The research presented in this paper shows an adaptive approach for long-term thermal error compensation of 5-axis machine tools (MT). A system of differential equations is used to compute the model ...based compensation values. The model can predict thermal displacements of the tool center point (TCP) based on changes in the environmental temperature, load-dependent changes and boundary condition changes and states, like machining with or without cutting fluid. The model based compensation of the rotary axis of a 5-axis MT is then extended by on-machine measurements. The information gained by the process-intermittent probing is used to adaptively update the model parameters, so that the model learns how to predict thermal position and orientation errors and to maintain a small residual error of the thermally induced errors of the rotary axis over a long time. This approach not only increases the MT accuracy but also reduces the amount of time spent on preproduction model parameter identification. Additionally an algorithm has been developed to dynamically adjust the length of the on-machine measurement intervals to maintain a high productivity and a constant deviation of the machined parts.
Experimental results confirm that the adaptive learning control (ALC) for thermal errors shows a desirable long-term prediction accuracy.
Powder bed fusion of polymers is becoming increasingly adopted by a variety of industries to tailor the strength, weight and functionality of end-use products. To meet the high standards of the ...modern manufacturing industry, parts built with powder bed fusion require consistent properties and to be free of defects, which is intrinsically connected to the quality of the powder bed prior to melting. The hypothesis of this work is that the roughness of the top surface of an unmelted powder bed can serve as a proxy for the powder bed density, which is known to correlate with final part density. In this study, a laser line scan profilometer is integrated onto the recoater arm of a custom powder test bench, which is able to automatically create layers of powder. A diverse group of polymers was investigated including polyamide 12 (PA12), polyamide 11 (PA11), polypropylene (PP), and a thermoplastic elastomer (TPU) under different recoating speed in order to increase the variance of the dataset. Data analytics were employed to compare roughness to measured powder bed density and a statically significant correlation was established between them.
Degraded or defect machine components and consumables negatively impact manufacturing quality and productivity. Diagnosing and predicting the wear or degradation status of critical machine components ...or parts are therefore of general interest. To tackle this challenge, data-driven approaches based on supervised machine learning principles have demonstrated promising results. However, supervised learning models capable of degradation identification require large quantities of data. In practice, run-to-failure data in large amounts is usually not available and expensive to obtain. To overcome this issue, this study proposes an unsupervised learning approach for degradation prognostics of machine tool components and consumables. It uses time series of multi-sensor signal data, which are transformed into a feature representation. The features consist of various characterizations of the time series, allowing to make different signal measurements comparable, and cluster them according to their feature values. The herewith obtained density-based clustering model is used to diagnose and predict the degradation states of components and parts in unknown conditions. The novelty in the proposed approach lies within the identification of continuous component and part degradation states based on unsupervised learning principles. The proposal is verified and demonstrated on an exemplary data set containing a small sample of run-to-failure multi-sensor signals of milling inserts and their corresponding wear state. By the application of the proposed procedure on the exemplary data set, we demonstrate that an unsupervised clustering approach is capable of separating wear data such that meaningful and accurate estimations of the part condition are possible. The advantages are its ability to cope with scarce data sets, its limited engineering and hyperparameter tuning effort, and its straightforward implementation to a multitude of degradation and wear diagnostics scenarios.
Additive manufacturing processes are among the most innovative manufacturing processes of this century. Powder-based direct metal deposition (DMD) is one of these processes. In the DMD process, local ...shielding takes place via the powder nozzle. The process is therefore critical for oxidation, especially for materials with an affinity for oxidation such as titanium, aluminum and their alloys. In order to study the oxidation behavior in more detail, the present gas dynamics must be further understood. Wirth and Wegener have made a first approach with their gas flow simulation. In this study, a measurement method for spatial oxygen concentration determination is presented. It can be shown that the spatial oxygen concentration follows the nozzle geometry. Furthermore, the coaxial nozzle is superior to the three-jet nozzle with respect to a low oxygen concentration from a carrier gas to shielding gas volume flow ratio of equal to or greater than 0.4. Finally, it can be shown that the use of a shielding gas chamber eliminates the optimization of the gas flow settings.
Silicon carbide is a ceramic material with a desirable combination of high thermal and mechanical stability, making it ideal for optical application in aerospace and next generation lithography. It ...is however notoriously difficult to machine down to super-fine finish when the shape is other than flat or spherical. In this paper, we describe the application of a “semi-elastic” machining method called shape adaptive grinding (SAG), in which an elastic tool is combined with rigid pellets made of nickel or resin, to which super abrasives are bonded. A comprehensive model of the physical interaction between SAG tool and workpiece is proposed, and used to understand the mechanics driving brittle-ductile transition on ceramic materials such as SiC. Machining parameters adequate for optical finishing are then derived from the model and demonstrated on an aspheric silicon carbide workpiece, which was manufactured by reaction bonding and coated with a layer of pure SiC by chemical vapour deposition (CVD). Through SAG processing and final polishing, this aspheric mirror was improved from an initial form error of 40µm down to 112nm Peak-to-Valley, with no residual damage visible on the surface.
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•Shape Adaptive Grinding (SAG) is a freeform grinding method applicable to ceramic materials such as silicon carbide.•Our model of SAG combines macroscopic scale contact between tool and workpiece with microscopic indentation of abrasives.•With this model, the brittle-ductile transition can be finely controlled while offering superior material removal rate.•A SiC aspheric mirror was ground with optimised SAG parameters, and polished down to 112 nm Peak-to-Valley form accuracy.
