Production Control

The integration of autonomous control methods into central PPC-systems enables a high logistics target achievement despite occurring dynamic influences. Thereby, the integration requires coupling strategies to specify the harmonisation of autonomous control and central planning methods. The logistics target achievement and the adherence to given planning parameters depend on the specific strategy and the related level of autonomy. This paper introduces a target system which enables the evaluation of coupling strategies considering both logistics target achievement and the adherence to given planning para-meters. Thereby, producing companies are enabled to consider both aspects for the selection of suitable planning and control methods.

One of the main purposes of the „Smart Virtual Worker“-project is the application of a digital human model for the simulation of industrial work tasks. This contribution focuses on the implementation of an autonomic action selection, such that a virtual agent is able to solve tasks under certain optimization criteria. To realize the autonomic action selection, we use the Q-Learning algorithm with different extensions. In this article, we describe these different learning algorithms and we briefly describe the performance of their implementation with regard to the industrial field.

To remain competitive in a globalized market, enterprises must consider sustainably aspects. The classic aims of quality, costs and time are extended by aspects of the ecological, economic and social quality. The challenge to cope with these new dimensions aiming at long term success is raised by the turbulent influences in a global market with varying demands. A new factory typology, the so called urban factory, pursues the aim to exhaust the potentials of an interlinking of factory and town efficiently. The aim of this topology is to improve the competitive situation of enterprises. In this case the synergetic use of material and energy sources is in focus.

Due to a variety of technological developments within the past decade, today’s industrial working environment is changing dramatically. Information and communication technologies as well as automation and robotics are penetrating the working environment. In the context of industrial 4.0, the interaction of systems increases. Sensor data, status information and control commands are exchanged, which creates the conditions for new types of production concepts such as Remote Manufacturing. Remote Manufacturing describes a delocalized production, which removes the spatial unity of production factors. It will be no longer necessary for the elementary production factors (labor, materials, equipment) and the dispositional production factors (planning, management, control, organization) to be located at one place. In the future, it will be possible to operate production facilities abroad and to monitor the process control level from Germany. In this paper, the economic environment and the ...

The networking between companies in a supply chain becomes tighter. This applies for manufacturing plants and the supply with manufacturing equipment as well. Hence, the complexity of the flow of information, in particular for tool management, increases. Currently the exchange of information is mostly paper-based and tool data is not available continuously along the supply chain. By using a digital and cloud-based tool management system, breaks in the flow of information along the supply chain for machining tools can be overcome. Herewith tool data can be called and updated ongoing and location-independent. Furthermore, after clearly identifying a tool, required tool data can automatically be transferred into the control system of the machine.

The upcoming high-throughput technologies are a new challenge for personnel and facilities in life science. These new technologies are enabling the acquisition of extensive knowledge in medical and pharmaceutical research. However, the new requirements can only be met by lab automation solutions. The research work of Fraunhofer IPA in the area of lab automation shows that the conditions in the lab are subject to dynamic effects and thus likewise lab automation solutions are inquired. The requested flexibility is currently unavailable on the market; instead there is a multitude of proprietary isolated applications. The necessary integration of single devices for the coverage of complex biochemical protocols in particular, is contrarian to user requirements due to high efforts in both expenses and time. An alternative to the proprietary isolated applications and the associated integration effort is given by a common communication standard for the cross linking of lab devices. Researchers ...

In the future, objects with embedded intelligence will be able to coordinate and steer the production sequence in a self-organizing production environment. Instead of existing central planning and control the new product-controlled manufacturing uses a multi-agent system with the possibilities of auctions and negotiation. The project “Self-organizing Production  SOPRO” implements autonomous micro systems and software agents to provide embedded intelligence on objects in production field.

In the research project LUPO a hybrid simulation laboratory is build up. This combines the advantages of the computer simulation with those of a model factory. For the systematic recording, mapping and analysis of various production processes a procedure model has been created that responds to the special requirements (quick, flexible, effortless determination of informative results) of the project. A library of production objects facilitates the reutilisa tion of existing machines and hereby a reduction of simulation effort.

In a world of steady change, manufacturing companies face major challenges. They have to react quickly to an increasing competition and more and more individual customer requests. New technologies and organisational structures like RFID or decentralised and real-time based production control can deal with this issue successfully. The specific realization of selection and analysis of each production process of the company often approves to be difficult and cost intensive. Previous simulation approaches are frequently combined with high efforts in order to state credible propositions regarding suitability and economic efficiency. The present paper shows a new opportunity towards managing these disadvantages. Additionally, the advantages are combined with computer-based simulations of the model factory. A fast and flexible illustration of the analysed production processes is achieved.

Dynamics and complexity of today`s production systems bring established approaches for production planning and control to their limits. Accordingly, developing new concepts and methods is a key point for research in this area. The combination of a decentralized control structure and innovative methods from the field of artificial intelligence seems promising here. Open source tools have proven their applicability to implement those methods. They are disposable and can be flexibly adapted to many problems. This contribution introduces an approach for the decentralized control of a shop floor. Here, artificial neuronal networks are used as adaptive control instruments. The simulation of these networks is performed with the open source tool Stuttgart Neural Network Simulator (SNNS) and its successor Java Neural Network Simulator (JNNS).