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A piezoelectric-based micro motion actuator is typically used in micro-scale movement technologies, with the actuator developed to deliver very small movements and high resolution for motion within several micrometer ranges. However, a significant challenge from the strong, nonlinear hysteresis arises affects the piezoelectric materials joining input voltage to output movement, which deteriorates the accuracy of the actuator and causes instability in a closed-loop system. To obtain high precision, accuracy and reduced nonlinear effects, piezoelectric actuators must be controlled with hysteresis compensation. Therefore, this research developed a piezoelectric-based microactuator system with a control scheme based on PID (proportional-integral-derivative) combined with the inverse hysteresis model implemented to compensate for the actuator's hysteresis. Furthermore, a Modified Prandtl-Ishlinskii (MPI) model was used to capture the hysteresis phenomenon, where its parameters were obtained through a system identification process. The inverse model of the hysteresis was then used to generate feedforward signals in the control system. The results showed that the control scheme is able to provide an accurate motion due to the decrease in hysteresis compensation signals from 4.87 to 0.97 . The closed loop control system consisting of the PID control and hysteresis compensation further improved the accuracy of the piezoelectric actuator and reduced the error down to 0.41 .

The electrified hybrid shipboard power system with high-level integration of renewable energy resources and energy storage system has become the new trend for the all-electric ship (AES) configuration. However, the traditional rule-based energy management system (EMS) is not able to fulfill the increasingly complex control requirements, and a more advanced EMS control algorithm is required to handle the multiple power sources and even achieve optimal energy management control. This paper proposes a supervisory-level EMS with an improved adaptive model predictive control (AMPC) strategy to optimize the power split among the hybrid power sources and to reduce the total cost of ownership (TCO) of vessel operation, which considers not only the fuel and emission costs but also the power source degradation. In order to achieve real-time implementation, the AMPC-based EMS software has been developed and deployed to a programmable logic controller (PLC) hardware. The prototyping controller verification tests have been performed with a hybrid fuel cell-fed shipboard power system hardware-in-the-loop (HIL) plant in the lab environment. Three typical tugboat load profiles with power fluctuations are implemented as case studies. Lastly, a cost study was performed to compute the economic benefits for a ten-year long-term vessel operational cycle. The proposed AMPC-based EMS is robust and effective, which can achieve up to 12.19% TCO savings compared to those of a traditional rule-based control strategy.

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.

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.

The increasing concern in environmental impacts contributed by the shipping industry leads to a growing interest toward incorporating sustainable energy sources to conventional power systems, to reduce fuel consumption and emissions. However, such advance power system architectures tend to face greater challenges in optimal power allocation due to the increased flexibility to split the load among multiple power sources and diversified operations of different vessel types. To address the power management challenges, in this paper, an instantaneous optimization-based approach is proposed for an all-electric hybrid power system, which adopts the equivalent consumption minimization strategy (ECMS). First, the dynamic models of the all-electric hybrid power system are formulated in MATLAB $\backslash $ Simulink and validated on a laboratory-scaled hybrid power system testbed. The power management problem is then formulated using ECMS approach, and a multi-level power management framework is proposed to integrate ECMS solution for practical implementation on an all-electric hybrid vessel. Finally, the feasibility of the proposed approach is validated on a full-scale setup identical to an actual all-electric hybrid vessel with dc distribution in MARINTEK, Trondheim, Norway. The performance of ECMS is also benchmarked against an improve rule-based strategy. The experimental results demonstrated that ECMS can achieve a substantial fuel savings of up to 24.4% as compared with improved rule-based strategy over one cycle of harbor tugboat operations, with the presence of available shore charging.

Sari. Dalam kegiatan anaiisis ini diiakukan permodelan sistem getaran dengan gaya eksitasi yang berasal dari ketidakseimbangan rotor dan ketaksesumbuan poros. Gaya eksitasi ini kemudian diteruskan ke bantalan dan selanjutnya melulaui bantalan diteruskan ke rumah bantalan. Ketaklinieran kekakuan sistem bantalan yang berasal dari celah (clearance) bantalan tersebut akan menghasilkan ciri sendiri sehinngga ciri ini dapat digunakan untuk mengidentifikasi keausan bantalan yang digunakan. Hasil analisis teoritik ini selanjutnya dibandingkan dengan data gertaran domain waktu (waveform) yang diperoleh dari kaji eksperimental pada sistem poros-tutor. Mechanical Signature Analysis of Synchronous and synchronous Excited Rotating Rotor Supported by Rolling Element Bearing Abstract. An analytical model of a rotor shaft vibration system subjected to rotor unbalance and shaft misalignment excitation forces has been developed. Those excitation forces are transmitted to a bearing housing through a roller bearing housing where the vibration response is measured. The non-linearity of the bearing stiffness due to the bearing clearance reveals some specific vibration signatures, which can be utilized for bearing, wear identification. The theoretical results obtained in this study are compared with those obtained from experiment.