The increasing substitution of metallic structural components by carbon fibre reinforced polymers (CFRP) in aerospace applications results in a growing need for drilling metal and CFRP in one ...operation, the so-called stack machining. The different material properties of the stack components in combination with high geometrical tolerance requirements result in large challenges for the drilling tool and the process strategy. Focussing on the process strategy, this paper deals with an extensive comparison of conventional and low-frequency vibration-assisted drilling (LF-VAD) of CFRP/aluminium stacks. The influence of the cutting speed, the feed rate and the amplitude of the superimposed oscillation in LF-VAD on the resulting bore quality is analysed. For the bore quality, three separate aspects are considered, namely, damages at the CFRP entrance, burr formation at the aluminium exit and deviations from the nominal diameter in both materials. Online temperature and force measurements enable interpretation of the influence on the bore quality. Based on experimental data, a clear dependency of the bore quality and the chip transport on both, the process parameters and the drilling strategy are identified. Based on high-speed recordings, the dynamic loading situation due to the superimposed oscillation in LF-VAD is found to be crucial for the formation of peel-up delaminations.
Based on powder-bed fusion, a coupled cellular automata (CA) approach to simulate the microstructure and concentration distribution of two different materials, stainless steel S316 L and nickel base ...alloy CM247LC, in the middle and high scan speed range is presented. Local non-equilibrium models for rapid solidification are considered in this study and described. The simulation outputs from S316L and CM247LC are qualitatively and quantitatively compared with each other and validated with experiments in case of S316L. The melt pool geometry defines the grain morphology. Thin columnar grains and therefore a larger number can be found in the case of CM247LC compared to S316L. The temperature history, cooling rates and diffusivities have a tremendous impact on the grain morphology, based on SLM microstructure cross-section and single crystal simulations. Furthermore, concentration maps are analysed for both materials. In case of CM247LC, concentration maps are suggested as a possibility to predict hot cracks. Temperature dependent diffusivity coefficients and atomic spacing parameters are suggested. Simulations and experimental results are in good agreement.
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•A cellular automata approach to simulate the microstructure and concentration distribution of S316L and CM247LC with non-equilibrium models is presented•Grains of CM247LC are thinner and the texture is more anisotropic compared to S316L•The grain morphology is determined by the undercooling, cooling rate and solute diffusion•The local higher concentrations of Al may predict intercrystalline hot cracks in case of CM247LC•Simulations and experiments of S316L are in good agreement for the two laser scan speed parameters (middle and high range)
The measure for assessing the acoustic quality of the rail surfaces, the acoustic roughness, is defined in the EN 15610 standard. It is shown that this standard contains gaps with regard to the ...applied procedures for processing the raw data to the quantity of acoustic roughness. Additions to the standard appear necessary to ensure better comparability of the results. A piece of rail tactilely measured by METAS (Swiss Federal Institute of Metrology) was used as a reference. Measurement data recorded by a laser triangulation sensor was used to quantify the adjustments to the standard. This paper provides an overview of the individual processing steps and systematically examines possible additions to the standard to improve the quality of the outcome. Special emphasis was given to a method for outlier removal, pre-filtering, spike removal, curvature correction and calculation of one-third octave bands. It becomes apparent that different implementations can have a significant impact on the final result. The filter used, the wavelength ranges, the methodology for removing outliers should be specified. The spike removal, curvature correction and the calculation of the one-third octave bands should be supplemented in detail to reduce ambiguities in the implementation.
•Evaluation method for thermal errors on machine tools.•Evaluation of thermal machine tools errors with machined test piece is possible as geometrical error influences are reduced.•Evaluation of ...thermal machine tool position and orientation errors is possible with the thermal test piece.•Designed to evaluate with coordinate measuring machine (CMM) or manually with handheld measurement devices.•Results are comparable with machine tool measurements using the R-Test measurement setup.
The accuracy of 5-axis machine tools is a key factor to modern manufacturing of multi-axes machined workpieces. Up to 75% of errors on manufactured workpieces are caused by thermally induced errors of machine tools. In this paper a thermal test piece for evaluating thermal errors of machine tools equipped with rotary tables is introduced. In comparison to geometric test pieces, which detect geometrical errors of machine tools only, the developed thermal test piece visualizes thermally induced errors in X-, Y- and Z-direction as well as two additional angular errors at the tool center point (TCP) of 5-axis machine tools over time. Further, the thermal material expansion error of the test piece on centric clamped machined workpieces can be evaluated with the new thermal test piece. The presented thermal test piece is machined during a corresponding eight hour test cycle and tested on two 5-axis milling machines with different axis configurations. The thermal test piece can either be evaluated with a coordinate measuring machine (CMM) or manually with handheld measurement devices such as dial gauge, micrometer and straightedge. The thermal behavior of the two machine tools is also investigated, following the ISO 230-3:2007 regarding thermal errors of machine tools, under no-load or finishing conditions using the R-Test measurement setup. The resulting workpiece errors evaluated by using the thermal test piece are compared to the machine tool measurements. A good correspondence between the R-Test measurements and the thermal workpiece errors can be shown. CMM and handheld measurements are both capable to detect the thermal machine tool errors.
Constrained Bayesian optimization with Gaussian process models is applied to optimize the turning of 1.4125 steel bars by considering tool life, machining time, and surface roughness, in addition to ...costs of insert, operator, and machine. Feed rate and cutting speed are the optimized process parameters with constant depth of cut, both for a simulated process and for on-machine experiments on a micromachining lathe. The results demonstrate that even without prior knowledge of the process, this method successfully recommends physically reasonable process parameters within a limited number of experiments. The flexibility of the method is demonstrated by providing optimized process parameters for various cost parameters and various requirements for the constraints, based on experimental data.
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