Speed Power Research Articles

A comparative study on ReLU Implementation using TMDFETs

In this study, we compare the implementation of the rectified linear (ReLU) activation function using transition metal dichalcogenide field-effect transistors (TMDFETs) and metal-oxide-semiconductor FETs (MOSFETs). Five TMDs - MoS 2, MoSe 2, MoTe 2, WS 2, WSe 2 along with three variants (low-power, high-performance, and multi-gate) of the MOSFETs are simulated. Three ReLU circuits utilizing these FETs are employed for the comparison. The power consumption, speed, and accuracy of the ReLU implementation are measured and compared for each circuit and each FET. Our simulation results show that the MOSFETs consume much less power than the TMDFETs and deliver more accurate ReLU functionality. However, the TMDFETs are much faster than the MOSFETs. Among the TMDFETs, the WS 2 FET stands out, as it has higher accuracy, consumes the least power and its power consumption is comparable to the MOSFETs. Additionally, WS 2 is faster compared to MOSFETs, resulting in a trade-off between power efficiency and speed. This makes WS 2 an attractive option for implementing the ReLU activation function.

An artificial neural network (ANN) modelling approach for evaluating turbidity properties of paper recycling wastewater

A pre-treatment process was evaluated in this work for wastewater from paper recycling using microwave technology followed by rapid precipitation of contaminants through centrifugation. Artificial neural networks (ANNs) were used to analyze and optimize the turbidity values. Thirty experimental runs were utilized including microwave (MW) power, duration, centrifuge time, and centrifuge speed as input variables, generated by the Central Composite Full Design (CCFD) approach. The experimental turbidity ranged from 8.1 to 19.7 NTU, while predicted values ranged from 8.4 to 19.7 NTU by ANN. The ANN model showed a robust prediction performance with low mean squared error values during training and testing. Moreover, high R2 values showed a remarkable agreement between the experimental observations and ANN predictions. The results obtained from the input values (A:150.00, B:60.00, C:15.00, D:30.00) of sample 2, which gave the lowest turbidity value, showed the most removal of pollution. The results obtained from the input values (A:250.00, B:60.00, C:7.00, D:20.00) of sample 30, which gave the highest turbidity value, showed the least removal of pollution. The results showed that increasing RPM and time of the centrifugation process significantly affected the removal of pollution in wastewater.

Design and Implementation of High Speed SRAM Cell

Static Random Access Memory's main design goals are to have a consuming low power and high speed, as technology is developing at a faster paceand this has given rise to new challenges.The high speed along with stability of SRAM are important issues to improve efficiency and performance of the system. This proposed work presents design and implementation of 1Kb 6T SRAM using CMOS technologies by using EDA tool. In this proposed work, SRAM’s cell is operated in 1V voltage. Our proposed high speed SRAM consisting 6T achieved read delay and write delay of 29.4ps & 15ps respectively. And also our designed cell has stability of 70 mV & 195 mV for read SNM and write SNM respectively

Fault Detection and Diagnosis of Three-Wheeled Omnidirectional Mobile Robot Based on Power Consumption Modeling

Three-wheeled omnidirectional mobile robots (TOMRs) are widely used to accomplish precise transportation tasks in narrow environments owing to their stability, flexible operation, and heavy loads. However, these robots are susceptible to slippage. For wheeled robots, almost all faults and slippage will directly affect the power consumption. Thus, using the energy consumption model data and encoder data in the healthy condition as a reference to diagnose robot slippage and other system faults is the main issue considered in this paper. We constructed an energy model for the TOMR and analyzed the factors that affect the power consumption in detail, such as the position of the gravity center. The study primarily focuses on the characteristic relationship between power consumption and speed when the robot experiences slippage or common faults, including control system faults. Finally, we present the use of a table-based artificial neural network (ANN) to indicate the type of fault by comparing the modeled data with the measured data. The experiments proved that the method is accurate and effective for diagnosing faults in TOMRs.

