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.
The tendency to make progressively smaller and increasingly complex products is no longer an exclusive demand of the electronics industry. Many fields such as medicine, biomechanical technology, the automotive and the aviation industries are searching for tools and methods to realize micro and nanostructures in various materials. The micro-structuring of very hard materials, like carbides or brittle-hard materials, pose a particularly major challenge for manufacturing technology. For these reasonns the Institute for Production Engineering and Laser Technology of the Vienna University of Technology is working in the field of electrochemical micromachining with ultra short pulses. With the theoretical resolution of 10 nm, this technology enables high precision manufacturing. [Kock M.
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.
Machine tools are highly integrated mechatronic systems consisting of dedicated mechanic design and integrated electrical equipment - in particular drive systems and the CNC-control - to realize the complex relative motion of tool towards work piece. Beside the process related capabilities, like static and dynamic stiffness as well as accuracy behavior and deviation resistance against thermal influence, safety aspects are of major interest. The machine tool enclosure must fulfill multiple requirements like retention capabilities against the moving parts of broken tools, lose work pieces or clamping components. In regular use, the noise emission have to be inhibited at the greatest possible extent by the machine tool enclosure. Nevertheless, the loading door and the moving parts of the workspace envelope are interfaces where noise transmission is harder to be avoided and therefore local noise emissions increase. The aim of the objective investigation is to analyse the noise emission of machine tools to determine the local noise transmission of a machine tool enclosure by using arrays of microphones. By the use of this measuring method, outer surfaces at the front, the side and on the top of the enclosure have been scanned. The local transient acoustic pressures have been recorded using a standard noise source placed on the machine table. In addition, an exemplary manufacturing process has been performed to analyse the frequency dependent location resolved sound emissions.
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