Search

This study integrates a hydrodynamic model with a structural analysis model to investigate the behavior of a reinforced concrete (RC) frame building under the influence of various hydrodynamic variables, such as debris flow materials, flow intensity, and flow direction. The impact force generated during the first phase of fluid-structure interaction is significantly larger around (20 ÷ 30) % than that in the steady phase, resulting in a substantially bigger maximum value of bending moment (Mmax) and displacement (δ) in every RC column. An increase in flow intensity and solid concentration leads to a larger impact load, resulting in higher values of Mmax and δ. Furthermore, the flow that passes through the porous exterior of the house poses a greater risk than the flow that affects the non-porous side of the house. By comparison with two Vietnamese standards of bending moment and displacement, all columns of the target building are susceptible to collapse in all scenarios when taking into account the bending moment. Nevertheless, in relation to the key threshold of displacement, the building is likely to remain secure.

This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensive monitoring of key parameters related to the health, well-being, and productivity of dairy cows. The proposed system employs 3D imaging sensors to capture detailed information about various parts of dairy cows, generating accurate, high-resolution point clouds. A robust automated algorithm has been developed to process these point clouds and extract relevant metrics such as dairy cow stature height, rump width, rump angle, and front teat length. Based on the measured data combined with expert assessments of dairy cows, the quality indices of dairy cows are automatically evaluated and extracted. By leveraging this technology, dairy farmers can gain real-time insights into the health status of individual cows and the overall herd. Additionally, the automated analysis facilitates efficient management practices and optimizes feeding strategies and resource allocation. The results of field trials and validation studies demonstrate the effectiveness and reliability of the automated 3D point cloud approach in dairy farm environments. The errors between manually measured values of dairy cow height, rump angle, and front teat length, and those calculated by the auto-measurement algorithm were within 0.7 cm, with no observed exceedance of errors in comparison to manual measurements. This research contributes to the burgeoning field of precision livestock farming, offering a technological solution that not only enhances productivity but also aligns with contemporary standards for sustainable and ethical animal husbandry practices.

This study proposes a low-cost and reliable smart fire alarm system that utilizes ultraviolet (UV) detection technology with an aspherical lens to detect fires emitting photons in the 185–260 nm range. The system integrates the aspherical lens with an accelerator and a digital compass to determine the fire source’s direction, allowing for safe evacuation and effective firefighting. Artificial intelligence is employed to reduce false alarms and achieve a low false alarm rate. The system’s wide detection range and direction verification make it an effective fire detection solution. Upon detecting a fire, the system sends a warning signal via Wi-Fi or smartphone to the user. The proposed system’s advantages include early warning, a low false alarm rate, and detection of a wide range of fires. Experimental results validate the system’s design and demonstrate high accuracy, reliability, and practicality, making it a valuable addition to fire management and prevention. The proposed system utilizes a parabolic mirror to collect UV radiation into the detector and a simple classification model that uses Fourier transform algorithm to reduce false alarms. The results showed accuracies of approximately 95.45% and 93.65% for the flame and UVB lamp, respectively. The system demonstrated its effectiveness in detecting flames in the range of up to 50 m, making it suitable for various applications, including small and medium-sized buildings, homes, and vehicles.

The study evaluates the surface water quality of the Bach Dang river basin. During the rainy season, the temperature, pH, EC, BOD _5 , COD, coliforms, ammonium and phosphate values were 27 °C–37 °C, 6.6–8.9, 300–2200 μ S cm ^−1 , 5–25 mg l ^−1 , 5–26 mg l ^−1 , 0–3000 MPN/100 mL, below 0.01 mg l ^−1 , and 0.02 mg l ^−1 , respective. During the rainy and dry seasons, Zn, Pb, Cr, As, Fe and Mn levels were below drinking water limits. In the rainy season, the water quality index varied from 76 to 91, signifying medium to excellent water quality. The most outstanding water quality sites were the QN12, QN18, and QN22. During the dry season, most areas had moderate to excellent (WQI values between 61 and 93), except for QN01, QN03, QN04, QN05, QN06, QN07, QN11, QN16, QN17, QN19, and QN23, where it dropped to moderate (61–73). The temperatures were greater and higher EC values in the southeast. The DO concentrations were higher in the northwest and southeast in both seasons. The southern and southeastern regions exhibited more excellent COD and BOD _5 values than the western, southwest, north, and northeast regions. The ammonium concentrations were higher in the southeast. During the dry season, the phosphate levels exceeded the permissible limit in the west, southeast, east, and northeast. Coliforms were below acceptable in both seasons, with greater levels in the northwest and northeast during the dry season. In addition, the Zn and Pb are favorably associated with the As, Mn, ammonium and phosphate in the rainy season. The COD positively correlated with the coliforms and BOD _5 . In the dry season, the pH inversely correlated with the Pb and Fe positively. The ammonium correlated negatively with the DO and positively with the phosphates. The pH negatively connected with the alkalinity, while the As positively correlated with the BOD _5 and COD.

