Varying Operating Conditions Research Articles

Probing pyrolysis conversion of separator from spent lithium-ion batteries: Thermal behavior, kinetics, evolved gas analysis and aspen plus modeling

The exponential growth in the consumption of lithium-ion batteries (LIBs) has resulted in a concomitant increase in the spent LIBs. Multi-component recycling is a highly desirable and beneficial process, yet the separator component remains overlooked. This study examined the thermal behavior, kinetics, thermodynamics, and evolved products of separator pyrolysis in N2 and CO2 atmospheres. Furthermore, the conversion was modeled using Aspen Plus and a sensitivity analysis was conducted to investigate the impact of varying operating conditions. The findings indicated that the whole degradation process could be divided into three stages: below 200 °C, 200–550 °C and 550–900 °C. The evolved products at peak temperatures of 485 °C in N2 and 500 °C in CO2 were composed of olefins and alkanes. The average apparent activation energy values were determined to be 222.78 kJ mol−1 and 159.36 kJ mol−1 in the N2 and CO2 atmospheres, respectively. Thermodynamic analysis indicated that the separator decomposition was an endothermic and non-spontaneous process. Aspen Plus modeling results were in agreement with the experimental data. The sensitivity analysis revealed that an increase in temperature lead to an increased content of lighter compounds (≤C9) and a decrease in the proportion of heavier compounds (>C10) in the pyrolysis products.

Dynamic modeling and analysis of compressed air energy storage for multi-scenario regulation requirements

Compressed air energy storage (CAES) technology has received widespread attention due to its advantages of large scale, low cost and less pollution. However, only mechanical and thermal dynamics are considered in the current dynamic models of the CAES system. The modeling approaches are relatively homogeneous. CAES power stations have gradually increased the demand for auxiliary services such as frequency modulation mode and voltage regulation mode in addition to the peak regulation mode. Therefore, it is urgent to pay attention to the dynamic response characteristics at shorter time scales. Due to the lack of an accurate and complete dynamic model of the CAES system for multi-scenario adaptation requirements, the development of system optimization design and control technology has been severely limited. The paper establishes a dynamic model of advanced adiabatic compressed air energy storage (AA-CAES) considering multi-timescale dynamic characteristics, interaction of variable operating conditions and multivariate coordinated control. The simulation data is compared with the measured data of the peak regulation, frequency regulation and voltage regulation scenarios of the Jintan Salt Cave CAES (JTSC-CAES). The results show that the deviation of the dynamic model is <7 %, which verifies the validity and accuracy of the proposed AA-CAES dynamic model. The established model and research results provide the theoretical model foundation for dynamic characteristic research, system design and optimization control of the JTSC-CAES.

Dynamic responses of locomotive axle box bearings under varying operating conditions

Axle box bearings (ABBs) are a vital component of a railway locomotive, ensuring their reliable performance requires an in-depth understanding of their dynamic behaviour. This work theoretically derives a locomotive–track longitudinal–vertical coupled dynamics model that takes detailed dynamics of the ABBs into account. The model considers a number of complex interactions, such as non-linear contact and friction forces within the ABB, wheel–rail interactions, and track irregularities. The motions of ABB are coupled with the wheelset and bogie frame, allowing detailed investigation of the ABB dynamic behaviour during operation. The correctness of the proposed model is verified in both the time and frequency domains by comparing with field test. The dynamic characteristics of ABBs under varying operating conditions were comprehensively investigated. The results demonstrate that track irregularities have a non-negligible influence on the dynamic forces of the locomotive ABB. Furthermore, the axle load transfer occurs during loading conditions, which leads to significant differences in vertical and longitudinal dynamic interactions within the ABBs across wheelsets. It is clear that the operating conditions of the locomotive should be suitably considered in the dynamic assessment, operation, and maintenance of ABBs.

Operating modal parameter identification of railway vehicle bogie based on k-value optimization variational mode decomposition and second-order blind identification method

Due to the variable operating conditions and strong background noise, the accuracy of modal parameter identification of the railway vehicle bogie is low. This paper proposes a method for identifying operational modal parameters by combining the K-value optimization variational mode decomposition (KVMD) and second-order blind identification (SOBI) method (KVMD–SOBI method). Firstly, the variational mode decomposition (VMD) algorithm is used to adaptively decompose a single-channel vibration signal into intrinsic mode function (IMF) components with different scale characteristics. The feature IMF components are selected through the K-value optimization method based on energy ratio and stability graph. Then, the SOBI algorithm is used to separate the matrix composed of the feature IMF components. And the mode shape and single-mode signal are obtained. Finally, the natural frequency and damping ratio of each order mode are extracted by the single-mode parameter recognition method. Through simulation and experimental verification, it is shown that the proposed method has good noise robustness and can effectively identify the first three modal parameters of railway vehicle bogies in variable-speed operation. At the same time, the feature IMF components extracted by KVMD algorithm eliminate the sensitivity of sensor installation positions and operating conditions to detection results. Therefore, this method is an excellent operational mode identification method based on a single-channel signal with good noise and working condition robustness.

