Design optimization of machines and manufacturing plants on the basis of component-oriented behavior models

机器和制造工厂是机电一体化系统。它们的设计需要几个学科的密切合作。机电一体化系统设计的目的是在早期阶段考虑预先指定的要求，创建，评估和优化不同的概念解决方案。概念机电一体化解决方案是后续学科工作的基础。表达性设计模型可以缩短实现时间并提高产品质量。该项目的目的是开发一种新的方法，用于优化机器和制造工厂的机电行为，以满足技术和管理要求。在数学优化方法的基础上，搜索那些机电元件和尺寸参数，以最佳地满足要求。接下来的步骤是比较具有最优参数化的竞争设计。行为、需求和组件数据的模型是优化的基础。为了进行行为建模，开发了一个包含机电、电气、机械、液压和气动组件的库。在这种情况下，问题是解决，模型应该如何设计，可以在早期阶段参数化，但在同一时间提供表达结果的优化。此外，还开发了各种方法来模拟需求，例如成本、周期时间和能耗，并将这些需求与行为模型联系起来。所有参数和组件组合的潜在设计空间通常非常大。因此，搜索组件和尺寸参数需要一个强大而有效的优化策略。一方面，这种优化策略由全局和局部求解器算法的适当组合组成。另一方面，该策略包括工程知识，以减少搜索空间显着。开发的模型被用来建立一个可重用的库和方法在原型中实现。随后将在几个测试案例中对模型和实施进行测试。

Machines and manufacturing plants are mechatronic systems. Their design requires the close cooperation of several disciplines. The aim of mechatronic system design is to create, evaluate and optimize different conceptual solutions at an early stage considering the beforehand specified requirements. The conceptual mechatronic solution is then the basis for the work in subsequent disciplines. Expressive design models allow to reduce realization-time and to increase product quality.The aim of the project is to develop a new methodology for optimizing the mechatronic behavior of machines and manufacturing plants with regards to technical and managerial requirements. On the basis of mathematical optimization methods those mechatronic components and dimensioning parameters are searched that optimally fulfill the requirements. The subsequent step is then to compare the competing designs with optimal parameterization.Models of behavior, requirements and component data are the basis optimization. For behavioral modeling a library with electromechanical, electrical, mechanical, hydraulic and pneumatic components is developed. In this context the question is addressed, how models should be designed that can be parameterized in the early phase, but at the same time deliver expressive results for optimization. In addition, methods are developed to model requirements, e.g. in terms of cost, cycle time and energy consumption, and to relate these to the behavior models.The potential design space of all parameter and component combinations is usually very large. Therefore, the search for components and dimensioning parameters requires a robust and efficient optimization strategy. On the one hand, this optimization strategy consists of an adequate combination of global and local solver algorithms. On the other hand, the strategy comprises engineering know-how to reduce the search space significantly.The developed models are used to build-up a reusable library and the methods are implemented in a prototype. The models and the implementation will be tested subsequently in several test cases.