Three-dimensional point cloud generation systems from scanning data of a moving camera provide extra information about an object in addition to color. They give access to various prospective study fields for researchers. With applications in animal husbandry, we can analyze the characteristics of the body parts of a dairy cow to improve its fertility and milk production efficiency. However, in the depth image generation from stereo data, previous solutions using traditional stereo matching algorithms have several drawbacks, such as poor-quality depth images and missing information in overexposed regions. Additionally, the use of one camera to reconstruct a comprehensive 3D point cloud of the dairy cow has several challenges. One of these issues is point cloud misalignment when combining two adjacent point clouds with the small overlapping area between them. In addition, another drawback is the difficulty of point cloud generation from objects which have little motion. Therefore, we proposed an integrated system using two cameras to overcome the above disadvantages. Specifically, our framework includes two main parts: data recording part applies state-of-the-art convolutional neural networks to improve the depth image quality, and dairy cow 3D reconstruction part utilizes the simultaneous localization and calibration framework in order to reduce drift and provide a better-quality reconstruction. The experimental results showed that our approach improved the quality of the generated point cloud to some extent. This work provides the input data for dairy cow characteristics analysis with a deep learning approach.

Silica nanoparticles (SiO2 NPs) synthesized by the Stober method were used as drug delivery vehicles. Doxorubicin hydrochloride (DOX·HCl) is a chemo-drug absorbed onto the SiO2 NPs surfaces. The DOX·HCl loading onto and release from the SiO2 NPs was monitored via UV-VIS and fluorescence spectra. Alternatively, the zeta potential was also used to monitor and evaluate the DOX·HCl loading process. The results showed that nearly 98% of DOX·HCl was effectively loaded onto the SiO2 NPs’ surfaces by electrostatic interaction. The pH-dependence of the process wherein DOX·HCl release out of DOX·HCl-SiO2 NPs was investigated as well. For comparison, both the free DOX·HCl molecules and DOX·HCl-SiO2 NPs were used as the labels for cultured cancer cells. Confocal laser scanning microscopy images showed that the DOX·HCl-SiO2 NPs were better delivered to cancer cells which are more acidic than healthy cells. We propose that engineered DOX·HCl-SiO2 systems are good candidates for drug delivery and clinical applications.

Optical spectrometers and sensors have gained enormous importance in metrology and information technology, frequently involving the question of size, resolution, sensitivity, spectral range, efficiency, reliability, and cost. Nanomaterials and nanotechnological fabrication technologies have huge potential to enable an optimization between these demands, which in some cases are counteracting each other. This paper focuses on the visible and near infrared spectral range and on five types of optical sensors (optical spectrometers): classical grating-based miniaturized spectrometers, arrayed waveguide grating devices, static Fabry–Pérot (FP) filter arrays on sensor arrays, tunable microelectromechanical systems (MEMS) FP filter arrays, and MEMS tunable photonic crystal filters. The comparison between this selection of concepts concentrates on (i) linewidth and resolution, (ii) required space for a selected spectral range, (iii) efficiency in using available light, and (iv) potential of nanoimprint for cost reduction and yield increase. The main part of this review deals with our own results in the field of static FP filter arrays and MEMS tunable FP filter arrays. In addition, technology for efficiency boosting to get more of the available light is demonstrated.

The spring-back phenomenon is one of the key problems in sheet metal forming process, especially with a complex shape. In this paper, the experiments of spring back have been first carried out to study the effect of punch strokes (different bending angles) on the spring-back angle for 6 (mm) thickness of SS400 sheet steel by V-bending process. The experimental results are then validated by finite element analysis (FEA) of ABAQUS software. To simulate spring-back of V-bending, deformed results of V-shape from a dynamic forming simulation in Abaqus/Explicit would be imported into Abaqus/Standard, and then a static analysis will calculate the spring-back of V-bending. Simulation studies have shown that there are different variations in the initial bending angle and the spring-back angle after the removal of the bending force based on isotropic and kinematic hardening models. Therefore, the combined hardening model has been proposed to overcome the mismatch between prediction results and the corresponding experiments. Based on the accuracy of FEM simulation, this study also verifies the effect of parameters such as a thickness of the sheet, punch radius and punch stroke on the spring-back angle in order to select the optimum input parameters by using Taguchi method.