Prediction of inlet gas volume fraction of rotary vane pump under variable operational conditions with 1DCNN-TL model utilizing vibration signals

The Rotary vane pump (RVP) exhibits promising potential in the lubricating oil system of aircraft engines. The entrainment of air into the lubricating oil flowing through the pump affects the suction and discharge flow rates of the RVP. Therefore, understanding the inlet gas volume fraction (IGVF) of the RVP is crucial to assess its capability to deliver sufficient lubricating oil for ensuring the safe operation of aircraft engines. This study presents a one-dimensional convolutional neural networks (1DCNN)-based transfer learning (TL) (1DCNN-TL) prediction model to predict IGVF for the RVP using experimental data from gas-liquid two-phase flow in the RVP. The model is pre-trained using the vibration signals under a specific differential pressure between the pump inlet and outlet to enable predictions of the IGVF under varying differential pressures. The 1DCNN serves as pre-trained model for analyzing the outcomes of different transfer tasks. The effect of different frozen layer numbers on training time and prediction accuracy of the neural network is explored. Subsequently, the influence of varied source domain data on prediction accuracy is investigated, revealing that for optimal TL results, stable condition data at 200 kPa should be preferred as the source domain. Then the 1DCNN-TL prediction model of IGVF of RVP under variable operational conditions is determined. The results indicate varying iteration convergence rates of the pre-trained model under a differential pressure of 200 kPa across different transfer tasks, with a final goodness of fit exceeding 0.958 after fine-tuning the model. The model demonstrates the high prediction accuracy and applicability, which can contribute to improving the reliability and safety of aircraft engines.

Adaptive safe reinforcement learning‐enabled optimization of battery fast‐charging protocols

AbstractOptimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.

Using removable sensors in structural health monitoring: A Bayesian methodology for attachment-to-attachment uncertainty quantification

Structural responses are vulnerable to perturbations when subjected to variable environmental and operational conditions, thereby contributing to the slow uptake of Structural Health Monitoring (SHM) beyond academic research. More importantly, SHM systems using removable sensors face additional sensing variability due to the inherent uncertainty in sensor reattachment compared to permanently attached sensors. In this study, a novel probabilistic approach is developed for output-only damage detection utilizing ultrasonic guided waves. Experimental uncertainties and sensor attachment factors are simultaneously considered for the first time in a Bayesian context. The uncertainty introduced by reattached sensors is incorporated into the likelihood function in a comprehensive way. Bayesian model class selection using an adaptive sampling scheme is adopted to evaluate the evidence of discrete damage classes. A reference case to an aluminum plate with progressive notches is utilized to demonstrate the significant effects of attachment uncertainty on guided wave signals. The optimal data normalization model for guided wave processing under different attachment states is evaluated using the Mahalanobis distance. The notch length is identified accurately along with the uncertainty, even though the latent adhesive parameters cannot be estimated directly.

ANFIS-RTSMC based MPPT for a hybrid PV-diesel-battery water pumping system

This paper introduces a novel hybrid method for achieving Maximum Power Point Tracking (MPPT) in a photovoltaic-diesel-battery hybrid water pumping system driven by an induction motor drive (IMD). The innovative MPPT control strategy uniquely combines the rapid response and minimal oscillations of the Adaptive Neuro Fuzzy Inference System with the high robustness of Robust Terminal Sliding Mode Control (RTSMC). This combination ensures unprecedented steady-state resilience to changes in solar irradiance, enhancing system stability across a variety of IMD speeds, and making the system robust to parameter variations. Unlike traditional approaches, this two-stage photovoltaic (PV) setup integrates a regulated boost converter for MPPT control and a three-phase inverter using Space Vector Pulse Width Modulation (SVPWM) to regulate the DC link voltage (Vdc). Extensive MATLAB Simulink simulations demonstrate the proposed method’s superior efficiency in power extraction and its resilience under varying operating conditions, showcasing its originality and effectiveness.

