Abstract-The simulation of a jet engine behavior is widely used in many different aspects of the engine development and maintenance. Achieving high quality jet engine control systems requires the iterative use of these simulations to virtually test the performance of the engine avoiding any possible damage on the real engine. Jet engine simulations involve the use of mathematical models which are complex and may not always be available. This paper introduces an approach based on Genetic Programming (GP) to model different parameters of a small engine for control design such as the Exhaust Gas Temperature (EGT). The GP approach has no knowledge of the characteristics of the engine. Instead, the model is found by the evolution of models based on past measurements of parameters such as the pump voltage. Once the model is obtained, it is used to predict the behaviour of the jet engine one step ahead. The proposed approach is successfully applied for the simulation of a Behotec j66 jet engine and the results are presented.
With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology and visualisation tool based on data analytics that allows the practitioner to anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine's settings are needed (tool replacement, repositioning the axis, etc). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.
<div class="section abstract"><div class="htmlview paragraph">Aerospace manufacturing is improving its productivity and growth by expanding its capacity for production by investing in new tools and more equipment to provide additional capacity and flexibility in the face of widespread supply disruptions and unpredictable demand. However, the cost of such measures can result in increased unit costs. Alternatively, productivity and quality can be improved by utilizing available resources better to reach optimal performance and react to emerging disruptions and changes. Elastic Manufacturing is a new paradigm that aims to change the response behavior of firms to meet sudden market demands based on automated analysis of the utilization of the available resources, and autonomous allocation of capacity to use resources in the most efficient manner. Through digitalization of the shopfloor, streaming data from equipment enables companies to identify areas for improvement and boost the efficiency without large capital expenditure. Additionally, the impact of supply chain disruptions can be reduced through demand forecasting, inventory optimization, early warning systems, and flexible reallocation of resources; all of which could be managed elastically through integrated data collection in the supply chain. This paper describes how smart factories with more flexibility and resilience can be achieved with semantically-enhanced quality analytics, maintenance solutions, and automated key performance indicator monitoring. An example of measuring the capacity utilization rate, by following the measurement of multiple KPIs from a shopfloor level using data from a real aerospace project is demonstrated showing the significance of monitored process performance.</div></div>
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