1,720,995 research outputs found
Upgraded Regularized Deconvolution of complex dynamometer dynamics for an improved correction of cutting forces in milling
No full textIn order to characterize cutting mechanics during high-speed milling and micromilling applications, high-end piezoelectric dynamometers with a wide frequency bandwidth are necessary. Nevertheless, when installed into the machine tool their signal bandwidth is limited by the dynamic behaviour of the machining system. Thus, special filters have to be adopted for dynamics compensation. State of the art filters are based on a simplistic 3 × 3 dynamic model of device transmissibility without taking into account the influence of input force location with respect to the centre of the sensing platform. The Upgraded Augmented Kalman Filter has been recently proposed for solving this problem. Although it outperformed the other state of the art filters, it was based on the preliminary identification of a parametric mathematical model that is generally a difficult and non-automatic task. Here a novel non-parametric filter is introduced, that was based on a more general and abstract model of dynamometer dynamics considering both input force direction and location. By so doing, impressive results were found both from modal analysis and from real cutting tests, showing the potential of the new method for an effective and almost completely automatic cutting force dynamic compensation
Polynomial Chaos-Kriging approaches for an efficient probabilistic chatter prediction in milling
No full textAfter more that 60 years of investigation, chatter vibrations in metal cutting are still a major cause for poor surface finish and machine tool damage. In order to avoid undesired machining conditions, chatter prediction algorithms may be applied to draw stability charts that allow a preliminary identification of the safe areas. Nevertheless, the stability boundaries are sensitive to the variations and uncertainties of the dynamic milling model coefficients. Thus, the accuracy and reliability of the obtained predictions can be inadequate for many industrial applications. For solving this problem, robust methods were recently devised that are fast but usually too conservative. On the other side, probabilistic approaches were also developed to estimate the probability of instability for a given combination of cutting parameters, by taking into account the statistical distributions of model coefficients. Probabilistic approaches allow a less conservative, risk-aware selection of stable cutting conditions. Unfortunately, their application is still very limited due to the required large amount of computational power and time. In this work, three novel probabilistic methods based on Polynomial Chaos and Kriging metamodels (PCE, KRI and PCK) were compared to state of the art probabilistic algorithms (MC, MC-SPA, DRM-SPA, RCPM). The numerical analysis and the experimental validation proved that MC-SPA, DRM-SPA, RCPM and PCE are too rough and thus needless for industrial applications. On the contrary, KRI and in some cases also PCK showed an excellent accuracy together with significantly shorter elaboration time than that required by the reference Monte Carlo (MC) technique
Superior optimal inverse filtering of cutting forces in milling of thin-walled components
No full textMeasuring the cutting force when milling slender/thin-walled parts is difficult because of the large and long-lasting structural vibrations that cause inertial disturbances in the measured signals. Under these conditions, signal filtering is the only option to significantly extend the dynamic bandwidth of the device above 3 kHz. Non-parametric filters are typically preferred over parametric ones because they are more practical and easier to apply in industrial applications. Currently available parametric filters cannot address this problem because they are based on oversimplified transmissibility models or are affected by computational problems when the impulse responses of the device are excessively long. In this study, the novel non-parametric Superior Optimal Inverse Filter was developed to address the limitations of state-of-the-art filters. It is a non-trivial extension of the Optimal Inverse Filter to a higher dimensionality, and it can process long transients and generic (possibly aperiodic) signals. Thus, outstanding results were obtained both from modal analysis and from an actual case study, demonstrating the potential of the new filter for an effective and almost completely automatic cutting force dynamic compensation when milling thin-walled structures. The proposed filter was compared with parametric Kalman filters and with the existing non-parametric filters, and it offered a considerably better performance, particularly for compensating for cross disturbances and for input force position-dependent dynamics
Influence of the Experimental Setup on the Damping Properties of SLM Lattice Structures
