Behavior Of Machine Research Articles

A new sociology of humans and machines.

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human-machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human-machine and machine-machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.

The Influence of Consumption Values on the Consumption Behaviour of Washing Machine among Housewives in Kulim, Kedah

The Influence of Consumption Values on the Consumption Behaviour of Washing Machine among Housewives in Kulim, Kedah

Could Snacks Based on Lupin Be a Nutritious Treat? A Point of View.

This viewpoint article presents an opinion about snacks made with lupin species. The nutritional quality of cereal-based snacks increased significantly when lupin was added. For instance, the protein and fibre content of lupin snacks could be as high as 55.7% and 8.3% respectively, soluble fibre as high as 61.2% of total fibre, and protein digestibility close to that of casein. As for sensory evaluation, some lupin snacks were ranked even better than controls. Moreover, some lupin snacks had similar or improved rheological behaviour with respect to controls. For instance, expansion indices of 11 versus 3 for controls. In summary, by adjusting formula and processing conditions, it is possible to obtain, at the same time, a healthy and tasty snack with very good machinability and rheological behaviour. This could improve the image and concept of snacks by providing an excellent opportunity for improving the diet quality of habitual consumers of snacks. This point of view also provides suggestions for improving the nutritional, rheological, and sensory evaluation of lupin snacks.

Validation of vibration synthesis with lumped tooth forces for electric motor NVH prediction

In this paper, the vibration synthesis used for the prediction of the vibration behavior of a Permanent Magnet Synchronous Machine (PMSM) is developed with Lumped Tooth Forces (LTF) and validated against a reference model. On the one hand, the reference Finite Element (FE) method applies FE-calculated magnetic nodal forces onto a 3D FE structural model via dedicated mesh mapping. On the other hand, the vibration synthesis approach stores unitary vibration responses and uses them with decomposed forces to obtain the vibration response of the electric motor. To support the validation process, an intermediate step will also apply the LTF on the 3D FE structural model. It will be shown that the vibration synthesis offers the same accuracy as the standard FE approach, especially when used with LTF. However, the vibration responses will show discrepancies when compared with the complete set of nodal forces, which will highlight the importance of properly mapping the forces similarly in both methods.

Temperature-dependent dynamics and dislocation behavior in nanoscale machining of FeCoNiCrAl high-entropy alloys: Molecular dynamics simulation

This study investigates the impact of machining parameters on forces, thermal dynamics, dislocation behavior, and crystalline structure changes in FeCoNiCrAl high-entropy alloys during nanoscale material removal using molecular dynamics (MD) simulations. Utilizing Embedded Atom Method (EAM) and Tersoff interaction potentials, simulations were performed at cryogenic (73 K) and room (293 K) temperatures. Novel findings reveal that at 73 K, cutting velocities below 200 m/s produced the highest forces along the [001] direction, whereas at 200 m/s, the peak force shifted to [100]. Increasing velocity decreased the force along [001], while [100] exhibited an inverse relationship. At 293 K, the force remained highest along [001] across all velocities. Notably, forces at 73 K were 1.82, 1.79, and 1.58 times higher than at 293 K for velocities of 100, 200, and 300 m/s, respectively. Dislocation density, particularly 1/6<112> (Shockley) dislocations, peaked under all machining conditions, with a slight initial decrease at 293 K before a significant drop with higher cutting speeds. At 293 K and a cutting speed of 100 m/s, dislocation densities for depths of 5, 10, 15, and 20 Å were approximately 1.04, 1.54, 1.17, and 1.14 times greater than those at 73 K, respectively.

Band Relevance Factor (BRF): A novel automatic frequency band selection method based on vibration analysis for rotating machinery

Monitoring rotating machinery has become a fundamental activity in the industry given the high criticality in production processes. Extracting useful information from signals is a key factor for effective monitoring. Several studies in the areas of Informative Frequency Selection bands (IFB) and Feature Extraction/Selection have demonstrated the need to identify the bands of interest in vibration signals. However, typical methods in these areas focus on identifying bands where impulsive excitations are present or analyzing the relevance of features after signal extraction. Therefore, they do not focus on other regions that may be related to changes in the dynamic behavior of machines or faults. Furthermore, the methods generally require parameter adjustments and are not automatic. To overcome these problems, the present study proposes a new approach called Band Relevance Factor (BRF). BRF aims to perform an automatic selection of all relevant frequency bands for a vibration analysis of a rotating machine based on spectral entropy. In other words, automatically identify all frequency bands that are related to changes in the behavior of the machines or faults. The results are presented through a relevance ranking and can be visually analyzed through a heatmap. The effectiveness of the approach is validated on a synthetically dataset and on two real datasets, showing that BRF is capable of automatically identifying bands that present relevant information for the analysis of rotating machinery.

