Cross phase process concepts for injection moulding using modern control strategies

在第一个资助期，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。轨迹规划以质量模型为基础，通过过程控制确定型腔压力轨迹。该过程控制是一种基于模型的自适应预测控制，可以以较低的工作量实现参数化。型腔压力的跨相控制可以减少过程控制变量的数量。此外，由于被控变量不会发生切换，因此过程控制更加稳健。工艺控制的一个重要部分是预定义的材料特性。由于批次的成分和历史不同，加工后消费后回收物(PCR)的加工条件有很大的差异。在循环经济方面，该研究项目的中心目标是实现恒定的成型质量，而不受批次特性变化的影响，以及减少所需的工艺设置工作量。在这种情况下，改变颗粒的材料性质需要对轨迹规划和过程控制进行调整。这一调整将通过延长现有的跨阶段过程控制战略实现自动化。为此，面向PCR的质量模型与基于学习的模型-预测腔压控制相结合，从而实现了整体较高的工艺稳定性。首先，通过测试、实验室分析和注塑实验来量化不同批次的差异。然后，将识别出的材料、工艺变量和零件质量之间的关系映射到质量模型中。利用导出的质量模型，可以在在线质量控制中显式地考虑回收物的影响。为了以最小的调整工作量获得鲁棒的控制性能，过程控制器的控制器模型被扩展为基于数据的误差模型。该误差模型基于高斯过程回归，其中批次相关模型偏差通过过程数据在线学习。对学习到的模型分量对过程稳定性的影响进行了定量评估。最后，将所开发的过程控制在技术机上集成并迭代改进。回收控制的评估和验证是在具有至少两种PCR型的复杂组件几何形状上进行的。到项目结束时，应该可以将与批次相关的零件质量偏差减少至少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，还将增加使用多氯联苯的吸引力，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.