This research studies the robustness of permanence and the continuous dependence of the stationary distribution on the parameters for a stochastic predator–prey model with Beddington–DeAngelis functional response. We show that if the model is extinct (resp. permanent) for a parameter, it is still extinct (resp. permanent) in a neighbourhood of this parameter. In the case of extinction, the Lyapunov exponent of predator quantity is negative and the prey quantity converges almost to the saturated situation, where the predator is absent at an exponential rate. Under the condition of permanence, the unique stationary distribution converges weakly to the degenerate measure concentrated at the unique limit cycle or at the globally asymptotic equilibrium when the diffusion term tends to 0. [ABSTRACT FROM AUTHOR]

The pursuit of precision machining in engineering materials has ignited an imperative to advance electrical discharge machining (EDM) techniques. This study is a deep dive into the domain of micro-electrical discharge machining (μ EDM) with a specific focus on the utilization of carbon-coated tool electrodes. The primary objective is to elucidate the influence of carbon-coated electrodes on vital machining parameters, including machining depth (Z coordinate), tool wear rate (TWR) and overcut (OC) in μ EDM operations. Moreover, the investigation extends to scrutinizing surface characteristics generated by μ EDM with carbon-coated tool electrodes. To enhance performance, carbon-coated tungsten carbide tool electrodes are employed for machining titanium alloy (Ti–6Al–4V) workpieces. The optimization process seeks to identify the optimal combination of process parameters using a Taguchi-DEAR-based multi-response approach. Experimental findings reveal that integrating carbon-coated electrodes significantly improves the μ EDM process, leading to enhanced surface performance metrics. Notably, this study identifies specific parameter settings that effectively optimize machining quality indicators. By achieving a voltage of 160 V, a capacitance of 10,000 pF and a tool rotation of 600 RPM, the research contributes valuable insights into the intricate realm of μ EDM, highlighting the potential for enhanced surface performance through strategic parameter manipulation. [ABSTRACT FROM AUTHOR]

Deep drawing processes play a pivotal role in the manufacturing of sheet and shell products, making it a widely adopted method. This research employs numerical simulations to investigate the impact of various process parameters on the fracture height of cylindrical cups made from SECC (Steel Electrogalvanized Commercial Cold rolled) material. Specifically, it examines parameters such as blank holder force (BHF), punch corner radius (Rp), die corner radius (Rd), and punch-die clearance (Wc). The study extends to optimizing fracture height, offering a solution to this challenge. Subsequently, the selected parameters are validated through experimental deep drawing of cylindrical cups, resulting in a minimal deviation of 1.55% between simulation and experiment outcomes. A precise mathematical equation is developed to estimate fracture height under diverse machining conditions, with a maximum deviation of 4.52% observed between the mathematical model and simulation. These findings represent a substantial advancement in deep drawing processes technology, particularly in reducing error rates during the production of cylindrical cups. [ABSTRACT FROM AUTHOR]

This study aims to investigate the interplay between cutting speed and Thermal-Assisted Machining (TAM) concerning surface roughness during the high-speed machining of SKD11 steel. The integration of pre-cutting workpiece heating introduces a temperature factor that intricately affects surface roughness. The primary objective is to ascertain optimal speed and temperature ranges that synergistically enhance machining efficiency, curtail costs, and elevate surface quality. The experimental protocol initiates with room temperature milling of SKD11 steel, progressively elevating the temperature gradient to systematically appraise temperature's impact on surface roughness under both conventional and elevated cutting speeds. Subsequent experimentation, conducted within specific temperature thresholds, entails stepwise augmentation of cutting speed to elucidate the influence of highspeed conditions on surface roughness. The ensuing analysis meticulously examines the ramifications of distinct cutting speed intervals on surface roughness. Ultimately, the study furnishes pragmatic recommendations for judiciously selecting cutting speeds and heating temperature parameters across diverse machining scenarios. [ABSTRACT FROM AUTHOR]

