated Annealing (SA) and Levenberg-Marquardt Algorithm (LM) in order to predict the values of residual stresses in cutting and radial direction after the MQL face turning process accurately. To verify the performance of the proposed approach, the predicted results are compared with the results obtained by training an ANN using SA and LM separately. The results have shown that the hybrid neural network outperforms SA and LM in predicting machining induced surface integrity that is critical to determine the fatigue life of the components.