Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

Filters

2
2
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    An integrated approach for object-oriented assembly system planning and assembly control
    ( 1996)
    Chang, K.-W.
    ;
    Hsieh, L.-H.
    ;
    Schreyer, M.
    Characteristics such as decentralized, autonomous, self-planning, self-controlled, and teamwork describe the trends or the nature of modern assembly systems which are usually hybrid. These accompanied with the continuously increasing number of variants and shorter product life cycle require reusable assembly units which are flexible and modular, as well as an efficient job order processing. The system should be free configurable based on the new product structure and features, and the assembly control must integrate the available information used and generated the system planning. This paper presents an integrated approach, starting from product structure to design an object-oriented assembly system model, and based on this computer-internal model to control the assembly sequence and operation according to the job orders. The characteristics of modern assembly will be summarized. The requirements on the planning of such an assembly system and assembly control are postulated. An archite cture for integrated assembly system planning and control will be described. The objects for such a system are then derived. The applicability of this approach will be shown on a concrete example of a subassembly from a transportation vehicles manufacturer.
  • Publication
    Automated learning system for control and supervision of assembly systems
    ( 1995)
    Groth, A.
    ;
    Hsieh, L.-H.
    ;
    Seliger, G.
    Automation has not been applied as widely in assembly as in other manufacturing areas, mainly due to the complexity of operations requiring control of many geometric and technological process parameters. This paper shows that automated learning can be applied to control and supervision of assembly systems for which only a qualitative process model exists. A qualitative process model is built from incomplete a-priori knowledge of processes, facilities and products including essential performance targets. Automated learning algorithms determine optimum process control and supervision strategies based on performance of the assembly system. The process model is updated in real time according to the process results. Critical parameters can be identified and supervision strategies optimized. A prototypical automated bonding system will serve as a practical example showing how automated learning can help determining and verifying control strategies.