Manik Rajora

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.

Optimization of Tensile Residual Stress on Machined Surface in MQL Turning

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.

A new algorithm based on evolutionary computation for hierarchically coupled constraint optimization: methodology and application to assembly job-shop scheduling

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.

A Hybrid RF-GA Approach to Bottleneck Machine Diagnosis and Suggestion in Parallel Machine Job-Shop Scheduling

In this paper, a hybrid Random Forest-Genetic Algorithm approach to detect and solve bottleneck machine problems in parallel machine Job-shop scheduling is developed with the aim of minimizing the makespan and the additional cost. The drawbacks of the existing methods for diagnosing bottlenecks is that they either do not consider the severity of the bottleneck or they do not consider the existence of multiple bottlenecks. In the existing models for solving bottlenecks, the cost is not considered as an objective function and only shifting of bottlenecks is utilized to solve the bottleneck machine problem. This approach is not feasible if the maximum capacity of the workshop has been reached. In this paper, a Random Forest classification model is utilized to diagnose bottleneck machine with different severity where the severity of the machines on the shop floor can either be none, low, medium, or high. Due to the lack of historical data, the Random Forest algorithm is trained using bottleneck data generated by simulating several identical parallel machine Job-shop scheduling problems. The trained Random Forest algorithm is then used in conjunction with Genetic Algorithm for finding the optimal actions to be taken for the most severe bottlenecks machines in order to reduce the makespan and the additional cost by optimizing the number of additional parallel machines to be utilized and overtime hours for the most severe bottleneck machines. The two objectives, makespan and additional cost, are combined into a single objective value by the use of weight values. These weight values depend on severity of the most severe bottleneck machine. If the bottleneck severity is “high” then makespan has a higher weight value than cost, if the severity is “medium” then both cost and makespan are weighed equally, and if the severity is “low” then cost has a higher weight value than makespan. In order to show the validity of the proposed approach it is used for diagnosing and solving the bottleneck problems in three different identical parallel machine Job-shop scheduling case studies 1. 3 jobs with 6 machines 2. 5 jobs with 9 machines and 3. 5 jobs with 12 machines. By utilizing the proposed approach the makespan and cost were reduced by 19.0%, 24.5% and 25.4% in case studies 1, 2, and 3 respectively. The results show that the trained Random Forest algorithm was able to correctly diagnose the bottleneck machines and their severity and Genetic algorithm was able to find the optimal number of additional hours and additional machines for the most severe bottleneck machines on the shop floor.© 2016 ASME

A Hybrid Neural Network for Prediction of Surface Roughness in Machining

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.

A Hybrid Neural Network for Prediction of Surface Residual Stress in MQL Face Turning

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.