In powder-mixed electrical discharge machining (PMEDM), the application of low-frequency vibrations to a workpiece is considered beneficial to the machining quality. This study evaluates the average surface roughness (Ra) of SKD61 steel workpieces subjected to low-frequency vibrations during PMEDM using a Cu electrode and titanium powder. Specifically, Taguchi's L25 experimental design method is used to evaluate the influence of the following process parameters: current, pulse-on time, concentration, push pressure, frequency, and amplitude. The analysis of variance technique is used to determine the minimum Ra (Ra min ) value. The results show that Ra is most and least strongly influenced by I and A , respectively. Further, Ra min is found to be 1.03 μ m. [ABSTRACT FROM AUTHOR]

Depth of machining and dimensional accuracy are important parameters in assessing the quality of microelectrical discharge machining (EDM); therefore, enhancement of these two entities is highly essential. The optimal process parameters in microEDM are significantly influenced by the electrode material, which directly affects the machining quality. In this study, depth of machining (Z co-ordinate) and overcut (OVC) in microEDM with carbon-coated electrode were selected; and capacitance (C), voltage (V), and rotary tool (RT) were the process parameters. The experimental work was performed on a titanium alloy (Ti-6 Al-4 V) with a thin film-coated microtool electrode. The surface of the tungsten carbide microtool electrode was coated with carbon and the thickness of the coating was approximately five microns. The results show that the Taguchi–multi-objective optimization based on ratioanalysis (MOORA) — analytic hierarchy process (AHP) is the best combination for performing a simple and concise calculation in the machining process; and U = 1 4 0 V results in higher machining efficiency with C = 1 0 0 0 0 pF and RT = 2 0 0 rpm. Our results show that the quality of the machined surface and machining accuracy in microEDM using coated electrode at optimal parameters are good. [ABSTRACT FROM AUTHOR]

Motion cueing algorithms are used to produce a motion which feels as realistic as possible while remaining in the limited workspace of driving simulators. Several optimal motion cueing algorithms were developed to improve both the exploitation of the workspace of a driving simulator and the realistic of the simulated motion. In the dynamics model of the optimal motion cueing algorithms, several kinds of motion-sensory systems are integrated to optimize the simulated motion sensation. However, most previous works have just focused on the visual and vestibular system. The mathematical model of the proprioceptive system, that also senses the non-visual motion, has rarely been concerned. In this paper, a novel optimal motion cueing algorithm, which integrates model of the proprioceptive system, is developed to reduce the false cues from muscle spindle of head/neck system sensing lateral tilted angle. The optimal motion cueing algorithm has a significant effect on the pilot's perception when the tilted angle is rather large. An example of the simulation of a roller coaster running along a planar S-curve trajectory with only lateral acceleration is investigated with current motion cueing algorithms and optimal motion cueing algorithm. Several objective criteria were introduced to evaluate the simulated perception of all investigated motion cueing algorithms. The results demonstrate that optimal motion cueing algorithm is better than current motion cueing algorithms in most criteria and also sub-criteria. [ABSTRACT FROM AUTHOR]

The aim of this study involved evaluating and predicting forming limit curves of boron steel 22MnB5 sheet at elevated temperatures. A finite-element method simulation was adopted based on ductile fracture criteria and simple experiments at elevated temperatures. First, tensile experimental data and ductile fracture criterions of Johnson–Cook and ductile void growth models were input to ABAQUS/Explicit software to predict and compare the same with fracture occurrence in experiments performed via Hecker's punch stretching tests at room temperature. Subsequently, punch stretching test data at room temperature were added to correct the fracture strain locus in the space of the stress triaxiality and the equivalent strain following the ductile void growth model. After confirming the accuracy of the forming limit curve prediction at room temperature, fracture strain loci at high temperatures using ductile void growth model were determined based on the average ratio between the fracture equivalent plastic strains at room temperature as well as higher temperatures. Finally, Hecker's punch stretching tests were numerically simulated to predict forming limit curve(s) of boron steel 22MnB5 sheet at high temperatures. [ABSTRACT FROM AUTHOR]

In the present study, four quality characteristics of the electrical discharge are simultaneously presented and optimized using titanium powder mixed electric discharge machining. The Taguchi method and the grey relational analysis are applied to the processing parameters to investigate the following: workpiece material, tool material, polarity, pulse-on time, current, pulse-off time, and powder concentration. The combination of the Taguchi method and grey relational analysis is applied to optimize simultaneously four quality characteristics of powder mixed electric discharge machining, including material removal rate, tool wear rate, surface roughness, and microhardness surface. Optimal results by the Taguchi-grey relational analysis show that both surface roughness and tool wear rate decrease, while both material removal rate and microhardness surface increase, respectively. This approach proves effective in terms of improving the processing efficiency of the study parameters. The results from both optimization calculations and experimentation demonstrate high accuracy and efficiency. Furthermore, powder mixed electric discharge machining has improved significantly. The concentration of titanium powder is the processing parameter with the strongest influence on the efficiency of powder mixed electric discharge machining. [ABSTRACT FROM AUTHOR]