Experimental study and neural network model based prediction of layer thickness influence on LPBF IN625 single track geometry

The performance of interacting metal surfaces having sliding motion is enhanced by fabricating microfeatures on-top of the surfaces. Laser powder bed fusion (LPBF) is used to fabricate the microfeatures of IN625 on-top of a flat surface. In LPBF, geometrical characteristics of microfeatures are based on the energy supplied and the volume of material melted (layer thickness) at that instant of time. There is very few research reported in open literature on the effect of layer thickness on microfeatures geometry during LPBF. In this work, a series of single tracks is fabricated with different combinations of laser power and scanning speed for 75 μm, 100 μm, and 125 μm layer thickness and its effect on single track geometrical characteristics and microstructure was evaluated. It has been found that laser power and scanning speed have a significant influence on the track geometry irrespective of layer thickness. The minimum linear energy density of 2.5 J/mm is considered to fabricate the continuous track formation for investigated layer thickness. From statistical analysis, it is observed that the laser power and scanning speed shows higher contribution on track width and remelting depth, respectively. Whereas, layer thickness shows a higher influence on track height and contact angle of single track as compared to laser power and scanning speed. In 125 μm layer thickness, the volume of material melting is increased which encourages the gravity effect to deform the melt pool and produces larger track width compare to 100 μm layer thickness. Further, the cell spacing is gradually increased with layer thickness resulting increase in the solidification period. The shape controllability of the single track is significantly increased with layer thickness, but as keeps on increasing layer thickness due to the larger volume of molten metal, the melt pool losses ability to maintain convex shapes. An Artificial Neural Network (ANN) model is developed to understand the relationship between the LPBF process parameters and single track geometrical characteristics to predict the responses for a wide range of process parameters. The multiple response neural network model shows excellent agreement with experimental results with R2 value greater than 0.95.

Numerical simulation of free-surface evolution in laser polishing of 100 Cr6 bearing steel

Abstract This research focuses on the laser surface polishing process of 100 Cr6, employing a moving Gaussian heat source. It establishes a two-dimensional transient model for laser polishing, taking into account the integrated effects of fluid flow and temperature fields, as well as the combined influence of capillary and thermocapillary forces. The model was used to perform numerical simulations to study the evolution of surface morphology during the laser polishing process. The investigation specifically examines how laser power and scanning speed influence the quality of the polishing outcome. The results demonstrate that an optimal surface roughness of approximately 0.571 μm is attained at a laser power setting of 200 W and a scanning speed of 300 mm/s.

Control-oriented wind turbine load estimation based on local linear neuro-fuzzy models

To enhance energy production, the wind energy industry builds increasingly larger wind turbines (WT) in terms of hub height and rotor diameter. This enlargement results in higher structural loads, which emphasizes the importance of load-reducing WT control alongside the nominal control of power and rotational speed. Generally, it is not possible to directly measure these structural loads, so a controller needs to include their online estimation. Here we extend a baseline WT state estimator in the form of an Extended Kalman Filter (EKF) to include two unaccounted loads. These loads are a change of thrust in the main bearing and a yaw moment in the rotor hub. We incorporate these loads via data-driven local linear neuro-fuzzy models (LLNFM). These LLNFMs can represent nonlinear relationships while maintaining limited complexity. We use alaska/Wind to generate the underlying regression data and to perform the state estimation both in simulation. The regression data consists of nominal WT operation under turbulent conditions for different average wind speeds. We further superimpose the individual pitch angles to excite the WT. The evaluation is performed in a different scenario, using another turbulent wind field and a realistic noisy sensor configuration. While we can estimate the change of thrust in the one per revolution (1P) frequency range, we achieve more precise results for the yaw moment due to the incorporation of sensors with a strong correlation to it. The estimation of the change of thrust and the yaw moment appear to be sufficiently precise for load-reducing WT control in practically relevant frequency ranges.

