Abstract
The performance of any production system is highly dependent on processing equipment that is free of faults and breakdowns. This can be achieved through maintenance planning and statistical quality control. The aim of this research is to develop a model for statistical quality control with an integrated optimization-based maintenance model for multicomponent series systems using an exponentially weighted moving average chart. The optimization model is based on both the preventive and corrective maintenance policies. The developed model is used to find the optimal values of sample size, sample frequency, width of control chart limit in units corresponding to the standard deviation, and interval of preventive maintenance while minimizing the total expected cost of the system per unit time. The study included a case study example to demonstrate its applicability and to analyze the influence of cost parameters on the developed integrated model. Finally, sensitivity analysis was conducted to demonstrate the effectiveness of the proposed model.
Highlights
The performance of a production system is significantly affected by the breakdown-free operations of equipment and processes
COST MODEL METHODOLOGY AND OPTIMIZATION To demonstrate the benefits of integrated preventive maintenance (PM) and SPC with the exponentially weighted moving average (EWMA) control chart, the section presents the cost model development to capture the costs correlated with process manufacturing that are affected by maintenance planning and quality control policies
In this study, an EWMA chart was used for monitoring process quality control
Summary
The performance of a production system is significantly affected by the breakdown-free operations of equipment and processes. Panagiotidou and Tagaras [17] proposed a model with an age-based PM policy for maintenance procedure optimization for a production process with two states of quality, namely process quality failure and shift. This study presents an integrated model to determine the optimal expected total cost of corrective maintenance (CM), PM, process failures, sampling, and inspection by jointly optimizing maintenance procedures and quality control chart parameters. The main contribution of this study is the implementation of a new integrated approach to optimize maintenance actions and process control policies to classify machine failures. Whenever a quality shift is detected due to external reasons, a resetting of the process is initiated to return the operation to an in-control state This type of maintenance action has double the benefits: eliminating costs related to out-of-control operations and machine degradation and improving machine reliability by protecting it against failures.
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