Currently, the finite element (FE) method simulation technique is being widely applied to optimize the forming process. To achieve high-accuracy FE simulation results, the identification of material properties and deformation characteristics such as yield criteria is important. In this study, characterisation of plastic deformation behavior of Ti–6Al–4V titanium alloy using anisotropic sheets were carried out using, biaxial tensile tests at 600 °C with a cruciform specimen. The test was performed until the specimen was broken down. The experimental results for the yield locus were first compared with other theoretical models to show the mismatch of the conventional function. A new yield function was then proposed in this study to describe not only the anisotropic but also the asymmetric behavior between the tension and compression of Ti–6Al–4V titanium alloy sheets using a fitting equation to determine the material constants of the yield function. Finally, yield loci at other elevated temperatures were obtained based on the relationship between tension yield stress and temperature. [ABSTRACT FROM AUTHOR]

To predict the ductile fracture of a magnesium alloy sheet when using rotational incremental forming, a combined kinematic and isotropic hardening law is implemented and evaluated from the histories of the ductile fracture value ( I) using a finite element analysis. Here, the criterion for a ductile fracture, as developed by Oyane ( J. Mech. Work. Technol., 1980, vol. 4, pp. 65–81), is applied via a user material based on a finite element analysis. To simulate the effect of the large amount of heat generation at elements in the contact area due to the friction energy of the rotational tool-specimen interface on the equivalent stress-strain evolution in incremental forming, the Johnson–Cook (JC) model was applied and the results compared with equivalent stress-strain curves obtained from tensile tests at elevated temperatures. The finite element (FE) simulation results for a ductile fracture were compared with the experimental results for a (80 mm × 80 mm × 25 mm) square shape with a 45 and 60 deg wall angle, respectively, and a (80 mm × 80 mm × 20 mm) square shape with a 70 deg wall angle. The trends of the FE simulation results agreed quite well with the experimental results. Finally, the effects of the process parameters, i.e., the tool down-step and tool radius, on the ductile fracture value and FLC at fracture (FLCF) were also investigated using the FE simulation results. [ABSTRACT FROM AUTHOR]

Abstract The development of soft computing methods has had a significant influence on the subject of autonomous intelligent agriculture. This paper offers a system for autonomous greenhouse navigation that employs a fuzzy control algorithm and a deep learning-based disease classification model for tomato plants, identifying illnesses using photos of tomato leaves. The primary novelty in this study is the introduction of an upgraded Deep Convolutional Generative Adversarial Network (DCGAN) that creates augmented pictures of disease tomato leaves from original genuine samples, considerably enhancing the training dataset. To find the optimum training model, four deep learning networks (VGG19, Inception-v3, DenseNet-201, and ResNet-152) were carefully compared on a dataset of nine tomato leaf disease classes. These models have validation accuracy of 92.32%, 90.83%, 96.61%, and 97.07%, respectively, when using the original PlantVillage dataset. The system then uses an enhanced dataset with ResNet-152 network design to achieve a high accuracy of 99.69%, as compared to the original dataset with ResNet-152’s accuracy of 97.07%. This improvement indicates the use of the proposed DCGAN in improving the performance of the deep learning model for greenhouse plant monitoring and disease detection. Furthermore, the proposed approach may have a broader use in various agricultural scenarios, potentially altering the field of autonomous intelligent agriculture.

The exploration of Electrical Discharge Machining (EDM) with coated electrodes represents a relatively novel research avenue, thereby yielding limited published research outcomes. The choice of coating material plays a pivotal role in the EDM machining process, and alterations in coating materials can directly influence the adjustment of technological parameters in EDM. Consequently, research dedicated to optimizing these technological parameters for EDM employing coated electrodes is of paramount importance, and it promises to advance the practical implementation of this cutting-edge technique. In this study, we systematically investigate the technological parameters for EDM employing a copper-coated aluminum electrode in the context of Ti-6Al-4V machining. Our research outcomes are framed within a multi-objective optimization paradigm, with a focus on Material Removal Rate (MRR) and Surface Roughness (SR) as vital quality indicators. To address the intricate multi-objective optimization challenge, we have harnessed the combined power of the Taguchi methodology and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The amalgamation of these techniques enables us to navigate the complexities of EDM parameter optimization effectively. Our findings reveal that the most favorable process parameters entail a configuration of U=40V, I=40 A, Ton=1000μs, resulting in an MRR of 0.028mg/min and an SR of 7.56μm. These optimized parameters exemplify a substantial enhancement in machining efficiency and surface quality when utilizing coated electrodes. Moreover, our study scrutinizes the quality of the machined surface under optimal conditions with coated electrodes. We employ the TOPSIS method as a proficient solution for this endeavor, offering a straightforward approach to this intricate calculation process. [ABSTRACT FROM AUTHOR]