Research on Simulation and Power Regulation Control Strategy of Fengning Variable-Speed Pumped Storage Unit

Abstract Variable-speed pumped storage unit demonstrates rapid power response and flexible adjustment of unit speed, significantly enhancing unit operational efficiency and the power system’s stability. This paper focuses on simulating the control strategy of the Fengning variable-speed pumped storage unit. First, the water pump/turbine and motor models are established based on the relevant parameters of the Fengning variable-speed pumped storage unit. Second, according to the distinct responsibilities of the governor and the excitation system, the control strategies of the unit in the power priority mode and speed priority mode are designed. Finally, we compare and analyze the power response characteristics of a single-machine infinite bus system under various operating conditions and control strategies. We also summarize the applicable scope and existing problems under different operating conditions.

Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations

Residual stress and plastic strain in additive manufactured materials can exhibit significant microscopic variation at the powder scale, profoundly influencing the overall properties of printed components. This variation depends on processing parameters and stems from multiple factors, including differences in powder bed morphology, non-uniform thermo-structural profiles, and inter-layer fusion. In this research, we propose a powder-resolved multilayer multiphysics simulation scheme tailored for porous materials through the process of selective laser sintering. This approach seamlessly integrates finite element method (FEM) based non-isothermal phase-field simulation with thermo-elasto-plastic simulation, incorporating temperature- and phase-dependent material properties. The outcome of this investigation includes a detailed depiction of the mesoscopic evolution of stress and plastic strain within a transient thermo-structure, evaluated across a spectrum of beam power and scan speed parameters. Simulation results further reveal the underlying mechanisms. For instance, stress concentration primarily occurs at the necking region of partially melted particles and the junctions between different layers, resulting in the accumulation of plastic strain and residual stress, ultimately leading to structural distortion in the materials. Based on the simulation data, phenomenological relation regarding porosity/densification control by the beam energy input was examined along with the comparison to experimental results. Regression models were also proposed to describe the dependency of the residual stress and the plastic strain on the beam energy input.

Long Jump Ability: Analyze of Leg Explosive Power and Running Speed for Junior High School Students

Long Jump Ability: Analyze of Leg Explosive Power and Running Speed for Junior High School Students

A Comparative Analysis of Low and High SiC Volume Fraction Additively Manufactured SiC/Ti6Al4V(ELI) Composites Based on the Best Process Parameters of Laser Power, Scanning Speed and Hatch Distance.

Silicon carbide (SiC) exhibits intriguing thermo-physical properties such as higher heat capacity and conductivity, as well as a lower density than Ti6Al4V(ELI). These properties make SiC a good candidate for the reinforcement of Ti6Al4V(ELI) with respect to its use as a heat shield in aero turbines to increase their efficiency. The traditional materials used in aircraft structures were required to have a combination of good mechanical properties such as strength, stiffness, and hardness and low weight, as well as low thermo-physical properties such as coefficient of thermal expansion (CTE) and thermal conductivity. The alloy Ti6Al4V(ELI) has a density of 4.45 g/cm3, which is lower than that of structural steel (7.4 g/cm3) and higher than that of aluminium (2.5 g/cm3). Lower density benefits light weighting. Aluminium is the lightest of the traditional materials used but has relatively low strength. The CTE of SiC of 4.6 × 10-6/K is lower than that of Ti6Al4V(ELI) of 8.6 × 10-6/K, while the density of SiC of 3.21 g/cm3 is lower than that of Ti6Al4V(ELI) of 4.45 g/cm3. Therefore, from the theory of composites, SiC/Ti6Al4V(ELI) composites are expected to have lower densities and CTEs than those of Ti6Al4V(ELI), thus providing for lightweighting and less thermal related buckling or separation at their joints with carbon/epoxy resin panels. The specific strength, stiffness, and Knoop hardness of SiC of 75-490 kNm/kg, 132 MNm/kg, and 600-3800 GPa, respectively, are generally larger than those of Ti6Al4V(ELI) of 211 KNm/kg, 24 MNm/kg, and 880 GPa, respectively. Therefore, investigating reinforcement of Ti6Al4V(ELI) with SiC particles is worthwhile as it will lead to the formation of composites that are stronger, stiffer, harder, and lighter, with lower values of CTE. For additive manufacturing, this requires initial studies to optimise the process parameters of laser power and scanning speed for single tracks. To print single tracks in the present work, different laser powers ranging from 100 W to 350 W and scanning speeds ranging from 0.3 m/s to 2.7 m/s were used for different SiC volume fraction values of values. To print single layers, different values of hatch distance were used together with the best values of laser power and scanning speed determined elsewhere by the authors for different volume fractions of SiC. Through optical microscopy, the built tracks and their cross sections were examined. By using laser power and scanning speeds of 200 W and 1.2 m/s, and 150 W and 0.8 m/s, respectively, the best tracks at 5% and 10% volume fractions were obtained, whereas the best tracks at 25% volume fraction were achieved using a laser power of 200 W and a scanning speed of 0.5 m/s. Furthermore, the results showed that the maximum SiC volume percentage of 30% resulted in limited or no penetration. Therefore, it is concluded from the study that parts with improved mechanical properties can be produced at SiC volume fractions ranging from 5% to 25%, while parts produced at the high volume fraction of 30% would have unacceptable mechanical qualities for the final part.