Sari. Bantalan gelinding digunakan secara luas sebagai pemompa pada setiap mesin rotasi. Oleh karena itu, karakteristik dinamina suatu bantalan harus dapat diketahui dengan baik. Karakteristik dinamik bantalan yang diketahui dapat digunakan untuk menganalisis respons getaran yang terjadi pada struktur yang menggunakan bantalan tersebut. Sayangnya mekanisme kontak pada bantalan mengakibatkan kekakuan yang tidak linier pada bantalan. Hal ini mengakibatkan bantalan gelinding merupakan system getaran non-linier. Analisis FRF Non-Linier dapat dilakukan dengan memanfaatkan deret Voltera. Dalam penelitian ini deret Voltera digunakan untuk menentukan FRF Non-Linier dari bantalan yang diamati sehingga dapat diramalkan respons getarannya pada frekuensi harmonic akibat sinyal pengeksitasi. Non-linear Vibration Respons of Bearing System Part II: Comparative Study of Runge-Kutta metho and Volterra-functional-series method Abstract. Rotating machinery is commonly utilized in the processing industry. Vibration monitoring based on predictive maintenance technology is widely utilized for condition monitoring of the rotating machinery in service. This maintenance technology is based on the signature of the vibration response normally measured at the bearing house. On the other hand, the vibration response of a system is determined by the system dynamic characteristic and its working stimulus. Therefore the nature of the main component dynamic characteristics results in significant influence on the predicted vibration response at the bearing house. As mentioned above the signature of the vibration response measured at the bearing house and presented in the frequency domain contains several harmonic signals. Most of the higher harmonics do not have physical meanings, but they exist in the vibration measurement results and confused the researchers. Regarding those harmonic signals a hypothesis can be developed for identifying its origin. They are assumed to be generated due to dynamic characteristic non-linearity of the vibration system combined with the stiffness non-linearity of the roll bearing. By analyzing harmonic signal response due to nonlinear property of the vibration system, hopefully the mechanical signature of nonlinear vibration system can be generated. Therefore, many other mechanical signatures of components failure can be formulated and the harmonic signals, which are usually occurred, can be predicted more accurately.

Mirror-finished surface products have a wide range of applications in different engineering industries, such as power generation, aerospace, semiconductors and optics. The surface topography of mirror-finished products is typically measured in a metrology laboratory, which is typically time consuming and cannot be integrated into the manufacturing process. To allow for in-situ product quality assurance and automatic tool change for manufacturing processes, a more accurate and responsive surface-measurement method is needed. For highly polished surfaces, a sub-micron surface fluctuation makes it possible to use light-scattering effects and image processing for surface texture analysis. A non-contact surface inspection system using a fringe-patterned illumination method is proposed in this paper. A predesigned pattern was projected onto the target surface, and its reflected image was captured by a camera. It was found that the surface parameters Sa and Sq, which are widely used to evaluate surface quality, are significantly correlated with luminous-intensity distribution. Another parameter, Str, which quantifies the uniformity of surface-texture directions due to polishing or grinding marks, was traditionally quantified after a complete-surface topographic measurement. In this research, a new approach is proposed to determine surface isotropy through a luminance-intensity distribution analysis. By rotating the test coupon, the variation of specular reflection showed correlation with the significance of surface-texture direction. The experimental results demonstrate that mirror-finished surfaces with a large deviation in luminance intensity across the pattern possess low Str values, which indicates low uniformity in surface texture.

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.

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.

