Thomas Auerbach

Self-optimising Production Systems

One of the central success factors for production in high-wage countries is the solution of the conflict that can be described with the term “planning efficiency”. Planning efficiency describes the relationship between the expenditure of planning and the profit generated by these expenditures. From the viewpoint of a successful business management, the challenge is to dynamically find the optimum between detailed planning and the immediate arrangement of the value stream. Planning-oriented approaches try to model the production system with as many of its characteristics and parameters as possible in order to avoid uncertainties and to allow rational decisions based on these models. The success of a planning-oriented approach depends on the transparency of business and production processes and on the quality of the applied models. Even though planning-oriented approaches are supported by a multitude of systems in industrial practice, an effective realisation is very intricate, so these models with their inherent structures tend to be matched to a current stationary condition of an enterprise. Every change within this enterprise, whether inherently structural or driven by altered input parameters, thus requires continuous updating and adjustment. This process is very cost-intensive and time-consuming; a direct transfer onto other enterprises or even other processes within the same enterprise is often impossible. This is also a result of the fact that planning usually occurs a priori and not in real-time. Therefore it is hard for completely planning-oriented systems to react to spontaneous deviations because the knowledge about those naturally only comes a posteriori.

Intelligent Assembly for Aero Engine Components

In this paper, an intelligent assembly system for a welding process of aero engine components is presented. In contrast to present approaches, the proposed assembly unit is able to automatically align and clamp different rotary components. Furthermore, an optimised orientation of the parts for the adjacent welding process is realised, taking the geometric information of each part into consideration. An optical measurement system is used to orientate the individual components to each other. The controller of the alignment system is implemented by using LabVIEW as a graphical software solution.

Meta-modeling for manufacturing processes

Meta-modeling for manufacturing processes describes a procedure to create reduced numeric surrogates that describe cause-effect relationships between setting parameters as input and product quality variables as output for manufacturing processes. Within this method, expert knowledge, empiric data and physical process models are transformed such that machine readable, reduced models describe the behavior of the process with sufficient precision. Three phases comprising definition, generation of data and creation of the model are suggested and used iteratively to improve the model until a required model quality is reached. In manufacturing systems, such models allow the generation of starting values for setting parameters based on the manufacturing task and the requested product quality. In-process, such reduced models can be used to determine the operating point and to search for alternative setting parameters in order to optimize the objectives of the manufacturing process, the product quality. This opens up the path to self-optimization of manufacturing processes. The method is explained exemplarily at the gas metal arc welding process.

Self-optimizing Production Technologies

Customer demands have become more individual and complex, requiring a highly flexible production. In high-wage countries, efficient and robust manufacturing processes are vital to ensure global competitiveness. One approach to solve the conflict between individualized products and high automation is Model-based Self-optimization (MBSO). It uses surrogate models to combine process measures and expert knowledge, enabling the technical system to determine its current operating point and thus optimize it accordingly. The objective is an autonomous and reliable process at its productivity limit. The MBSO concept is implemented in eight demonstrators of different production technologies such as metal cutting, plastics processing, textile processing and inspection. They all have a different focus according to their specific production process, but share in common the use of models for optimization. Different approaches to generate suitable models are developed. With respect to implementation of MBSO, the challenge is the broad range of technologies, materials, scales and optimization variables. The results encourage further examination regarding industry applications.

Concept for a Technology Assistance System to Analyze and Evaluate Materials and Tools for Milling

Turbine engine manufacturers permanently aim to improve the efficiency of their products. This is often accompanied by the development of new materials which have to be introduced to manufacturing. As a consequence, engineers responsible for machining process development are regularly confronted with the question, how to identify the optimal machining conditions in order to deal with the new constraints. Nowadays, the effort and success of such identification processes are to a significant degree depending on technology expert skills and experiences. From the process planning perspective, however, this circumstance is characterized by a significant degree of uncertainty.This article presents an innovative concept for a technology assistance system (TAS) for milling. The TAS supports the operator to determine optimal machining conditions by autonomously evaluating machinability criteria such as cutting force, tool wear or surface roughness for certain work piece material/ tool combinations. This includes the planning and organization of milling experiments, its standardized and automated execution as well as the generation of surrogate models to describe the machinability criteria for a given parameter range, serving as input for a future optimization. All functionalities of the TAS are conceptually described and first results achieved using a prototype solution are introduced and discussed.© 2015 ASME

Digitales Technologiewissen durch intelligente Fertigungssysteme

Kurzfassung Ein integraler Bestandteil von innovativen Systemansätzen für die Produktion von morgen ist digitales Technologiewissen. Dieses Wissen kann über Modelle, die prozessspezifische Zusammenhänge mathematisch beschreiben, bereit gestellt werden. Im vorliegenden Artikel wird das grundsätzliche Vorgehen zur Entwicklung solcher Modelle kurz erläutert. Darauf aufbauend wird ein Konzept zur autonomen Wissensgenerierung vorgestellt. Damit wird ein Fertigungssystem befähigt, digitales Technologiewissen selbstständig aufzubauen.

Approaches of Self-optimising Systems in Manufacturing

Within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” one major focus is the research and development of self-optimising systems for manufacturing processes. Self-optimising systems with their ability to analyse data, to model processes and to take decisions offer an approach to master processes without explicit control functions. After a brief introduction, two approaches of self-optimising strategies are presented. The first example demonstrates the autonomous generation of technology models for a milling operation. Process knowledge is a key factor in manufacturing and is also an integral part of the self-optimisation approach. In this context, process knowledge in a machine readable format is required in order to provide the self-optimising manufacturing systems a basis for decision making and optimisation strategies. The second example shows a model based self-optimised injection moulding manufacturing system. To compensate process fluctuations and guarantee a constant part quality the manufactured products, the self-optimising approach uses a model, which describes the pvT-behaviour and controls the injection process by a determination of the process optimised trajectory of temperature and pressure in the mould.

Prozessüberwachung für zertifizierte Fertigungsprozesse in der Luftfahrtindustrie

Kurzfassung Der vorliegende Artikel beleuchtet die aktuelle Situation der Fertigung von sicherheitskritischen Bauteilen in der Triebwerksindustrie. Entscheidend für die Bauteilqualität ist in erster Linie die Oberflächenintegrität. Diese wird in der Produktion derzeit mit Hilfe klassischer nicht-zerstörender Prüfverfahren verifiziert. Neue Ansätze bedienen sich der Prozessüberwachung, die ein großes Potenzial im Bereich der prozessbegleitenden Qualitätssicherung bietet. Ein erster Prototyp für eine industrienahe Lösung zur Prozessüberwachung beim Bohren wird in diesem Artikel vorgestellt.

Integrative Business and Technology Cases

In order to strengthen the relevance and integrativity of research in the Cluster of Excellence, current best practice “business and technology cases” were selected. Hereby the theories, hypotheses, predictions and technology projects developed in the Cluster of Excellence are evaluated and advanced in close collaboration with leading production companies in Germany and Europe. To make the work more transparent, different application scenarios will be developed.

Business and Technology Cases

Um die Relevanz und Integrativitat der Forschungsarbeiten im CoE zu verstarken, wurden aktuelle Best Practice „Business and Technology Cases“ ausgewahlt. Hierdurch werden die erarbeiteten Theorien, Hypothesen, Pradiktionen und Technologieansatze in Zusammenarbeit mit fuhrenden Produktionsunternehmen aus Deutschland und Europa uberpruft und weiterentwickelt. Um die durchzufuhrenden Arbeiten transparent zu machen, werden ferner unterschiedliche Anwendungsszenarien entwickelt.