Implementation of inverse method for identification of process parameters in a laser heating process

Changes in process parameters can alter the temperature data of a solid body during the laser heating process. This paper proposes an inverse method to determine the laser heating parameters, namely, sheet width, sheet thickness, laser power, and scan speed of the work specimen for the given temperature response which is assigned by the user. The method uses an analytical tool on moving heat source as a forward model capable of real-time temperature prediction of the sheet under the laser heating process. The effectiveness of the present forward model that incorporated the temperature-dependent material properties is tested by experimental findings performed on Al 6061-T6 sheets. Next, an iterative search process based on heuristic method is carried out for satisfying a prescribed temperature response by the optimization of unknown parameters. To illustrate the implementation and assess the practicality of the inverse method, two examples based on laser heating applications are used. The method proposed herein recover the unknowns for the prescribed temperature response after a few iterations, provides an efficient way to optimize the laser heating process. Additionally, the effect of measurement error on the findings of the inverse problem is addressed.

Model-Based Sensitivity Analysis of the Temperature in Laser Powder Bed Fusion.

To quantitatively evaluate the effect of the process parameters and the material properties on the temperature in laser powder bed fusion (LPBF), this paper proposed a sensitivity analysis of the temperature based on the validated prediction model. First, three different heat source modes-point heat source, Gaussian surface heat source, and Gaussian body heat source-were introduced. Then, a case study of Ti6Al4V is conducted to determine the suitable range of heat source density for the three different heat source models. Based on this, the effects of laser processing parameters and material thermophysical parameters on the temperature field and molten pool size are quantitatively discussed based on the Gaussian surface heat source. The results indicate that the Gaussian surface heat source and the Gaussian body heat source offer higher prediction accuracy for molten pool width compared to the point heat source under similar processing parameters. When the laser energy density is between 40 and 70 J/mm3, the prediction accuracy of the Gaussian surface heat source and the body heat source is similar, and the average prediction errors are 4.427% and 2.613%, respectively. When the laser energy density is between 70 and 90 J/mm3, the prediction accuracy of the Gaussian body heat source is superior to that of the Gaussian surface heat source. Among the influencing factors, laser power exerts the greatest influence on the temperature field and molten pool size, followed by scanning speed. In particular, laser power and scan speed contribute 38.9% and 23.5% to the width of the molten pool, 39.1% and 19.6% to the depth of the molten pool, and 38.9% and 21.5% to the maximum temperature, respectively.

