Application of Stochastic Regression for the Configuration of Microrotary Swaging Processes

In micromanufacturing, a precise adjustment of manufacturing, handling, and quality control processes constitutes an essential factor for success. The continuing miniaturization of workpieces and production devices results in ever decreasing tolerances, whereas machines and processes become increasingly more specialized. Thereby, the so-called size effects render the direct application of knowledge from the area of macromanufacturing impossible. In this context, this paper describes the application of the μ-ProPlAn method for the configuration of an infeed rotary swaging process for microcomponents. At this, the cause-effect relationships between relevant process parameters are analyzed using stochastic regression models, in order to determine cost-efficient process configurations for the manufacturing of bulk and tubular microcomponents

Dynamic adjustment of dispatching rule parameters in flow shops with sequence dependent setup times

Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence dependent setup times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs

Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system

Nowadays, material planning and control strategies are becoming continuously complex tasks spanning from individual plants to logistic networks. In fact, this is the consequence of increasing intricacy in product variants and their respective convolution in networks’ structures. Customers ask for specific products with individual characteristics that force companies for more clever performances by more flexibility. For doing so, the existing planning and control systems, which work based on central monitoring and controlling, show some limitations for organizing every operation on time or in the right time. Therefore, in the recent decade, a great attention is put on decentralized control and, to some extent, autonomy. This paper tries to investigate the possibility of combining this new research paradigm with existing strategies in production logistics, in order to improve material handling and control task according to material flow criteria. To show this, an exemplary plant after decoupling point out of a logistic network is considered for simulation and analysis. This combines Conwip system with learning autonomous pallets’ concept in a discrete event simulation model. Several decentralized control scenarios are experimented and compared together. Here, the learn methodology is brought to pallets based on fuzzy rules and advantage of closed loop systems

ScienceDirect Autonomous control in closed dynamic logistic systems

Abstract The material flow in dynamically changing logistic systems underlies specific conditions, as destinations and stopovers only exist temporarily. The order-dependent circulation of rental articles constitutes such a case, where the articles are on the move between the lender and one or more customers. The related planning and control processes are highly complex and challenging as they comprise the scheduling of orders, the compilation of transports and the determination of suitable routes. This paper introduces an autonomously controlled approach for the distribution of rental articles, including the autonomous decision-making and the representation of the involved objects as autonomous entities. A use case from the field of event logistics illustrates the proceeding for the integration of the approach in real world processes