USEFULNESS OF VIBRATION ANALYSIS TECHNIQUES AND SENSORS TO IMPROVE THE MONITORING OF INDUSTRIAL EQUIPMENT

The food industry is constantly seeking advanced technologies to improve the efficiency and reliability of machinery, which is critical to improving product quality and manufacturing efficiency. This paper explores the applications of integrating vibration analysis techniques and sensors to improve machine monitoring in the food industry. Vibration analysis is a well-established technique for mechanical fault detection, providing valuable insight into the condition of rotating equipment. By incorporating advanced sensors that can capture a wide range of vibration signals the aim of this research is to improve the accuracy and sensitivity of device health monitoring. The review reviews existing literature on vibration analysis, sensor technology, and applications in the food industry. It explores how traditional vibration analysis methods can be incorporated into advanced sensor technologies such as accelerometers and other sensors. The integration of these technologies will provide a comprehensive understanding of machine behavior, leading to faster abnormal detection and predictive maintenance.

Effect of Machining Parameters on the Surface Roughness of Hot Die Steel

H13 tool steel is frequently used in hot work dies, hot forging, hot extrusion, and mold materials for casting ferrous and nonferrous materials. Surface roughness is of key importance in case of dies and moulds, as it insures the finish of the final product. After studying the work done by the researchers in field of machining and its impact over surface roughness, it was found that machining behaviour of H13 steel at the annealed state is not being studied. The surface finish obtained during turning of H13 steel are not studied on a greater scale, so the present study includes the analysis of finish (surface roughness) generated. Turning operation is carried out on the CNC machine at dry condition with an aim to find systematically the effect on surface roughness by distinct machining variables like feed rate, depth of cut and speed. RSM model using Box Behnken design is used to predict the roughness developed during turning of annealed H13 steel using carbide tool. It was found that feed rate and the depth of cut have a direct relation with the surface finish and the value of surface roughness increases with increase in these parameters. On the other side, cutting speed has a less impact over the surface roughness &amp; is inversely related.

Subtractive manufacturing of composite materials with robotic manipulators: a comprehensive review

AbstractRobotic manipulators play an innovative role as a new method for high-precision, large-scale manufacturing of composite components. However, machining composite materials with these systems presents unique challenges. Unlike traditional monolithic materials, composites exhibit complex behaviour and inconsistent results during machining. Additionally, robotic manipulator as a machine tool often associates with stiffness and vibration issues which adds another layer of complexity to this approach. By employing a comprehensive analysis and a combination of quantitative and qualitative review methodology, this review paper aims to survey diverse properties of composite materials by different categories and their interaction with machining processes. Subsequently, a survey of manufacturing techniques for composite machining following with a review in various modeling practices to capture material machining behaviour under a systematic framework is presented. Thereafter, the reviewed literature examines the errors inherent in robotic systems, alongside ongoing research efforts in modeling to characterise robot behaviour and enhance its performance. Afterward, the paper explores the application of data-driven modelling methods, with a primary focus on digital twins, in enabling real-time monitoring and process optimisation. Finally, this paper aims to identify the gap in this field and suggests the potential routes for future research and application as well as their challenges.

Experimental Determination of Influences of Static Eccentricities on the Structural Dynamic Behavior of a Permanent Magnet Synchronous Machine

In electrified vehicles, the masking noise behavior of internal combustion engines is absent, making the tonal excitation of the electric machine particularly noticeable in vehicle acoustics, which is perceived as disturbing by consumers. Due to manufacturing tolerances, the tonal NVH characteristics of the electric machine are significantly influenced at wide frequency ranges. This paper presents a systematic exploration of the influence of static eccentricity as one manufacturing tolerance on the NVH behavior of Permanent Magnet Synchronous Machines (PMSMs). The study utilizes a novel test bench setup enabling isolated variations in static eccentricity of up to 0.2 mm in one PMSM. Comparative analysis of acceleration signals reveals significant variations in the dominance of excitation orders with different eccentricity states, impacting critical operating points and dominant frequency rages of the electric machine. Despite experimentation, no linear correlation is observed between increased eccentricity and changes in acceleration behavior. Manufacturing eccentricity and deviations in rotor magnetization are discussed as potential contributors to the observed effects. The findings emphasize static eccentricity as a critical parameter in NVH optimization, particularly in electrified powertrains. However, the results indicate that further investigations are needed to explore the influence of eccentricities and magnetization deviations on NVH behavior comprehensively.

