A dynamic scheduling tool and a methodology for creating digital twin of manufacturing systems for achieving Zero Defect Manufacturing
Over the years, the manufacturing industry has seen constant growth and change. From one side, it has been affected by the fourth industrial revolution (Industry 4.0). From the other side, it has had to enhance its ability to meet higher customer expectations, such as more customized products in a shorter time. Those factors have led many manufacturing companies to produce new products faster than ever for two main reasons: achieving higher profits and meeting increasing demand from their
... ers. This phenomenon has imposed new rules on the manufacturing of products, such us producing in a shorter time and smaller batches, making strategies that had been successful in the past useless or not as efficient as required. In the contemporary competitive market of manufacturing, quality is a criterion of primary importance for winning market share. Quality improvement must be coupled with a performance point of view. One of the most promising concepts for quality control and improvement is called zero defect manufacturing (ZDM), which utilizes the benefits from Industry 4.0 technologies. ZDM imposes the rule that any event in the production should have a counter-action to mitigate it. Specifically, in this thesis, a systematic literature review was performed on the ZDM concept from 1987 to 2017 to summarize the state of the art and highlight shortcomings and further directions in research. Accordingly, the ZDM implementation methods were investigated and evaluated identifying the main research patterns in the sample by analyzing key factors. Based on the extensive review of the ZDM literature, we identified and highlighted four distinct strategies based on overarching themes for ZDM, namely detection, repair, prediction, and prevention. The goal of this research was twofold: first, it aimed to provide to manufacturers with a dynamic scheduling toοl that embraces the principles of ZDM, which would grant the opportunity to implement ZDM strategies in their production lines and simultaneously maintain the performance of the production system at an acceptable level. The integration of ZDM into the scheduling tool was achieved by creating a separate component for each one of the four ZDM strategies. The second goal was focused on creating a methodology for the manufacturer to correctly select the appropriate ZDM strategies to implement at each manufacturing stage. This methodology consists of several steps. The first step is to conduct several simulations using the developed scheduling tool with specific data sets. The data sets are created using the design of experiments methodology. Using the results of the experiments, a digital twin model is created for predicting the results of the developed scheduling tool without using said tool. Using the digital twin model, multiple ZDM parameter-combination sets are created and plugged into the model. The outcome of this process will generate a set of maps that show the performance of each ZDM strategy at each manufacturing stage. These maps are intended to provide information for comparing different ZDM-oriented equipment to reach a final decision for correct and efficient ZDM implementation.