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This paper deals with the issue of economic batch quantity (EBQ) in a single machine system in which defective items are produced in each cycle of production. The accumulated defective items produced in a period, consisting of several equal cycles, are all reworked in the last cycle of this period called the rework cycle. At the end of each period the whole process will start all over again. We assume that there is a limitation on the total defective items. We also do not adopt the restriction that the rework process rate be equal to the normal production rate. In addition we assume that there is a set up time for rework process. Further we assume that the number of rework cycles be as small... 

This paper considers economic batch quantity (EBQ) in a single machine system in which defective items are produced and are all reworked in the same cycle. In the literature of inventory control, when the rework is done in the same cycle, the setup times are not considered; the rework process starts immediately after each production run, and the waiting of defectives for rework is ignored. Considering these points we assume non-zero setup times for rework, delayed rework in the same cycle, and the cost of waiting, to obtain the EBQ for two different cases  

Statistical process control provides useful tools to improve the quality of multistage machining processes, specifically in continuous manufacturing lines, where product characteristics are measured at the final station. In order to reduce process errors, variation source identification has been widely applied in machining processes. Although statistical estimation and pattern matching-based methods have been utilized to monitor and diagnose machining processes, most of these methods focus on stage-by-stage inspection using complex models and patterns. However, because of the existence of high rate alarms and the complexity of the machining processes, a surrogate modelling is needed to solve... 

An integrated approach is presented for predicting granule particle size using Partial Correlation (PC) analysis and Artificial Neural Networks (ANNs). In this approach, the proposed model is an abstract form from the ANN model, which intends to reduce model complexity via reducing the dimension of the input set and consequently improving the generalization capability of the model. This study involves comparing the capability of the proposed model in predicting granule particle size with those obtained from ANN and Multi Linear Regression models, with respect to some indicators. The numerical results confirm the superiority of the proposed model over the others in the prediction of granule...