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Disruptions in manufacturing processes can cause severe impacts on process efficiency and down time that adversely affect production costs. These anomalies/disruptions, in any automated process, have negative impacts on consumer centric values, high rejection of raw materials and overall cost. Mostly the effects of disruptions are propagated and cascaded in the whole assembly line by blockage and starvation caused by disruptive workstation. The accurate analysis of the aftermath of each disruption is the key to proper allocation of resources and downtime mitigation. Process down time can be reduced by monitoring manufacturing processes for faults, categorizing those faults and prioritizing actions. Disruption handling has been the aim of many researchers of the past decade. Many of these techniques have been fruitful in many aspects, nevertheless these techniques are based on a static mathematical models and pre-defined manner. This practice does not consider the process dynamically based on process status and sometimes becomes the source of false alarms, batch rejection, increase in the overall cost decrease in process efficiency. The issues relating disruptions and their solutions has been the primary aim of the presented work. Four subsequent models have been developed pertaining to disruptions. The first model in this scenario is Agent Based Fault Tolerant Framework. This model dynamically evaluates disruptions based on the process weightage. Human immune system presents an astounding grounds and example for developing Artificial Immune system, wherein the disruptions caused by viruses and bacteria is addressed by deploying B and T cells, which either destroys the pathogen (virus or bacteria) by killing it (Phagocytosis) or renders it harmless. Inspired from that, a second model analogous to Human Immune System is developed for industrial applications, to deal with disruptions. Flexibility in the Alarm Trip Point (ATP) is yet another fascinating technique that introduces flexibility in the defined ATP based on the statuses of adjacent workstations. The implementation of this model shows promising results. Finally, an augmented model has been developed which combines first and second model to overcome some of the limitations of previous models and is implemented on a test rig. This model presented outstanding results in comparison to the established approaches.
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