Miniaturized spectrometers can be implemented using Fabry–Pérot (FP) filter arrays. Such filters are defined by two parallel mirrors with a resonance cavity in between. For high optical quality, ion beam sputtered distributed Bragg reflectors (DBRs), with alternating high and low refractive index material pairs, can be used as the FP mirrors; while 3D nanoimprint technology provides an efficient way of implementing multiple organic FP cavities of different heights in a single step. However, the high residual stress in ion beam sputtered films results in poor adhesion between the DBR films and the organic polymer cavities, causing debonding of the DBR. Therefore, the residual stress of the ion beam sputtered films forming the DBRs must be reduced. Niobium pentoxide (Nb2O5) and silicon dioxide (SiO2) are used as the DBR materials in this work due to their high index contrast, resulting in high reflectivity for only a few alternating pairs. Stress relaxation in ion beam sputtered Nb2O5 and SiO2 films is achieved in this work by deposition under simultaneous high energy ion bombardment (oxygen and argon gas mixture) from a second ion source. Using this technique, the film density and hence compressive film stress for both Nb2O5 and SiO2 films is reduced without introducing any additional optical absorption in the films. FP filter arrays fabricated with stress reduced Nb2O5 and SiO2 as DBR films exhibit high optical and mechanical performance, with good adhesion between the films and the polymer cavity. [ABSTRACT FROM AUTHOR]

Enhancing the operational efficacy of electrical discharge machining (EDM) is crucial for achieving optimal results in various engineering materials. This study introduces an innovative solution—the use of coated electrodes—representing a significant advancement over current limitations. The choice of coating material is critical for micro-EDM performance, necessitating a thorough investigation of its impact. This research explores the application of different coating materials (AlCrN, TiN, and Carbon) on WC electrodes in micro-EDM processes specifically designed for Ti-6Al-4V. A comprehensive assessment was conducted, focusing on key quality indicators such as depth of cut (Z), tool wear rate (TWR), overcut (OVC), and post-machining surface quality. Through rigorous experimental methods, the study demonstrates substantial improvements in these quality parameters with coated electrodes. The results show significant enhancements, including increased Z, reduced TWR and OVC, and improved surface quality. This evidence underscores the effectiveness of coated electrodes in enhancing micro-EDM performance, marking a notable advancement in the precision and quality of Ti–6Al–4V machining processes. Among the evaluated coatings, AlCrN-coated electrodes exhibited the greatest increase in Z, the most significant reduction in TWR, and the best OVC performance compared to other coatings and the uncoated counterpart.

Micro-electrical discharge machining (micro-EDM) stands out as a transformative methodology, offering substantial progress in both technical and economic efficiency through the integration of coated electrodes. This study meticulously analyzes various technological parameters in micro-EDM, focusing specifically on Ti-6Al-4V, a widely employed titanium alloy. The application of a titanium nitride (TiN) coating material on a tungsten carbide (WC) electrode is investigated using the Taguchi method of experimental design. This study employs an ANOVA and factorial design methodology to scrutinize the influence of key parameters, namely voltage (V), capacitance (C), and spindle rotation (in revolutions per minute) (RPM) on the tool wear rate (TWR), overcut (OVC), and Z coordinate (depth) within the micro-EDM process. The findings unveil a noteworthy increase in the TWR with an elevated V, C, and RPM, with capacitance exerting a pronounced influence while voltage exhibits the least impact. OVC exhibits notable variations, revealing an inverse relationship with RPM. The Z coordinate (depth) is significantly affected by capacitance, with voltage and RPM each having a relatively negligible impact. A surface quality analysis exposes similarities and numerous defects in both coated and uncoated electrodes, emphasizing the need for further exploration into the effectiveness of coated electrodes in enhancing post-micro-EDM machined surface layers. This study contributes valuable insights to optimize and advance micro-EDM processes, laying groundwork for future innovations in precision machining.