Backstepping control of DFIG wind turbine system based rotor flux observer

This paper introduces a novel approach to controlling a wind energy system based on a doubly fed induction generator (DFIG) using backstepping control. The proposed control method allows for independent control of the active and reactive power of the stator. Unlike traditional methods that rely on vector control of the stator fluxes and neglect the stator resistance, this approach takes into account the nonlinearities and coupled nature of the system. By using a nonlinear model in the Park frame, the control is more accurate and closer to the behavior of a real machine. This method has yet to consider any estimation of the rotor flux model. Using a high-gain observer can resolve the issue of measuring rotor flux. The stability of the nonlinear observer is demonstrated through the application of Lyapunov theory. The simulation results indicate that the suggested control scheme has enhanced the dynamic performance of the system.

Predictive modeling of crushing power in cone crushers with the discrete element method

In the mineral processing industry, crushers are relevant machines used for the comminution of ore. One method that has been used to model mining equipment is the discrete element method (DEM), which has proven to be an excellent tool for predicting the behavior of mineral processing machines. This paper presents a DEM model of a laboratory cone crusher with two versions: full and simplified. The simplified version — developed for simulation using fewer computer resources — is compared with the full model to analyze its usefulness. Complete and rigorous parameter calibration was performed, and the results obtained in both models were validated with experimental testing of crushing power, mass flow rate, and product size distribution; errors of less than 15% were obtained in all tests. In addition, a detailed analysis of the loads on the crusher mantle was performed using a polar coordinate representation of the crusher mantle.

Prediction-based multi-objective optimization method for 3D printing resource consumption

A prediction-based multi-objective optimization (PBMO) method is proposed in this paper to forecast and reduce 3D printing (3DP) resources on demand, including time, energy, and material. In the authors’ previous research work, a hybrid code-based and data-driven modeling (HCDM) scheme was proposed to customize the predictive models based on process parameters, material deposition paths, and machine behaviors. This study further utilizes the models as multi-objectives to be minimized, aiming at the appropriate solution of process parameters that consume the least resources. Non-dominated sorting genetic algorithm II (NSGA-II), one of the commonly used metaheuristic algorithms, is adopted to construct the PBMO framework, where the HCDM process is embedded in the fitness evaluation step. The corresponding computing program is compiled and then validated on two material extrusion (MEX) machines. Based on the optimization results, hypervolume, as a Lebesgue measure, is used to evaluate the superiorities of all near-optimal solutions, thereby recommending the best-performing solutions for real 3DP. Apart from the 3DP process, the proposed optimization method is adaptable to other mainstream computer numerical control (CNC) manufacturing processes and will guide process design to promote resource conservation for cleaner production.

Optimization of process parameters in micro electrochemical machining using TOPSIS technique with Analytical Hierarchy Process (AHP)

Electro-chemical machining (ECM) is one of the extensively used unconventional machining processes employed in industries to machine difficult-to-machine materials such as super alloys, titanium alloys, stainless steel, etc It does not impart any mechanical stresses on the workpiece. The primary requirement of the work material used in this process should be electrically conductive materials. In favour of these characteristics, Hastelloy C276 is used as a work material because it exhibits excellent physical properties over cast iron. Generally, the quality of machining mostly relies on the electrolyte (mmol L−1), feed rate (mm/rev), and duty ratio. These credential parameters are considered in this present work to enhance the machining quality, such as circularity, taper angle, and metal removal rate (MRR). The Taguchi L9 orthogonal array is used to learn machining behaviour under various circumstances. And this time, an Analytic Hierarchy Process (AHP) is used to allocate the weight for each response in the TOPSIS multi-criteria decision-making method. According to the findings, sodium nitrate (NaNo3) has a higher metal removal rate than sodium chloride (NaCl) and sodium bromide (NaBr). It will, however, result in a low-quality hole. The taper angle is inversely proportional to the feed rate.

Anomaly detection based on system text logs of virtual network functions

In virtual network environments building secure and effective systems is crucial for its correct functioning, and so the anomaly detection task is at its core. To uncover and predict abnormalities in the behavior of a virtual machine, it is desirable to extract relevant information from system text logs. The main issue is that text is unstructured and symbolic data, and also very expensive to process. However, recent advances in deep learning have shown remarkable capabilities of handling such data. In this work, we propose using a simple LSTM recurrent network on top of a pre-trained Sentence-BERT, which encodes the system logs into fixed-length vectors. We trained the model in an unsupervised fashion to learn the likelihood of the represented sequences of logs. This way, the model can trigger a warning with an accuracy of 81% when a virtual machine generates an abnormal sequence. Our model approach is not only easy to train and computationally cheap, it also generalizes to the content of any input.

