Cross phase process concepts for injection moulding using modern control strategies

在第一个供资期间，对跨阶段过程控制进行了调查。这包括轨迹规划和过程控制。轨迹规划基于质量模型，并指定要通过过程控制实现的型腔压力轨迹。过程控制是一种自适应的基于模型的预测控制，它可以参数化与低的努力。腔压力的交叉相位控制允许减少过程控制变量的数量。此外，由于在受控变量中不发生切换，因此过程控制更鲁棒。过程控制的一个重要部分是预定义的材料属性。处理消费后回收物（PCR）的处理条件存在很大差异，因为批次的组成和历史各不相同。在循环经济方面，该研究项目的中心目标是一方面，实现恒定的成型质量，而不考虑批次属性的变化，另一方面，减少所需的工艺设置工作。在这种情况下，改变材料的特性需要调整轨迹规划和过程控制。通过扩展现有的跨阶段过程控制策略，使适应自动化。为此，面向PCR的质量模型与基于学习的模型预测型腔压力控制相结合，从而实现整体的高工艺稳定性。首先，通过测试实验室分析和注塑实验来量化PCR批次的差异。然后，材料，工艺变量和零件质量之间的关系被映射到一个质量模型。与派生的质量模型，回收物的影响，可以显式地考虑在线质量控制。为了以最小的调整努力实现鲁棒控制性能，过程控制器的控制器模型由基于数据的误差模型扩展。该误差模型基于高斯过程回归，其中批次相关的模型偏差通过过程数据在线学习。定量评估学习模型组件对过程稳定性的影响。最后，所开发的过程控制将在技术机器上集成并迭代改进。回收物控制的评估和验证在具有至少两种PCR类型的复杂组件几何形状上进行。到项目结束时，应该可以将零件质量中与批次相关的偏差减少至少50%。从长远来看，该项目应有助于减少废品以及材料和能源消耗。此外，还将增加使用PCR的吸引力，以实现循环经济意义上的材料回收。

In the first funding period, a cross-phase process control was investigated. This consists of trajectory planning and process control. The trajectory planning is based on a quality model and specifies the cavity pressure trajectory to be realized by the process control. The process control is an adaptive model-based predictive control, which can be parameterized with low effort. The cross-phase control of cavity pressure allows to reduce the number of process control variables. In addition, the process control is more robust since no switching occurs in the controlled variable. An essential part of the process control are the pre-defined material properties. Processing post-consumer recyclate (PCR) comes with strong differences in the processing conditions as the composition and history of the batches vary. In terms of the recycling economy, the central goals of the research project are for one, the realization of a constant molding quality irrespective of batch property variations and for another, the reduction of the required process set-up effort. In this context, changing material properties of the granulate require an adjustment of both the trajectory planning and the process control. The adaptation is to be automated by an extension of the existing cross-phase process control strategy. For this purpose, a PCR-oriented quality model is combined with a learning-based model-predictive cavity pressure control, so that overall high process stability is achieved. First, differences in PCR batches are quantified by testing laboratory analysis and injection molding experiments. Then, the identified relationship between material, process variables and part quality are mapped in a quality model. With the derived quality model, the influence of the recyclate can be explicitly considered in an inline quality control. To achieve a robust control performance with a minimum of adjustment effort, the controller model of the process controller is extended by a data-based error model. This error model is based on a Gaussian Process Regression, in which batch-dependent model deviations are learned online via process data. The effect of the learned model component on process stability is quantitatively assessed. Finally, the developed process control is to be integrated at the technical machine and iteratively improved. The evaluation and validation of the recyclate control are carried out on a complex component geometry with at least two PCR types. By the end of the project, it should be possible to reduce batch-related deviations in part quality by at least 50%. In the long term, the project should thus help to reduce rejects as well as material and energy consumption. In addition, the attractiveness of the use of PCR is to be increased in order to enable material recycling in the sense of the recycling economy.