This research presents an innovative methodology for accurately predicting spring-back tendencies in V-bending of SS400 steel sheets under elevated temperatures. The study leverages extensive tensile test data to determine parameters for pure isotropic and kinematic hardening laws at varying temperatures, crucial inputs for Finite Element Method (FEM) simulations. While using pure isotropic or kinematic hardening laws alone has limitations, especially at elevated temperatures, a hybrid approach is recommended for robust predictive models in ABAQUS 6.13 software. To address this challenge, a novel method is introduced, utilizing flow stress curve ratios between elevated and room temperatures as a function of equivalent strain to derive combined hardening law parameters. Rigorous comparison of simulation and experimental results confirms the model’s effectiveness in predicting spring-back in the V-bending of SS400 steel sheets, particularly under elevated temperatures. This innovative approach enhances understanding of material behavior at high temperatures and improves predictive capabilities for designing and optimizing complex V-bending processes.

Open-die forging holds a pivotal role in shaping machine parts within industrial applications. This study focuses on the assessment of stress–strain curves for C45 material at different elevated temperatures and strain rates through numerical simulations employing the finite element method (FEM). Specifically, the research investigates how the flow curve of materials at elevated temperatures and individual strain rates impacts the forming force during the flange forming of C45 billet tubes. By comparing the simulation results with experimental data on the flange-forming force, this study observes that optimal outcomes arise when considering both elevated temperature and strain rates in the flow curve of materials. The study then conducts simulations for C45 billet tubes with varying upsetting ratios (H0/D0), (S0/D0), and the punch’s pitch angle (α), aiming to address optimization challenges related to the flange-forming force. Consequently, a mathematical model is developed to represent the relationship between the flange-forming force and geometric parameters (H0/D0, S0/D0, and α). This model accurately predicts the forming force under various flange-forming conditions, demonstrating high precision with a maximum error of 4.26% compared with the experimental results. This study significantly contributes to the advancement of flange-forming technology in open-die forging through numerical simulation, enabling the optimization of the flange-forming force and the selection of appropriate equipment. These findings pave the way for more effective and efficient industrial processes, fostering innovation and progress in the field.

This study aimed to investigate the effects of Thermal-Assisted Machining (TAM) on SKD11 alloy steel using titanium-coated hard-alloy insert cutting tools. The microstructure, material hardness, chip color, cutting force, chip shrinkage coefficient, roughness, and vibration during TAM were evaluated under uniform cutting conditions. The machining process was monitored using advanced equipment. The results indicated that thermal-assisted processing up to 400 °C did not alter the microstructure and hardness of the SKD11 alloy steel. However, a significant variation in chip color was observed, indicating improved heat transfer through TAM. The cutting force, vibration amplitude of the workpiece, and surface roughness all decreased with increasing TAM. Conversely, the chip shrinkage coefficient of the machined chips tended to increase due to the high temperatures.

This study proposed an innovative method for improving the prediction of the cutting force (F) and chip shrinkage coefficient (K) for milling of SKD11 alloy steels using simulations and experimental results. Preliminary experimental measurements of the F and K were made for variable cutting speeds and depths, and simulations were then conducted using the Johnson–Cook model. However, significant discrepancies between the experiments and simulations were observed for the F and K. Therefore, an improved method was proposed, utilizing the relationship between simulation/experimental cutting forces and the equivalent fracture strain of simulation elements in the shear zone in the space of the stress triaxiality and equivalent strain. The progression of fracture strain paths according to the stress triaxiality until the desired cutting forces were achieved was utilized for adding new data to the fracture strain locus in the space of the stress triaxiality and equivalent strain. The new fracture strain locus was adopted again, to simulate and predict the F and K at full 2 × 3 levels of cutting speeds and cutting depths, and the results were compared with those of the corresponding experiments. Based on the highest deviations between the simulation and experimental data for the cutting force (5.29%) and chip shrinkage coefficient (5.08%), this study confirmed that the proposed method for determining the new fracture strain locus can improve the prediction of the F and K for milling of SKD11 alloy steels.

