Production Control

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).

The ongoing development of Radio Frequency Identification (RFID) opens up new possibilities to parallelize information and material flows. Manufacturing processes can be provided with real time information and new lifecycle services (e.g. definition of optimal cutting parameters, life cycle data, etc.) can be realized. In this paper the advantages of RFID on metal cutting tools are discussed. RFID on tools can support an efficient tool management and help to automate manual activities within the manufacturing process.

The continuously growing cost pressure, individual production orders as well as decreasing innovation cycles are only a few characteristics of the vital structural change that the production industry has been confronted with during the past years. In order to stand out against the competitors and to gain an advantage in competition, an enterprise has to have the ability to adjust to the dynamic demands of the internal and external environment. A contribution to that is a flexible control system for the material flow. This article presents a heuristic based algorithm for the hot rolling production scheduling problem of a steel manufacturer.

In recent years dynamics in the automotive industry has increased rapidly. Beside a higher degree of customer orientation product life cycles become continuously shorter, whereby an over-all effect on Ramp-up and Phase-out Management can be stated. Because of an increasing amount of new model ranges Ramp-up Management has been well discussed during the last few years. In contrast, discussions about an efficient Phase-out Management are mostly still missing. This paper presents an approach focusing on the transparency of information considering the requirements of Production Phase-outs.