SPRout-DBN: a cross domain bearing fault diagnosis method based on spatial pyramid pooling residual network-DBN

Abstract Effectively leveraging the spatial features of time series signals to improve the accuracy of bearing fault classification in neural networks presents a significant challenge. To address this issue of different operating conditions, a novel model termed spatial pyramid pooling residual network-deep belief network (SPRout-DBN) is proposed. First and foremost, the Gramian angular difference fields (GADF) are utilized to encode original vibration signals of bearings. Secondly, two-dimensional images transformed by GADF from original signals are input to a novel designed residual network with spatial pyramid pooling to extract fixed-size temporal fusion feature vectors. Finally, a deep belief network is employed for classification and cross-domain learning, enabling the identification of fault samples under varying operating conditions. The proposed method is validated by two sets of datasets from Case Western Reserve University and Jiangnan University, achieving accuracies of 99.81% and 99.0% under identical operating conditions, and 99.41% and 98.43% under different operating conditions with 40 samples. Comparative analysis indicates that the proposed SPRout-DBN remains more robust and effective compared with other methods such as K-nearest neighbors, support vector machines, LeNet-5, ResNet-18, domain adaptation networks, and domain-adversarial neural networks in diverse operating environments.

Chemical-Inspired Material Generation Algorithm (MGA) of Single- and Double-Diode Model Parameter Determination for Multi-Crystalline Silicon Solar Cells

The optimization of solar photovoltaic (PV) cells and modules is crucial for enhancing solar energy conversion efficiency, a significant barrier to the widespread adoption of solar energy. Accurate modeling and estimation of PV parameters are essential for the optimal design, control, and simulation of PV systems. Traditional optimization methods often suffer from limitations such as entrapment in local optima when addressing this complex problem. This study introduces the Material Generation Algorithm (MGA), inspired by the principles of material chemistry, to estimate PV parameters effectively. The MGA simulates the creation and stabilization of chemical compounds to explore and optimize the parameter space. The algorithm mimics the formation of ionic and covalent bonds to generate new candidate solutions and assesses their stability to ensure convergence to optimal parameters. The MGA is applied to estimate parameters for two different PV modules, RTC France and Kyocera KC200GT, considering their manufacturing technologies and solar cell models. The significant nature of the MGA in comparison to other algorithms is further demonstrated by experimental and statistical findings. A comparative analysis of the results indicates that the MGA outperforms the other optimization strategies that previous researchers have examined for parameter estimation of solar PV systems in terms of both effectiveness and robustness. Moreover, simulation results demonstrate that MGA enhances the electrical properties of PV systems by accurately identifying PV parameters under varying operating conditions of temperature and irradiance. In comparison to other reported methods, considering the Kyocera KC200GT module, the MGA consistently performs better in decreasing RMSE across a variety of weather situations; for SD and DD models, the percentage improvements vary from 8.07% to 90.29%.

Experimental evaluation of municipal solid waste air-gasification in a pilot-scale reciprocating moving-grate furnace

The increasing volume of municipal solid waste (MSW) worldwide presents significant environmental challenges, necessitating the development of efficient waste-to-energy (WtE) solutions. Among various thermochemical methods, gasification offers a promising approach for converting MSW into syngas, which can be utilized for energy generation. This study investigates the gasification characteristics of MSW in a pilot-scale reciprocating moving-grate furnace, focusing on the effect of key operating parameters such as equivalence ratio (ER), gasification temperature, and gasifying agent-staged ratio on gasification characteristics.Seven experimental schemes were tested with varying lower heating values (LHV) of MSW (ranging from 6.98 to 15.1 MJ/kg) and throughputs (ranging from 0.77 to 1.67 tons per day) to assess the adaptability and stability of the moving-grate system under different conditions. The results indicate that an ER between 0.6 and 0.7, a gasification temperature of 760 °C, and a gasifying agent-staged ratio of 7:3 are optimal for achieving a maximum energy conversion efficiency of 71.6 %. It was observed that the LHV of syngas decreases when the gasification temperature exceeds 850 °C due to increased oxidation of light hydrocarbons. Moreover, the study highlights the influence of grate moving speed on residence time and reaction completeness, which are critical for optimizing syngas yield and quality.The findings demonstrate that while the maximum energy conversion efficiency of the moving-grate system is lower than other reactor types, its lower capital and operating costs, due to the lack of dedicated feedstock pretreatment, make it a viable option for small-scale and pilot-scale applications. This study provides valuable insights into optimizing MSW gasification processes and underscores the potential of the moving-grate furnace for adaptable and cost-effective WtE applications. The novelty of this work lies in the comprehensive evaluation of the moving-grate gasification process under varied operating conditions, providing a foundation for future research on improving efficiency and reducing environmental impact in large-scale MSW management.

