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Hydro-, steam- and gas- turbines, aircraft components or moulds are milled parts with complex geometries and high requirements for surface quality. The production of such industry components often necessitates the use of long and slender tools. However, instable machining situations together with work pieces with thin wall thickness can lead to dynamic instabilities in the milling processes. Resulting chatter vibrations cause chatter marks on the work piece surface and have influence on the tool lifetime. In order to detect and avoid the occurrence of process instabilities or process failures in an early stage, the Institute for Production Engineering and Laser Technology (IFT) developed an active control system to allow an in-process adaption of machining parameters. This system consists of a sensory tool holder with an integrated low cost acceleration sensor and wireless data transmission under real time conditions. A condition monitoring system using a signal-processing algorithm, which analyses the received acceleration values, is coupled to the NC- control system of the machine tool to apply new set points for feed rate and rotational speed depending on defined optimisation strategies. By the implementation of this system process instabilities can be avoided.

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.

Vibration assisted machining has the advantages of improved tool lifetime and chip breaking together with improved chip flushing and chip clearance. The basic principle of vibration assisted machining is the stimulation of either the cutting tool or the workpiece. Therefore the IFT developed a hydraulic based tool post actuator system making it possible to investigate the influence of superimposed frequencies on surface roughness and chip breaking. Frequencies of 0 to 30 Hz were applied while measuring the stroke of the tool post using laser interferometry. The results show that there is a significant dependency between the frequency of the tool post actuator system and the stroke value. Machining a specimen with vibration assistance showed better surface quality at higher frequencies and lower strokes. Moreover, vibration assisted machining had a significant influence on chip breaking causing eye shaped chips with comparably shorter length thus preventing continous chips completely.