Swarm learning anomaly detection framework for cloud data center using multi-channel BiWGAN-GTN and CEEMDAN

Anomaly detection is an important task for maintaining the performance of cloud data center. Traditional anomaly detection primarily examines individual Virtual Machine (VM) behavior, neglecting the impact of interactions among multiple VMs on Key Performance Indicator (KPI) data,e.g., memory utilization. Furthermore, the non-stationarity, high complexity, and uncertain periodicity of KPI data in VM also bring difficulties to deep learning-based anomaly detection tasks. To settle these challenges, this paper proposes MCBiWGAN-GTN, a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network (BiWGAN-GTN) and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). (a)The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data. (b) The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process. (c) MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components. (d) To adapt the proposed algorithm for the entire cloud data center, a cloud data center anomaly detection framework based on Swarm Learning (SL) is designed. The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline, with an F1-score of 0.96, an accuracy of 0.935, a precision of 0.954, a recall of 0.967, and an FPR of 0.203. The experiments also verify the stability of MCBiWGAN-GTN, the impact of parameter configurations, and the effectiveness of the proposed SL framework.

Dynamic modelling and efficiency prediction for forging operations under a screw press

Accurate predictions concerning a forging process can be obtained by numerical simulation, but only with a thorough knowledge of the main process variables. The material flow behavior and the interface effects are already well studied in the literature, but not the machine behavior, although it is required to estimate blow efficiency and deduce the energy actually transmitted to the workpiece. In this paper, an experimental methodology was applied to determine a spring-mass-damping model and its associated parameters for a screw press. The model and its parameters were identified with press strikes performed without billet. For validation, simulations were performed to predict blows on copper billets. The model’s predictions were in good agreement with the experimental measurements for ten consecutive blows on a copper billet. The decrease of process efficiency and the evolution from inelastic blows to elastic blows were correctly depicted by the model.

Harmonic Sequence Component Model-Based Small-Signal Stability Analysis in Synchronous Machines during Asymmetrical Faults

Power systems are complex and often subject to faults, requiring accurate mathematical models for a thorough analysis. Traditional time-domain models are employed to evaluate the dynamic response of power system elements during transmission system faults. However, only the positive sequence components are considered for unbalanced faults, so the small-signal stability analysis is no longer accurate when assuming balanced conditions for asymmetrical faults. The dynamic phasor approach extends traditional models by representing synchronous machines with harmonic sequence components, making it suitable for an unbalanced condition analysis and revealing dynamic couplings not evident in conventional methods. By modeling electrical and mechanical equations with harmonic sequence components, the study implements an eigenvalue sensitivity analysis and participation factor analysis to identify the variable with significant participation in the critical modes and consequently in the dynamic response of synchronous machines during asymmetric faults, thereby control strategies can be proposed to improve system stability. The article validates the dynamic phasor model through simulations of a single-phase short circuit, demonstrating its accuracy and effectiveness in representing the transient and dynamic behavior of synchronous machines, and correctly identifies the harmonic sequence component with significant participation in the critical modes identified by the eigenvalue sensitivity to the rotor angular velocity and rotor angle.

Optimization of a damper system for noise and vibration reduction in a PMSM

PurposeThis paper aims to optimize passive damper system (PDS) design by configuring its parameters to improve its performance and behavior in permanent magnet synchronous machines (PMSM).Design/methodology/approachFirst, the principle and effectiveness of the PDS are recalled. Second, the impact of different PDS parameters on its operation is analyzed. Third, a multi-objective optimization is proposed to explore a compromise design of PDS. Finally, the transient finite element method simulation is performed to validate the optimized design, which can ensure an excellent noise reduction effect and weaker negative impacts.FindingsA suitable capacitance value in PDS is a key to realizing the damping effect. A larger copper wire can improve the noise reduction performance of PDS and reduce its Joule losses. A compromise solution obtained from a multi-objective optimization remains the excellent noise reduction and reduces Joule losses.Originality/valueThis paper explores the impact of PDS parameters on its operation and provides an orientation of PDS optimization, which is favorable to extend its application in different electrical machines.

Quantum Boltzmann machines: Clifford algebras and asymptotic analysis

We analyze mathematical aspects related to general quantum Boltzmann machine models through representations of algebras and functional analytic techniques. In this context, based on algebraic formulation we show the existence of a symmetry group for a class of Hamiltonians based on generators of Clifford algebras and discuss its implications for the learning scheme. Also, we have developed a rigorous mathematical analysis that allows us to investigate issues related to the asymptotic behavior of quantum Boltzmann machines. Particularly, we obtain a lower bound of fidelity in terms of the upper limit of quantum relative entropy.