B-spline functions are widely used in many industrial applications such as computer graphic representations, computer aided design, computer aided manufacturing, computer numerical control, etc. Recently, there exist some demands, e.g. in reverse engineering (RE) area, to employ B-spline curves for non-trivial cases that include curves with discontinuous points, cusps or turning points from the sampled data. The most challenging task in these cases is in the identification of the number of knots and their respective locations in non-uniform space in the most efficient computational cost. This paper presents a new strategy for fitting any forms of curve by B-spline functions via local algorithm. A new two-step method for fast knot calculation is proposed. In the first step, the data is split using a bisecting method with predetermined allowable error to obtain coarse knots. Secondly, the knots are optimized, for both locations and continuity levels, by employing a non-linear least squares technique. The B-spline function is, therefore, obtained by solving the ordinary least squares problem. The performance of the proposed method is validated by using various numerical experimental data, with and without simulated noise, which were generated by a B-spline function and deterministic parametric functions. This paper also discusses the benchmarking of the proposed method to the existing methods in literature. The proposed method is shown to be able to reconstruct B-spline functions from sampled data within acceptable tolerance. It is also shown that, the proposed method can be applied for fitting any types of curves ranging from smooth ones to discontinuous ones. In addition, the method does not require excessive computational cost, which allows it to be used in automatic reverse engineering applications.

Flexible membrane wings (FMWs) are known for two inherent advantages, that is, adaptability to gusty airflow as the wings can flex according to the gust load to reduce the effective angle of attack and the ability to be folded for compact storage purposes. However, the maneuverability of UAV with FMWs is rather limited as it is impossible to install conventional ailerons. The maneuver relies only on the rudders. Some applications utilize torque rods to warp the wings, but this approach makes the FMW become unfoldable. In this research, we proposed the application of a tendon-sheath mechanism to manipulate the wing shape of UAV. Tendon-sheath mechanism is relatively flexible; thus, it can also be folded together with the wings. However, its severe nonlinearity in its dynamics makes the wing warping difficult to control. To compensate for the nonlinearity, a dedicated adaptive controller is designed and implemented. The proposed approach is validated experimentally in a wind tunnel facility with imitated gusty condition and subsequently tested in a real flight condition. The results demonstrate a stable and robust wing warping actuation, while the adaptive washout capability is also validated. Accurate wing warping is achieved and the UAV is easily controlled in a real flight test.

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.

The number of studies on the Internet of Things (IoT) has grown significantly in the past decade and has been applied in various fields. The IoT term sounds like it is specifically for computer science but it has actually been widely applied in the engineering field, especially in industrial applications, e.g., manufacturing processes. The number of published papers in the IoT has also increased significantly, addressing various applications. A particular application of the IoT in these industries has brought in a new term, the so-called Industrial IoT (IIoT). This paper concisely reviews the IoT from the perspective of industrial applications, in particular, the major pillars in order to build an IoT application, i.e., architectural and cloud computing. This enabled readers to understand the concept of the IIoT and to identify the starting point. A case study of the Amazon Web Services Machine Learning (AML) platform for the chamfer length prediction of deburring processes is presented. An experimental setup of the deburring process and steps that must be taken to apply AML practically are also presented.

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 Ra range of 0.2–7 μm (bandwidth λc/λs of 300), with a relative accuracy of 5%.

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.

Process monitoring using indirect methods relies on the usage of sensors. Using sensors to acquire vital process related information also presents itself with the problem of big data management and analysis. Due to uncertainty in the frequency of events occurring, a higher sampling rate is often used in real-time monitoring applications to increase the chances of capturing and understanding all possible events related to the process. Advanced signal processing methods are used to further decipher meaningful information from the acquired data. In this research work, power spectrum density (PSD) of sensor data acquired at sampling rates between 40–51.2 kHz was calculated and the corelation between PSD and completed number of cycles/passes is presented. Here, the progress in number of cycles/passes is the event this research work intends to classify and the algorithm used to compute PSD is Welch’s estimate method. A comparison between Welch’s estimate method and statistical methods is also discussed. A clear co-relation was observed using Welch’s estimate to classify the number of cycles/passes. The paper also succeeds in classifying vibration signal generated by the spindle from the vibration signal acquired during finishing process.

This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps. Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature. Step (2) is the prediction of the selected features’ trends and the estimation of the remaining useful life (RUL) of the slew bearing. In this step, kernel regression is used with time-domain kurtosis, WD kurtosis and the LLE feature. The application of the method is demonstrated with laboratory slew bearing acceleration data.