A. Pavim

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.

Self-optimizing assembly of laser systems

Laser assembly can be a tedious task if performed manually. Especially if miniaturization of the laser is desired, robot-based assembly can greatly improve quality, performance and throughput, while self-optimization is regarded as a strategy to reduce planning efforts and increase the robustness of the assembly. An automated laser assembly system has been developed together with a concept to increase the autonomy through a multi-agent system control structure. The multi-agent system allows assembly steps like sequence planning, measurement of components and deviations, selection of components, soldering elements onto a carrier plate and active resonator alignment to be handled by the system itself and enables the assembly system to uniquely plan every laser system and execute its assembly within a flexible robot-based assembly cell.

Flexible optical metrology strategies for the control and quality assurance of small series production

The demand for achieving smaller and more flexible production series with a considerable diversity of products complicates the control of the manufacturing tasks, leading to big challenges for the quality assurance systems. The quality assurance strategy that is nowadays used for mass production is unable to cope with the inspection flexibility needed among automated small series production, because the measuring strategy is totally dependent on the fixed features of the few manufactured object variants and on process parameters that can be controlled/compensated during production time. The major challenge faced by a quality assurance system applied to small series production facilities is to guarantee the needed quality level already at the first run, and therefore, the quality assurance system has to adapt itself constantly to the new manufacturing conditions. The small series production culture requires a change of paradigms, because its strategies are totally different from mass production. This work discusses the tight inspection requirements of small series production and presents flexible metrology strategies based on optical sensor data fusion techniques, agent-based systems as well as cognitive and self-optimised systems for assuring the needed quality level of flexible small series. Examples of application scenarios are provided among the automated assembly of solid state lasers and the flexible inspection of automotive headlights.

Cognitive Production Metrology: A new concept for flexibly attending the inspection requirements of small series production

The current trend for product individualisation and customer satisfaction results in a demand for smaller and flexible production series with a considerable diversity of components. This paper discusses the inspection requirements of small series production and presents the new concept of Cognitive Production Metrology (CPM) as an innovative solution to increase the manufacturing efficiency within flexible production lines. This is intended to contribute directly to reducing the complexity of pilot production series, for speeding up the production start time and assuring a maximum quality level for the process and product in dynamic environments. Fundamental tools for the conception of cognitive and autonomous quality assurance systems, such as agent-based and knowledge-based systems, as well as the use of different and combined measurement and inspection systems are introduced in an example scenario at the end.

Multiagent-based approach for the automation and quality assurance of the small series production

The dynamic conditions of global markets force manufacturers to invest in flexible production strategies to cope with demanding clients and still survive in a competitive economic scenario. In this sense, small series production appears as a trend for many manufacturing niches and brings many challenges regarding manufacturing and quality assurance aspects. Investing in production flexibility implies increasing production control complexity and planning. This flexibility usually does not correlate with higher degrees of manufacturing automation or with quality assurance strategies. The concept of Cognitive Metrology strives for handling the challenging automation and quality inspection requirements of small series production with a new approach based on self-optimizing systems. This paper introduces the concepts of self-optimization and Cognitive Metrology and focuses especially on a multiagent-based approach for supporting flexible automation and quality assurance in small series production, as a basis for the development of the Cognitive Metrology technology. Initial results of the application of this approach into industrial prototypes are introduced and discussed as well as the migration of this system to different industrial scenarios.

Machine Vision System for Inline Inspection in Carbide Insert Production

In steps of the production chain of carbide inserts, such as unloading or packaging, the conformity test of the insert type is done manually, which causes a statistic increase of errors due to monotony and fatigue of the worker and the wide variety of the insert types. A machine vision system is introduced that captures digital frames of the inserts in the production line, analyses inspects automatically and measures four quality features: coating colour, edge radius, plate shape and chip-former geometry. This new method has been tested on several inserts of different types and has shown that the prevalent insert types can be inspected and robustly classified in real production environment and therefore improves the manufacturing automation.

Transparency in Production by Monitoring the Condition of Molds, Dies and Machines

Deep drawing and injection molding play an important role in industrial production processes for many products. For competitive processes, the availability is a major leverage. The approach described in this paper uses the application of sensors on molds, dies and machines to monitor the condition and thereby provide a basis for internal and external services such as condition based maintenance and an accelerated try-out period. Sensors that offer the metrological basis for condition based surveillance are available, but are not sufficiently deployed in production, yet. Research has to be done to investigate effective and efficient combinations of different sensors applied to the production systems. Within this approach the signals of sensors are combined to gather relevant information of the tools’ and machines condition to increase the availability of the complete manufacturing system. For deep drawing and injection molding, relevant condition data can be gathered and transformed in continuative steps to valuable information and knowledge about the condition to improve the availability of the analysed manufacturing processes.