This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that ...represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.
This paper proposes a novel approach for in-process endpoint detection of weld seam removal during robotic abrasive belt grinding process using discrete wavelet transform (DWT) and support vector ...machine (SVM). A virtual sensing system is developed consisting of a force sensor, accelerometer sensor and machine learning algorithm. This work also presents the trend of the sensor signature at each stage of weld seam evolution during its removal process. The wavelet decomposition coefficient is used to represent all possible types of transients in vibration and force signals generated during grinding over weld seam. “Daubechies-4” wavelet function was used to extract features from the sensors. An experimental investigation using three different weld profile conditions resulting from the weld seam removal process using abrasive belt grinding was identified. The SVM-based classifier was employed to predict the weld state. The results demonstrate that the developed diagnostic methodology can reliably predict endpoint at which weld seam is removed in real time during compliant abrasive belt grinding.
To improve the accuracy of the current vision-based linear displacement measurement in a large range, a new type of linear displacement sensing system, namely, image grating, is proposed in this ...paper. The proposed system included a patterned glass plate attached to the moving object and an ultra-low distortion lens for high-accuracy image matching. A DFT local up-sampling phase correlation method was adopted to obtain the sub-pixel translation of the patterns onto the target plate. Multiple sets of stripe patterns with different designs were located on the glass plate to expand the measurement range, based on the principle of phase correlation. In order to improve the measurement accuracy, the main errors of the image grating system were analyzed, and the nonlinear error compensation was completed based on the dynamic calibration of the pixel equivalent. The measurement results, after the error compensation, showed that the total error of the proposed system was less than 2.5 μm in the range of 60 mm, and the repeatability was within 0.16 μm, as quantified by standard deviation.
Two main challenges in using a pneumatic artificial muscle (PAM) actuator are the nonlinearity of pneumatic system and the nonlinearity of the PAM dynamics. The latter is complicated to characterize. ...In this paper, a Maxwell-slip model used as a lumped-parametric quasi-static model is proposed to capture the force/length hysteresis of a PAM. The intuitive selection of elements in this model interprets the unclear, but blended contributing causes of the hysteresis very well, which are assumed to originate from the dry friction of the double helix weaving of the PAM braided shell, the friction of the weaving and the bladder, the elasticity of the bladder and/or the deformation of the conical parts of a PAM close to the end caps. The obtained model is simple, but physically meaningful and easy to handle in terms of control.
The characterization of surface topographic features on a component is typically quantified using two-dimensional roughness descriptors which are captured by off-line desktop instruments. Ideally any ...measurement system should be integrated into the manufacturing process to provide in-situ measurement and real-time feedback. A non-contact in-situ surface topography measuring system is proposed in this paper. The proposed system utilizes a laser confocal sensor in both lateral and vertical scanning modes to measure the height of the target features. The roughness parameters are calculated in the developed data processing software according to ISO 4287. To reduce the inherent disadvantage of confocal microscopy, e.g., scattering noise at steep angles and background noise from specular reflection from the optical elements, the developed system has been calibrated and a linear correction factor has been applied in this study. A particular challenge identified for this work is the in-situ measurement of features generated by a robotized surface finishing system. The proposed system was integrated onto a robotic arm with the measuring distance and angle adjusted during measurement based on a CAD model of the component in question. Experimental data confirms the capability of this system to measure the surface roughness within the
range of 0.2⁻7 μm (bandwidth
of 300), with a relative accuracy of 5%.
In this research paper, a precision position-measurement system based on the image grating technique is presented. The system offers a better robustness and flexibility for 1D position measurement ...compared to a conventional optical encoder. It is equipped with an image grating attached to a linear stage as the target feature and a line scan camera as the stationary displacement reader. By measuring the position of the specific feature in the image and applying a subpixel image registration method, the position of the linear stage can be obtained. In order to improve the computational efficiency, the calculations for pattern correlation and subpixel registration are performed in the frequency domain. An error compensation method based on a lens distortion model is investigated and implemented to improve the measurement accuracy of the proposed system. Experimental data confirms the capability of the developed image grating system as ±0.3 µm measurement accuracy within a 50 mm range and ±0.2 µm measurement accuracy within a 25 mm range. By applying different optics, the standoff distance, measurement range, and resolution can be customized to conform to different precision measurement applications.
In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical ...role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic approaches, supporting online lifelong prognostics without the requirement of a retraining or reconfiguration phase. The PANFIS method is applied to normal-to-failure bearing vibration data collected for 139 days to predict the time-domain features of vibration slew bearing signals. The performance of the proposed method is compared to some established methods, such as ANFIS, eTS, and Simp_eTS. From the results, it is suggested that PANFIS offers an outstanding performance compared to those methods.
This article explores the effects of parameters such as cutting speed, force, polymer wheel hardness, feed, and grit size in the abrasive belt grinding process to model material removal. The process ...has high uncertainty during the interaction between the abrasives and the underneath surface, therefore the theoretical material removal models developed in belt grinding involve assumptions. A conclusive material removal model can be developed in such a dynamic process involving multiple parameters using statistical regression techniques. Six different regression modelling methodologies, namely multiple linear regression, stepwise regression, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and random forests (RF) have been applied to the experimental data determined using the Taguchi design of experiments (DoE). The results obtained by the six models have been assessed and compared. All five models, except multiple linear regression, demonstrated a relatively low prediction error. Regarding the influence of the examined belt grinding parameters on the material removal, inference from some statistical models shows that the grit size has the most substantial effect. The proposed regression models can likely be applied for achieving desired material removal by defining process parameter levels without the need to conduct physical belt grinding experiments.
3D Concrete Printing (3DCP) has been gaining popularity in the past few years. Due to the nature of line-by-line printing and the slump of the material deposition in each extruded line, 3D printed ...structures exhibit obvious lines or marks at the layer interface, which affects surface finish quality and potentially affect bonding strength between layers. This makes it necessary to control the extrudate formation in 3DCP. However, it is difficult to directly analyse the extrudate formation process because the extrudate shape depends on many parameters. In this paper, a machine learning technique is applied to correlate the formation of the extrudate to the printing parameters using an Artificial Neural Network model. The training data for the model development was obtained from extrudates printed in 3DCP experiments. The performance of the trained model was experimentally validated and the predicted extrudate geometry resulting from the developed model showed good agreement to the actual extrudate geometry. Subsequently, the developed model was used to find proper nozzle shapes to produce designated extrudate geometries. Significant improvement on the printing quality was demonstrated using nozzle shapes generated from the model on 3D printed objects consisting a vertical wall, an inclined wall and a curved part.
Wire arc additive manufacturing (WAAM) is getting much research attention because of its cost-effectiveness in the metallic production of large and complex parts. In pursuit of best-quality products ...and minimizing material loss, multimodal process monitoring methods are key. This paper presents the use of acoustic and current signals in identifying one of the critical defects in WAAM, i.e., porosity. Aluminum and unalloyed steel were deposited in a controlled environment which developed different amounts of porosity alongside measurements from current and gas sensors. Feature reduction of the signals was carried out using a combination of wavelet scattering networks and sparse principal component analysis (sPCA). While the models predict porosity reasonably, the dominant features learned by the model are also investigated and reported.
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