Advanced Phase Diversity Method for telescope static aberration compensation

Accurate calibration is essential for high-performance Adaptive Optics (AO) systems in astronomy. This paper presents a novel methodology for AO systems calibration that preserves the optical path integrity while achieving high-quality results. By eliminating the need for optical modifications, this approach simplifies system complexity and accounts for all static aberrations. The main novelty of this proposal is an advanced Phase Diversity (PD) method for estimating static optical aberrations. Unlike conventional PD techniques, this approach uses the AO system’s deformable mirror to introduce different diversities, eliminating the need for additional calibration hardware. Furthermore, the optimizer Adam is introduced for the first time in this field to estimate the optical aberrations. To evaluate the performance of this new methodology, a set of experiments under varying operating conditions and optimization parameters has been conducted. Results demonstrated that the presented methodology is capable of providing diffraction-limited corrected images with a Strehl Ratio (SR) exceeding 0.80 within 2 min. Furthermore, employing a Manhattan distance-based error function effectively balanced estimation speed and accuracy. The method demonstrated effectiveness across a wide range of aberration magnitudes, achieving an error of 3 nm in the best scenarios. This proposal represents an advancement in the identification and correction of static aberrations in telescope optical systems, directly improving the acquisition of high-quality references for AO sensing.

Multiphysics analysis of mechanical responses in micro-reactors under varied operating conditions

Micro-reactors are a promising alternative for power production applications where traditional methods may not be feasible (e.g., space missions). Their design requires critical selection of materials, especially for moderator and fuel, to ensure long-term operation and optimal performance. This research focuses on developing computational analysis models for micro-reactors, aiming to explore their structural behavior under thermal and mechanical loadings. The micro-reactor model is based on the Zero Energy Breeder Reactor Assembly (Zebra) Kilowatt (kW) reactor design conceptualized at Los Alamos National Laboratory. This study investigates the thermal and mechanical responses of three distinct reactor models considering factors, such as boundary conditions, material properties, and thermal-mechanical loading. In this study, Beryllium Oxide (BeO), Beryllium (Be), Aluminum Oxide (Al2O3), and Magnesium Oxide (MgO) are chosen as materials for the reflector. Yttrium Hydride (YH) and Zirconium Hydride (ZH) are chosen as moderators. Uranium Nitride (UN) and Uranium Molybdenum (UMo) are chosen as reactor fuel materials. The thermal analysis predicts the deformation due to the thermal expansion within the reactor under steady operating conditions at 25 kWth, mimicking the maximum rated power of the Zebra kW design. The mechanical study simulates the system's response to forced vibration loads applied along the cooling channels, matching the natural frequency of the system. The results from both analyses show that Beryllium Oxide, Yttrium Hydride, and Uranium Nitride demonstrate the most optimal stress distribution in the system compared to other configurations investigated here. The results of this study can be developed into datasets to facilitate future machine learning studies, enabling the prediction of mechanical and thermal responses for micro-reactors.

Thermodynamic analysis of a modified two-stage transcritical CO2 refrigeration cycle with an ejector and a subcooler

