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Model-based increase of flexibility and robustness of an aerodynamic part feeding system for high-performance assembly

基于模型提高高性能装配的空气动力零件供给系统的灵活性和稳健性

基本信息

批准号：
243351293
负责人：
Professor Dr.-Ing. Peter Nyhuis
金额：
--
依托单位：
Institut für Fabrikanlagen und Logistik (IFA)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2013
资助国家：
德国
起止时间：
2012-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/243351293?language=en
关键词：
Model based increase flexibility robustness

项目摘要

To counter existing deficits of conventional feeding systems regarding feeding performance, reliability and variant flexibility, an aerodynamic part feeding system has been developed at the IFA. This system is particularly characterized by its feeding rate of up to 1,000 workpieces per minute and its high technical availability. The setting-up of the part feeding system to different workpieces is limited to the adaption of only four system parameters. However, the identification of optimal parameter values was very time-consuming. In previous research activities, therefore, a genetic algorithm has been developed. This algorithm enables the part feeding system to autonomously identify optimal parameter values and to adjust them automatically via corresponding hardware. As a consequence, the time to adjust the part feeding system could be reduced significantly. In this subsequent research proposal, the spectrum of workpieces to be feeded should be expanded by adapting the genetic algorithm and the part feeding system in order to meet the challenges of increasing product individualization. Nowadays, the simulation of different workpiece geometries necessitates manual adjustments in the simulation model. This requires the existence of corresponding simulation experts. Therefore, an aim is to extend the simulation model in a way that enables the simulation of different workpieces without the need to adjust the simulation manually. Furthermore, the simulation model should be extended and optimized in a way that its results lead to a reduction of time to identify optimal parameter values in practice. For being able to feed a wider spectrum of workpieces without decreasing the feeding rate, optimal settings of the genetic algorithm (e. g. mutation rate or population size) should be identified as a function of the characteristics of the workpieces to be feeded and an economic triggering of a reparameterization when ambient conditions change should be developed. Thereby, the spectrum of workpieces to be feeded is to be defined by a catalog of relevant workpiece characteristics and their associated limits.

为了解决传统供料系统在供料性能、可靠性和变型灵活性方面存在的不足，IFA开发了一种气动零件供料系统。该系统的特点是进给速度高达每分钟1,000个工件，且技术可用性高。对于不同的零件，进给系统的设置仅限于四个系统参数的自适应。然而，确定最佳参数值非常耗时。因此，在以前的研究活动中，已经开发了一种遗传算法。该算法使零件进给系统能够自主识别最佳参数值，并通过相应的硬件自动调整参数值。因此，调整零件供料系统的时间可以显著减少。在随后的研究方案中，应通过采用遗传算法和零件进给系统来扩大待进给的工件范围，以满足日益增加的产品个性化的挑战。目前，不同零件几何形状的仿真都需要在仿真模型中进行手动调整。这就需要相应的仿真专家的存在。因此，目标是扩展仿真模型，使其能够在不需要手动调整仿真的情况下对不同的工件进行仿真。此外，还应扩展和优化仿真模型，使其结果能够减少在实际应用中确定最优参数值的时间。为了能够在不降低进给速度的情况下提供更广泛的工件，遗传算法的最佳设置(例如，突变率或种群大小)应该被识别为待进给的工件的特性的函数，并且应该开发当环境条件改变时重新参数化的经济触发。因此，要进给的工件的范围由相关的工件特性及其相关限制的目录来定义。