Solidification of Ni-Mn-Ga magnetic shape memory alloy built via laser powder bed fusion on a single crystal substrate

Ni-Mn-Ga-based magnetic shape memory (MSM) alloys have significant potential as fast actuation materials due to their ability to undergo large magnetic-field-induced strains in an external applied magnetic field. This study explored the manufacture of Ni-Mn-Ga via the laser powder bed fusion (PBF-LB/M) additive manufacturing process, aiming to determine the parameter combinations that enhance the development of a crystallographically oriented coarse grain structure. In the first part of the study, the single tracks were melted on pre-heated single-crystalline Ni-Mn-Ga substrates by varying the applied laser power and scanning speed. This experiment revealed the existence of a parameter window where epitaxial solidification of the single-track can be achieved. Additionally, the results demonstrated that preheating prevents cracking of the single-crystal substrate. Subsequent experiments using a thin-walled 3D geometry demonstrated that this epitaxial structure was replicated across multiple layers, with the built samples exhibiting crystallographically oriented oligocrystalline structures following a high-temperature homogenization treatment. PBF-LB/M shows high potential for facilitating the additive manufacturing of oriented coarse grain oligocrystals for the production of microscale actuators.

Phase transformation behavior and mechanical properties of NiTi shape memory alloys fabricated by directed laser deposition

Directed laser deposition shows promise as a method for fabricating complex structure NiTi shape memory alloy parts in engineering, aerospace, automobile, and biomedical fields. The dislocation density has an impact on phase transformation behavior and mechanical properties of NiTi manufactured by directed laser deposition. In this work, NiTi samples were produced by directed laser deposition, and the mechanism of martensitic phase transformation and its interactions with dislocation density were revealed through molecular dynamics. Laser power and scanning speed are the main factors affecting dislocation density, the phase compositions of the samples are B2, B19′, and Ti2Ni phase, and the samples with dislocation density below 2 × 1015 m−2 show single-step B2–B19′ martensitic transformation, stress plateau and improved elongation, the samples with dislocation density above the value exhibit no significant transformation peak and no stress plateau. The movement of (100) twin planes and the growth of advantageous variants are observed in the simulation which can explain stress-induced martensitic reorientation and the stress plateau phenomenon. The bending test suggested that the sample under the optimal parameters has an 85 % recovery ratio within 8 % pre-strain.

Taguchi-Grey relational analysis driven multi-objective optimization of weld bead geometry and hardness in laser welded Ti6Al4V Alloy

AbstractThis study used Taguchi-based Grey relational analysis to optimize the bead geometry (bead width and length) and the hardness of laser welded 3 mm thick Ti6Al4V alloy sheets joined using the butt configuration. Firstly, the L9 (32) orthogonal array, with varying laser power and welding speed, was employed to optimize the input parameters (bead length, width, and hardness). The Taguchi optimization showed that the welding speed and laser power have a significant effect on the hardness of the welds and that laser power has a significant impact on the bead length. Meanwhile, the welding parameters had little effect on the bead width. These results were further affirmed with the Analysis of Variance (ANOVA). The Grey relational multi-objective optimization shows the optimized parameters for all responses to be 3 kW laser power and 2.2 m/min welding speed. It is important to note that the combination of Taguchi and Grey relational analysis and ANOVA is useful in optimizing welding parameters to achieve suitable weld properties.

Prediction analysis and an experimental investigation of precision process of CO2-laser micro-turning for Armox 500 T accompanied via N2 gas

The production of micro grooves on extremely hard workpieces like Armox 500 T, commonly used in military sectors like armored protective vehicles, poses challenges in conventional machining processes. Accordingly, an innovative technique called CO2 laser turning process (LTP) assisted by N2 gas was used for manufacturing micro grooves on Armox 500 T to confront the restrictions of traditional machining processes. This study was concerned with investigating the impact of various operating parameters related to the thermal grooving machining process such as: (i) laser power (P), (ii) gas pressure (GP), (iii) feed rate (F), and (iv) motor speed (S). These parameters were examined under various operating conditions like: (i) cutting depth (DC), (ii) upper width cut (UC), (iii) bottom width cut (BC), (iv) roundness error (RE), and (v) metal removal rate (MRR). Thus, response-surface-methodology (RSM) was used to examine the experimental results, enabling the mathematical modeling of the results, and reducing the need for additional experiments. The findings revealed that the optimal combination of condition settings for the CO2-LTP of Armox 500 T (assisted by N2 gas) included a CO2 laser power of 3000 Watts, N2 gas pressure of 17.2370 bar, a feed rate of 400 mm/min, and a motor speed of 10 RPM. Furthermore, the optimized combinations of thermal machining conditions resulted in increasing MRR, DC, BC, UC, DC, and reducing RE, with values of 0.0163 µm, 0.2333 mm, 0.2511 mm, 0.5241 mm, and 19.403 µm, respectively. Additionally, the correctness of the optimization procedure was demonstrated by the difference between the optimal experimental findings and the forecasted magnitudes for MRR, DC, BC, UC, and RE, which were up to 7.9 %, 6.71 %, 6.1 %, 3.6 %, and 2.98 %, respectively.

