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In this paper, the optimization of cutting parameters for constrained machining operations is reported. Modified genetic algorithm (MGA) is an evolutionary computation technique that has been proposed in this paper to solve the machining problem. Additional constraints have been incorporated to the multipass turning model. The optimization of drilling and facing parameters have also been carried out. To demonstrate the procedure and performance of the approach, an illustrative example is discussed. The results of the proposed algorithm are compared with other traditional and non-traditional techniques such as Newton’s method, hill climbing and ants colony technique. [ABSTRACT FROM AUTHOR]

Considering alternative machines for operations, forbidden intervals during which machines cannot be available and a job’s batch size greater than one in the real manufacturing environment, this paper studies the batch splitting scheduling problem on alternative machines with forbidden intervals, based on the objective to minimize the makespan. A scheduling model is established, taking before-arrival set-up, processing, and transfer time into account. And a new hybrid parallel algorithm, based on differential evolution and genetic algorithm, is brought forward to solve both the batch splitting problem and the batch scheduling problem by assuming a common number of sub-batches in advance. A solution consists of the actual optimum number of sub-batches for each job, the optimum batch size for each sub-batch, and the optimum sequence of operations for these sub-batches. Experiments on the performance of the proposed algorithm under different common numbers of sub-batches are carried out. The results of simulations indicate that the algorithm is feasible and efficient. [ABSTRACT FROM AUTHOR]

This paper considers an interesting topic of recipe qualification during each fabrication step of semiconductor manufacturing. In particular, this type of stochastic optimization scheme within a discrete-event simulation process is discussed and a new optimization method, with the capability to anchor robust recipes for batch processing, is proposed, implemented and evaluated. A particular real-coded genetic algorithm (GA) suitable for resolving continuous optimization problems in noisy environments is developed. Four test functions appearing in the literature are used as a test-bed to assess the proposed genetic algorithm via a variety of experimental studies, showing that the new method can produce much more accurate estimates of the true optimum points than the other two optimization procedures, the Nelder-Mead (NM) simplex search procedure and a well-known variant (RS+S9) of Nelder-Mead suitable for stochastic optimization. As such, the new method could serve as a useful tool for process recipe optimization in noisy semiconductor manufacturing environments. Finally, the chemical mechanical planarization (CMP) process, a turnkey process during semiconductor fabrication, is simulated from batch to batch based on the real-data equipment model and the presented algorithm is employed to seek the optimal recipe profile while processing each batch of wafers sequentially through the CMP tool. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) have been widely applied for many non-polynomial hard optimisation problems, such as flow shop and job shop scheduling. It is well known that the efficiency and effectiveness of a GA is highly depend on its control parameters, but setting suitable parameters often involves tedious trial and error. Currently, setting optimal parameters is still a substantial problem and is one of the most important and promising areas for GAs. In this paper, the determination of optimal GA control parameters with limited computational effort and simulation replication constraints, namely, population size, crossover and mutation probabilities, is firstly formulated as a stochastic optimisation problem. Then, the ordinal optimisation (OO) and the optimal computing budget allocation (OCBA) are applied to select the optimal GA control parameters, thereby providing a reasonable performance evaluation for hard flow shop scheduling problems. The effectiveness of the methodology is demonstrated by simulation results based on benchmarks. [ABSTRACT FROM AUTHOR]

Quality and performance of the products requiring higher precision and involving moving-parts mainly depends on the surface finish and dimensional accuracy. Generally, finishing operations use fine abrasive particles in different forms along with some carrier or binding medium. Finishing operations are crucial, expensive, uncontrollable, and a labor-intensive phase in the overall production, and contribute significantly to the total production time and cost. As surface finish requirement increases, the cost of finishing operations increases exponentially. Though progress has been made in automating the finishing operations to reduce the production time to some extent, it increases the initial investment and operating costs significantly. Quality, cost, time, and efficiency of finishing operations can be improved significantly by choosing the optimum values of the process parameters. This paper presents the details of process parameters optimization of two advanced fine-finishing processes namely abrasive flow machining (AFM) and magnetic abrasive finishing (MAF), which are capable of giving nano-level surface finish, using real-coded genetic algorithms (GA). It also describes the development of a surface roughness model that was developed to form the objective function for the optimization of AFM process. [ABSTRACT FROM AUTHOR]

Conventional tolerance analysis is tedious and time consuming, which makes engineers resist doing it. Complex assembly problems are generally beyond the capabilities of most design and manufacturing engineers. In this paper, genetic algorithm, a kind of non-traditional optimization technique is used as the basic foundation for optimal tolerance allocation to help design and manufacturing engineers to overcome the shortcomings in the conventional tolerance stack analysis and allocation system. [ABSTRACT FROM AUTHOR]

In an advanced manufacturing system, accurate assessment of tool life estimation is very essential for optimising the cutting performance in turning operation. Estimation of tool life generally requires considerable time and material and hence it is a relatively expensive procedure. In this present work, back-propagation feed forward artificial neural network (ANN) has been used for tool life prediction. Speed, feed, depth of cut and flank wear were taken as input parameters and tool life as an output parameter. Twenty-five patterns were used for training the network. Recently there have been significant research efforts to apply evolutionary computational techniques for determining the network weights. Hence an evolutionary technique named particle swarm optimisation has been used instead of the back-propagation algorithm and it is proved that the experimental results matched well with the values predicted by both artificial neural network with back-propagation and the proposed method. It is found that the computational time is greatly reduced by this method. [ABSTRACT FROM AUTHOR]