Full text linkBackground: Metal lattice structures obtained through Selective Laser Melting may increase the strength-to-weight ratio of advanced 3D printed parts, as well as their damping properties. Recent experimental results showed that AlSi10Mg and AISI 316L lattices are characterized by higher Rayleigh damping coefficients with respect to the fully dense material. However, some unclear or contradictory results were found, depending on the experimental setup adopted for modal analysis. Objective: In this work the influence of the experimental setup when performing modal analysis on different SLM AISI 316L lattice structures was deeply investigated. The study provides a critical comparison of various experimental modal analysis approaches, allowing to evaluate the influence of external damping sources and material internal damping phenomena. Methods: The dynamic behaviour of SLM AISI 316L specimens incorporating lattice structures was estimated by means of pulse testing and sinusoidal excitation through an electromagnetic shaker. The validity of the viscous damping model was assessed by means of sinusoidal excitation with different levels of vibration velocity. Moreover, the influence of experimental setup on modal analysis results was critically evaluated, by considering different actuators, contact and non-contact sensors and boundary/clamping conditions. Results: The classical viscous damping model describes with good approximation the damping properties of SLM lattice structures. When exciting single specimens in free-free conditions, those embedding lattice structure and unmelted metal powder filler were characterized by superior internal damping properties with respect to the specimens incorporating the lattice structure without any filler, which was however more effective than the full density equivalent material. Most of the other experimental setups introduced additional external damping sources, that could alter this important outcome. Conclusions: SLM lattice structures embedded into 3D printed components provide superior damping properties against mechanical and acoustic vibrations and the metal powder filler does significantly enhance such damping capacity. A correct estimation of material internal damping was achieved by applying non-contact sensors and free-free boundary conditions, whereas other experimental setups were partly inadequate
Development of a universal, machine tool independent dynamometer for accurate cutting force estimation in milling
Full text linkWhen integrating a dynamometer into a machining system, it is necessary to identify the dynamic relationship between the effective input forces and the measured output signals (i.e., its transmissibility) through dedicated experimental modal analysis. Subsequently, a filter can be derived and applied to reconstruct the effective input forces from the measured signals. Unfortunately this identification phase can be complex, posing challenges to the device's applicability in both laboratory and industrial conditions. Here this challenge is addressed by introducing a novel dynamometer concept based on both load cells and accelerometers, along with a Universal Inverse Filter. Notably, this filter is independent of the dynamic behavior of the mechanical system where the device is installed. A single calibration suffices, ideally conducted by the device manufacturer or by an expert, allowing the dynamometer's integration by a non-expert user into any machining system without the need for repeating the identification phase and the filter generation. Furthermore, this new concept offers another significant advantage: it attenuates all inertial disturbances affecting the measured signals, including those arising from the cutting process and those originating from exogenous sources such as spindle rotation, linear axes’ movements, and other vibrations propagating through the machine tool structure. To illustrate, a simplified model is introduced initially, followed by an overview of the novel dynamometer design, innovative identification phase, and filter construction algorithm. The outstanding performance of the novel (non-parametric) Universal Inverse Filter – about 5 kHz of usable frequency bandwidth along direct directions and 4.5 kHz along cross dir. – was experimentally assessed through modal analysis and actual cutting tests, compared against state of the art filters. The efficacy of the new filter, which is even simpler than its predecessors, was successfully demonstrated for both commercial and taylor-made dynamometers, thus showing its great versatility
Upgraded Kalman filtering of cutting forces in milling
Full text linkAdvanced piezoelectric dynamometers with a wide frequency bandwidth are required for cutting force measurement in high-speed milling and micromilling applications. In many applications, the signal bandwidth is limited by the dynamic response of the mechanical system, thus compensation techniques are necessary. The most effective compensation techniques for a full 3D force correction require an accurate and complex identification phase. Extended Kalman filtering is a better alternative for input force estimation in the presence of unknown dynamic disturbances. The maximum bandwidth that can be currently achievable by Kalman filtering is approximately 2 kHz, due to crosstalk disturbances and complex dynamometer’s dynamics. In this work, a novel upgraded Kalman filter based on a more general model of dynamometer dynamics is conceived, by also taking into account the influence of the force application point. By so doing, it was possible to extend the frequency bandwidth of the device up to more than 5 kHz along the main directions and up to more than 3 kHz along the transverse directions, outperforming state-of-the-art methods based on Kalman filtering
A novel thermo-geometrical model for accurate keyhole porosity prediction in Laser Powder-Bed Fusion
Full text linkWhen performing Laser Powder-Bed Fusion, undesired physical phenomena, such as balling, preballing and keyhole, must be avoided in order to achieve high-quality products. To date, keyhole-free process parameters can be identified either using demanding empirical methods or complex numerical simulations, while only a few analytical models can be found in literature for this purpose. In this work, state-of-the-art analytical models for predicting keyhole porosity were summarized and proved to be rather inaccurate because they are only based on thermodynamic principles, whereas they neglect the geometry and both the kinetics and kinematics of the keyhole cavity, which do also influence cavity collapse and porosity formation. Here an innovative physics-based semi-analytical model for predicting the formation of keyhole-related porosities was conceived, in which both thermodynamic and geometrical factors are taken into account. The proposed model was validated by performing single tracks experiments on Ti6Al4V according to a full factorial DoE on laser power and scanning speed. Produced samples were cross-sectioned and analyzed to evaluate keyhole porosity formation. The comparison between experimental data and model predictions confirmed the higher accuracy of the new model with respect to state of the art models. Besides improving the understanding of the keyhole phenomenon, the proposed model may provide a novel, effective and simple tool for fast process parameter optimization in industry
Multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network for cross-domain, poor thermal information prediction in cloud-edge control services
No full textThermal error, which has spatiotemporal behavior, severely reduces machining accuracy of high-accuracy machine tools and should be controlled in real time. The deep learning is used to establish spatiotemporal thermal error model with large-sample thermal information as input. But the acquisition of large-sample thermal information is extremely difficult and costly. So, in the actual application, thermal error is predicted with poor thermal information as input, and then the robustness is weak because the cross-domain and poor spatiotemporal thermal information prediction is still a severe challenge. In this study, the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is proposed without deepening network depth, instead, its width is expanded. Specifically, the adjacency matrix is constructed to consider the spatial information by defining the distance between each two sensors, and then the graph convolutional network is integrated into long short-term memory and gated recurrent unit to propose graph convolutional long short-term memory and graph convolutional gated recurrent unit with constructed adjacency matrix as input, respectively. The graph convolutional long short-term memory and graph convolutional gated recurrent unit are superimposed to propose the multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network. To improve the robustness, the adjacency matrix is retrained, and the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is proposed and applied to forecast thermal error, and its prediction accuracy is 94.926%. Spatiotemporal characteristics of temperature and thermal error are fully captured by transfer learning model of graph convolutional long short-term memory and graph convolutional gated recurrent unit. Finally, the transfer learning model of multi-layer parallel-perceptual-fusion spatiotemporal graph convolutional network is embedded into thermal error control service architecture based on cloud-edge collaboration. With implementation of thermal error control service architecture based on cloud-edge collaboration, machining error of machine tools is reduced by more than 80%
Digital upgrade of a bandsaw machine through an innovative guidance system based on the digital shadow concept
Full text linkNowadays, there is an increasing trend towards advanced CNC machine tools having a high level of automation. Nevertheless, manually operated equipment is still playing an important role in many industrial workshops. Operators’ experience is still essential in the perspective of increasing productivity, enhancing product quality, reducing manufacturing costs related to tool wear, waste and maintenance. Thus, even manual operations that are apparently less important in terms of product added value may deserve attention and need to be improved according to the principles of the digital transformation era. This paper introduces a structured approach for design, development and implementation of an operator guidance system for a manual bandsaw machine, based on the digital shadow concept and additional feedback sensors. This provides an actual example of how the digital transformation of a small-scale equipment may improve the manufacturing performance and ergonomics as well
Passive chatter suppression of thin-walled parts by means of high-damping lattice structures obtained from selective laser melting
Full text linkChatter vibrations arising during machining operations are detrimental for cutting process performance, since they may cause poor surface quality of the machined part and severe damages to machine tool elements. Passive approaches for chatter suppression are based on the integration of special mechanical components with high-damping properties within the machining system. They represent a good solution to this problem thanks to their intrinsic simplicity. Recently, the application of metallic lattice structures inside 3D printed parts obtained from the Selective Laser Melting technology have proven superior damping properties with respect to the same full density material. Here, this idea is further explored by considering the novel configuration where the unmelted powder grains are retained inside the lattice structure by an external shell, acting as a multiplicity of microscopic mechanical dampers. This concept is applied for passive chatter suppression of thin-walled parts that are of particular relevance for industry. Preliminary experimental investigation was first carried out on simple beam-like specimens, and then on thin-walled benchmarks that were identified through modal analysis and tested under real cutting conditions. The main conclusion is that the novel proposed configuration (lattice plus unmelted powder) has higher damping properties with respect to the full density and lattice alternatives. Accordingly, it may be successfully applied for passive chatter suppression in real machining operations
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