Explore the vast range of titles available.

In order to ensure high productivity capabilities of machine tools at a low cost but at increased geometric accuracy, modeling of their static and dynamic behavior is a crucial task in structure optimization. The drive control and the frictional forces acting in feed axes significantly determine the machine’s response in the frequency domain. The aim of this study was the accurate modeling and the experimental investigation of dynamic damping effects using a machine tool test rig with three-axis kinematics. For this purpose, an order-reduced finite element model of the mechanical structure was coupled with models of the drive control and of the non-linear friction behavior. In order to validate the individual models, a new actuator system based on a tubular linear drive was used for frequency response measurements during uniaxial carriage movements. A comparison of the dynamic measurements with the simulation results revealed a good match of amplitudes in the frequency domain by considering dynamic damping. Accordingly, the overall dynamic behavior of machine tool structures can be predicted and thus optimized by a coupled simulation at higher level of detail and by considering the damping effects of friction. Dynamic testing with the newly designed actuator is a prerequisite for model validation and control drive parameterization.

Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.