Effects of process parameters and geometry on dimensional accuracy and surface quality of thin strut heart valve frames manufactured by laser powder bed fusion

Laser powder bed fusion (LPBF) is one of the most popular metal additive manufacturing technologies, which has found its applications in high-value sectors such as aerospace and biomedical devices. Some recent studies on the LPBF of stents have demonstrated its feasibility in the fabrication in thin strut structures including heart valve frames, as used in transcatheter aortic valve implantation (TAVI) for the treatment of severe aortic stenosis. The state of the art method of laser cutting TAVI frame limits the scope for novel concepts which are made possible by additive manufacturing. However, the surface quality and dimensional accuracy of LPBF parts are lower than that produced by laser cutting. To start the development of new TAVI concepts, the feasibility of manufacturing thin frames by LPBF has been investigated based on the SAPIEN 3 frame by Edwards Lifesciences. In this study, simplified frames with strut size from 0.3 to 0.5 mm have been successfully manufactured. The effects of strut size, strut angle, laser power and scan speed on the dimensional accuracy and surface quality were systemically studied. In addition, a representative SAPIEN 3 frame was manufactured and assessed with high-resolution X-ray CT scans. Good overall dimensional accuracy and low porosity were obtained for the SAPIEN 3 frame. However, inclined struts were found to have relatively low dimensional accuracy and poor surface quality.

Achievement of a Parameter Window for the Selective Laser Melting Formation of a GH3625 Alloy.

In the selective laser melting (SLM) process, adjusting process parameters contributes to achieving the desired molten pool morphology, thereby enhancing the mechanical properties and dimensional accuracy of manufactured components. The parameter window characterizing the relationship between molten pool morphology and process parameters serves as an effective tool to improve SLM's forming quality. This work established a mesoscale model of the SLM process for a GH3625 alloy based on the discrete element method (DEM) and computational fluid dynamics (CFD) to simulate the forming process of a single molten track. Subsequently, the formation mechanism and evolution process of the molten pool were revealed. The effects of laser power and scanning speed on the molten pool size and molten track morphology were analyzed. Finally, a parameter window was established from the simulation results. The results indicated that reducing the scanning speed and increasing the laser power would lead to an increase in molten pool depth and width, resulting in the formation of an uneven width in the molten track. Moreover, accelerating the scanning speed and decreasing the laser power cause a reduction in molten pool depth and width, causing narrow and discontinuous molten tracks. The accuracy of the simulation was validated by comparing experimental and simulated molten pool sizes.

Gear Train Optimization of High Speed Machinery by using CATIA and ANSYS

This project aims to optimize gear trains for high-speed machinery using CATIA and ANSYS. Gear trains are crucial in various mechanical systems, especially in high-speed applications where efficiency and reliability are essential. The objective is to combine CATIA for designing and modelling gear trains with ANSYS for analysing their performance under high rotational speeds. Helical gear train use for Reverses Motion design as per requiment of power and motor speed. Same design check for maximium load by load on pinion taken as step of 13K, 15K, 20K, 25K and 30K Newtons are applied on helical gear and check the stress and other parameters material High speed steel. Dynamic analysis model also be carried for mode frequency Keywords: Gear trains, Finite element analysis, CATIA, ANSYS.