In the application scope of large-scale supermarkets, the practicality of transcritical CO2 refrigeration device has been confirmed to be quite satisfying. A modified two-stage transcritical CO2 refrigeration cycle with an ejector and a subcooler (MTC) is proposed in this paper. In the modified cycle, the ejector recovers expansion works and reduces irreversible losses in the throttling process. The subcooler provides subcooling degree for the CO2 entering the low-temperature (LT) evaporator, thus increasing the refrigeration capacity and improving the COP of the modified cycle. Thermodynamic analysis has shown that the exergy efficiency (ηex) and COP of MTC under given operating condition has been enhanced by 13.7 % and 14.2 % compared to BTC. The discharge temperature at the outlet of high-pressure compressor in MTC is decreased by 8.3℃. Under typical operating condition, the optimal discharge pressure of MTC is 9.07 MPa, which is lower than that of BTC. The correlation to calculate optimal discharge pressure for single-stage CO2 refrigeration cycle is also suitable for MTC under given operating conditions. The MTC has also shown better performance under variable operating conditions. For MTC, when the temperature at the outlet of gas cooler increases from 35 – 45℃, the COP and ηex are enhanced by 14.1 % - 16.1 % and 12.8 % - 14.2 % compared to BTC, respectively. When gas cooler outlet pressure decreases from 11.0 to 7.5 MPa, the COP and ηex are enhanced by 14.0 % - 22.8 % and 13.4 % - 18.7 %. As the evaporating temperature at the cold side of subcooler increases from -25 to -15℃, the COP and ηex increase from 1.82 to 1.98 and 27.4 % to 29.7 %. With the ratio of refrigeration capacity (Rec) between medium-temperature evaporator and low-temperature evaporator varies from 0.7 to 1.2, the COP and ηex are improved by 10.7 % - 16.3 % and 10.7 % - 15.4 % compared to BTC. There is the maximum exergy loss at gas cooler in the MTC, whereas that of BTC is located in the expansion valve before LT evaporator. The economic analysis shows the cost per unit of exergy of MTC is decreased by 11.5 % under typical operation condition. According to simulation results, the modified cycle has better performance in severe working conditions such as high gas cooler outlet temperature and low gas cooler outlet pressure in the given range of working conditions compared to BTC.

Feasibility of using red mud as bed material for treating N2O in fluidized bed combustion

Red mud, a type of industrial waste, currently can’t be utilized effectively, and there are no efficient methods for controlling N2O emissions in circulating fluidized bed (CFB) boilers. Based on characteristics of N2O and the components of the red mud, the red mud was used in this study as bed materials of fluidized bed to treat N2O produced by coal combustion. The decomposition effect of red mud on N2O under various operating conditions during the coal combustion process was quantified in a bubbling fluidized bed, and the decomposition of N2O and NO after the flue gas passed through the bed material was investigated. In a variety of operational conditions, the presence of red mud has been observed to exert a pronounced inhibitory effect on the formation of N2O, while concurrently exhibiting the capacity to diminish the nitrogen conversion rate of N2O to varying degrees. Comparative analysis of N2O and NO emissions from char and coal revealed different levels of reduction, with the combination of CO and red mud being the most effective method. Red mud demonstrated superior N2O removal capabilities, while CO was more effective for NO removal. The issue of increasing NO can be resolved by balancing the iron-aluminum ratio of red mud, allowing the flue gas to pass through the red mud in the CFB’s low-temperature zone, and focusing on N2O removal in the furnace.

High-temperature fretting wear behavior and microstructure stability of a laser-cladding Ti-Al-C-N composite coating meditated by variable cycle conditions

Ti-6Al-4 V titanium alloy is used for low-pressure compressor blades of aero-engine due to its high specific strength, excellent corrosion resistance and high-temperature stability. Low-pressure compressor blades are subject to fretting wear in service, which may lead to fracture failure of the blades. The preparation of wear-resistant coatings is currently an effective solution in solving the problem of fretting wear on Ti-6Al-4 V titanium alloy. However, the fretting wear resistance and microstructure stability of the coating under variable cycle fretting conditions require more attention. In this work, the fretting wear performance and microstructure stability of the Ti-Al-C-N composite coating under variable cycle fretting conditions are investigated. The results show that the parameters of variable cycle operating conditions have a notable effect on the coefficient of friction (COF). The variable frequency and variable temperature working conditions result in the alteration of the fretting operation mechanism. Whereas the variable stroke amplitude and the variable load conditions do not cause changes in the fretting operation mechanism, and both are in the gross slip region (GSR). The lowest wear rate occurs under variable stroke amplitude conditions. The wear rate is the maximum under the variable cycle temperature condition. According to the dissipated energy calculation, the difference between the two different stroke amplitudes is the biggest among all the conditions. EBSD and TEM analyses show that the subsurface of variable stroke amplitude worn scar has a more uniform strain distribution, and reveals a unique subsurface structure with a transformation from crystalline to amorphous, which contributes to improving the wear resistance. This work provides insights into coating microstructure stability and wear mechanisms under variable cycle conditions.

