Coupling of centralized and decentralized scheduling for robust production in agile production systems

Individualized products and timely delivery require agile just-in-time manufacturing operations. Scheduling needs to deliver a robust performance with high and stable results even when facing disruptions such as machine failures. Existing approaches often generate predictive schedules and adjust them reactively as disturbances occur. However, the effectiveness of rescheduling approaches highly depends on the available degrees of freedom in the predictive schedule. In the proposed approach, a centralized robust scheduling procedure is coupled with a decentralized reinforcement learning algorithm in order to adjust the required degrees of freedom for a maximally efficient production control in real-time

Prädiktiv-reaktives Scheduling zur Steigerung der Robustheit in der Matrix-Produktion

Due to the increasing individualization of products, manufacturing companies are offering more and more variants with decreased quantities per variant. In addition, customer demand is becoming more volatile and difficult to predict. The main challenge is to eco-nomically produce a fluctuating mix of variants with fluctuating total quantities. Matrix-Production systems aiming for a production in batch size 1 decoupled from a takt are therefore a current object of research. In addition to the design of these systems, an increasingly important role is filled by production planning and control, since the material flows in such production systems are highly complex. The state of research is characterized by a multitude of predictive-reactive methods for scheduling even in complex production systems. However, there is no approach that specifically considers robustness in predictive planning in order to enable reactive rescheduling to maintain the desired logistical performance despite unforeseen disruptions. Therefore, a method for predictive-reactive product control of matrix-structured produc-tion systems was developed in this thesis, which allows the determination of an optimal degree of robustness in predictive robust scheduling and thus enables an optimal mix of prevention and reaction in production control. The method consists of three parts: First, in predictive robust scheduling, a schedule is generated on the basis of the pro-duction program, in which a desired extent of slip times between processing steps is then inserted. The robust schedules are then carried out in a discrete-event simulation. In the event of longer disturbances, a rescheduling corridor is determined secondly, which indicates which processing steps of which orders must be rescheduled depending on the duration of the disturbance and the underlying schedule. The rescheduling corridors are then rescheduled thirdly in reactive rescheduling and the results are transferred to the discrete event simulation for reintegration. Reactive rescheduling uses reinforcement learning based on a decentralized Markov process to learn optimal selection strategies for orders depending on the station. The method was tested in an application for a concept of a flexible body-in-white production with a partner from the automotive industry. The developed method contributes to the understanding of the concept of robustness as well as to the application possibilities and limits of reinforcement learning in production control. To the author’s knowledge, the work is the first approach to integrate robustness considerations directly into predictive-reactive scheduling approaches in order to improve the logistical performance

Computational fluid dynamic and thermal stress analysis of coatings for high-temperature corrosion protection of aerospace gas turbine blades

The current investigation presents detailed finite element simulations of coating stress analysis for a 3-dimensional, 3-layered model of a test sample representing a typical gas turbine component. Structural steel, Titanium alloy and Silicon Carbide are selected for main inner, middle and outermost layers respectively. ANSYS is employed to conduct three types of analysis- static structural, thermal stress analysis and also computational fluid dynamic erosion analysis (via ANSYS FLUENT). The specified geometry which corresponds to corrosion test samples exactly is discretized using a body-sizing meshing approach, comprising mainly of tetrahedron cells. Refinements were concentrated at the connection points between the layers to shift the focus towards the static effects dissipated between them. A detailed grid independence study is conducted to confirm the accuracy of the selected mesh densities. The momentum and energy equations were solved, and the viscous heating option was applied to represent improved thermal physics of heat transfer between the layers of the structures. A discrete phase model (DPM) in ANSYS FLUENT was employed which allows for the injection of continuous uniform air particles onto the model, thereby enabling an option for calculating the corrosion factor caused by hot air injection. Extensive visualization of results is provided. The simulations show that ceramic (silicon carbide) when combined with titanium clearly provide good thermal protection; however, the ceramic coating is susceptible to cracking and the titanium coating layer on its own achieves significant thermal resistance. Higher strains are computed for the two-layer model than the single layer model (thermal case). However even with titanium only present as a coating the maximum equivalent elastic strain is still dangerously close to the lower edge. Only with the three-layer combined ceramic and titanium coating model is the maximum equivalent strain pushed deeper towards the core central area. Here the desired effect of restricting high stresses to the strongest region of the gas turbine blade model is achieved, whereas in the other two models, lower strains are produced in the core central zones. Generally, the CFD analysis reveals that maximum erosion rates are confined to a local zone on the upper face of the three-layer system which is in fact the sacrificial layer (ceramic coating). The titanium is not debonded or damaged which is essential for creating a buffer to the actual blade surface and mitigating penetrative corrosive effects. The present analysis may further be generalized to consider three-dimensional blade geometries and corrosive chemical reaction effects encountered in gas turbine aero-engines. Key words: Thermal coating; Silicon Carbide ceramic; ANSYS; Finite element stress analysis; CFD (computational fluid dynamics); mesh density; total deformation; erosion.</i