Potential of Wake Scaling Techniques for Vertical-Axis Wind Turbine Wake Prediction

Analytical wake models are widely used to predict wind turbine wakes. While these models are well-established for horizontal-axis wind turbines (HAWTs), the analytical wake models for vertical-axis wind turbines (VAWTs) remain under-explored in the wind energy community. In this study, the accuracy of two wake scaling techniques is evaluated to predict the change in the normalized maximum wake velocity deficit behind VAWTs by re-scaling the maximum wake velocity deficit behind an actuator disk with the same thrust coefficient. The wake scaling is defined in terms of equivalent diameter, considering the geometrical properties of the wake-generating object. Two different equivalent diameters are compared, namely the momentum diameter and hydraulic diameter. Different approaches are used to calculate the change in the normalized wake velocity deficit behind a disk with the same thrust coefficient as the VAWT. The streamwise distance is scaled with the equivalent diameter to predict the normalized maximum wake velocity deficit behind the desired VAWT. The performance of the proposed framework is assessed using large-eddy simulation data of VAWTs operating in a turbulent boundary layer with varying operating conditions and aspect ratios. For all of the cases, the momentum diameter scaling provides reasonable predictions of the VAWT normalized maximum wake velocity deficit.

Research on Small Sample Rolling Bearing Fault Diagnosis Method Based on Mixed Signal Processing Technology

The diagnosis of bearing faults is a crucial aspect of ensuring the optimal functioning of mechanical equipment. However, in practice, the use of small samples and variable operating conditions may result in suboptimal generalization performance, reduced accuracy, and overfitting for these methods. To address this challenge, this study proposes a bearing fault diagnosis method based on a symmetric two-stream convolutional neural network (CNN). The method employs hybrid signal processing techniques to address the issue of limited data. The method employs a symmetric parallel convolutional neural network (CNN) for the analysis of bearing data. Initially, the data are transformed into time–frequency maps through the utilization of the short-time Fourier transform (STFT) and the simultaneous compressed wavelet transform (SCWT). Subsequently, two sets of one-dimensional vectors are generated by reconstructing the high-resolution features of the faulty samples using a symmetric parallel convolutional neural network (CNN). Feature splicing and fusion are then performed to generate bearing fault diagnosis information and assist fault classification. The experimental results demonstrate that the proposed mixed-signal processing method is effective on small-sample datasets, and verify the feasibility and generality of the symmetric parallel CNN-support vector machine (SVM) model for bearing fault diagnosis under small-sample conditions.

A numerical model for predicting the wear of a piston ring/cylinder liner at the top dead center

The cylinder liner-piston ring interface is a critical friction pair in diesel engines, and liner wear can significantly affect engine operation. This study proposes a numerical simulation model under variable operating conditions to quantitatively predict wear at the top dead center (TDC) of the liner, and verifies the model's accuracy. It reveals the influence patterns of engine speed, ambient temperature, and ambient pressure on cylinder liner wear. The research demonstrates good qualitative and quantitative agreement between the wear simulation model and experimental data, particularly with high precision near TDC. Further analysis shows that overall TDC wear depth increases with higher engine speeds. Specifically, wear at 1500 r/min is relatively lower compared to 1300 r/min. Moreover, TDC wear depth exhibits a trend of initially decreasing and then increasing with decreasing ambient pressure and increasing ambient temperature, reaching its minimum at 70 kPa pressure and 25°C temperature.

Enhancement of tool life using magneto-rheological fluid damping and tool wear prediction through deep learning model in milling

Undesirable vibrations generated during milling significantly intensify milling tool wear and reduces tool life. Hence it is imperative to employ the effective vibration control strategy during milling to optimize the overall effectiveness of the process. Thus, the present study incorporates the magneto-rheological fluid damping for vibration mitigation during the milling to enhance the life of a cutting tool under different operating conditions. Simultaneously, the data-driven modelling are employed to predict the tool wear using real-time multi-sensor machining data under different operating conditions. Accordingly, the real-time multi-sensor data was acquired at different operating conditions during normal and vibration-damped milling. Later, the extracted tine-domain features were selected using the sequential feature selection with Random Forest Regressor to obtain the most relevant time-domain features from multi-sensor data for improving the model performance. These selected features are then fed to the different deep-learning algorithms for tool wear prediction. Thus, Magneto-rheological fluid damping significantly reduces the vibrations and cutting forces in vibration-damped milling between 65 and 80% and improves the tool life by 18–21 % compared to normal milling under different operating conditions. Moreover, among the different deep learning algorithms, hybrid Encoder-Decoder Long short-term Memory is considered the optimal choice for tool wear prediction due to highest accuracy with coefficient of determinations (R2) ranging from 0.980 to 0.995 with the lowest mean squared error between 22 and 50 μm under varying operating conditions during normal and vibration-damped milling. Thus, it facilitates the significant enhancement in the tool life under different operating conditions, and robust data-driven model for the effective implementation of predictive maintenance approach.