In this article, a numerical model for predicting spring-back in U-draw bending of DP350 high-strength steel sheet was presented. First, the hardening models were formulated based on combined isotropic–kinematic hardening laws, along with the traditional pure isotropic and kinematic hardening laws. A simplified method was proposed for determining the material parameters. Comparison of stress–strain curves of uniaxial tests at various pre-strains predicted by the numerical models and experiment showed that the combined isotropic–kinematic hardening model could accurately describe the Bauschinger effect and transient behavior subjected to cyclic loading conditions. Then, a finite element model was created to simulate the U-draw bending process using ABAQUS. Simulations were then conducted to predict the spring-back of DP350 high-strength steel in U-draw bending with geometry provided in the NUMISHEET’2011 benchmark problems. It was shown that the predictions of spring-back using the proposed model were in good agreement with the experimental results available in the literature. Finally, the effects of various tool and process parameters such as punch profile radius, die profile radius, blank holding force, and punch-to-die clearance on the spring-back were investigated. The simulation results suggested the significance of tool and process parameters on the final shape of the formed parts influenced by the spring-back.

In this study, to estimate the forming limit curves of a steel DP350 sheet, a combination of the finite element method simulation and experimental methods was adopted using the fracture height of experimental specimens and the corresponding in-plane major/minor strains of the finite element method simulation. Hecker’s punch stretching tests were first performed to measure the fracture height for forming limit curve testing specimens with a different notch radius. The finite element method process was then performed to get in-plane major/minor strains ( ε 1 and ε 2 ) at various points on specimens with different dimensions and the same fracture height of each corresponding experimental data point. An interpolated curve from the tip of the strain paths was derived using the limit curve (FLC1) of a DP350 steel sheet. The resultant FLC1 will be the input data for a finite element method simulation, in order to predict the formability of the steel DP350 sheet. Finally, experiments for difference specimens of Hecker’s punch stretching tests were performed as a comparison and showed a good agreement between the simulation and experimental results.

This article predicts the relationship between chip shrinkage coefficient and cutting parameters such as cutting speed and uncut chip thickness in a cutting process of A6061 aluminum alloy. To this end, shrinkage coefficient–based finite element method was first estimated using the Johnson–Cook (J-C) and Bao–Wierzbicki (B-W) material fracture models. After that, experimental measurements of the chip shrinkage coefficients were performed at the same cutting conditions to compare with simulation data and confirm the accuracy of material damage models. By a simultaneous evaluation of the effects of cutting parameters on the chip shrinkage coefficient, the B-W model was found to match well with the experiment. So that, the B-W model was then utilized to verify the effect of various cutting speeds and uncut chip thicknesses on the chip shrinkage coefficient and cutting force. Finally, using the least square method, the relationship between chip shrinkage coefficient and cutting force was obtained. The above-mentioned relationship is believed to be useful in determination of optimal cutting conditions in high-speed machining.

This paper investigates cutting force in thermal-assisted machining (TAM) by induction heating for SKD11 tool steel which is widely used in the mold industry. Experimental studies were first conducted at room and elevated temperatures to evaluate the effectiveness of the heating process on chip morphology and the cutting forces during the thermal-assisted machining and comparing with conventional machining method. The Taguchi method based on orthogonal array and analysis of variance ANOVA method was then used to design the number of experiments and evaluate the influence of cutting speed, feed rate, cutting depth, and elevated temperature on the cutting force. Study results showed a decrease in the cutting force in the TAM process. The optimal condition of parameters obtained for thermal-assisted machining were cutting speed 280 m/min, feed rate 230 mm/min, cutting depth 0.5 mm and temperature 400 °C. Finally, a proposed equation was established to determine the cutting force that was presented as a function of elevated temperatures when milling SKD11 material. A proposed cutting force model was compared, evaluated and confirmed to be in good agreement with experimental results.

We investigated changes in PA levels by the treatment of human bone-marrow-derived mesenchymal stem cells (hBM-MSCs) in ischemic stroke in rat brain model and in cultured neuronal SH-SY5Y cells exposed to oxygen-glucose deprivation (OGD). In ischemic rat model, transient middle cerebral artery occlusion (MCAo) was performed for 2 h, followed by intravenous transplantation of hBM-MSCs or phosphate-buffered saline (PBS) the day following MCAo. Metabolic profiling analysis of PAs was examined in brains from three groups: control rats, PBS-treated MCAo rats (MCAo), and hBM-MSCs-treated MCAo rats (MCAo + hBM-MSCs). In ischemic cell model, SH-SY5Y cells were exposed to OGD for 24 h, treated with hBM-MSCs (OGD + hBM-MSCs) prior to continued aerobic incubation, and then samples were collected after coculture for 72 h. In the in vivo MCAo ischemic model, levels of some PAs in brain samples of the MCAo and MCAo + hBM-MSCs groups were significantly different from those of the control group. In particular, putrescine, cadaverine, and spermidine in brain tissues of the MCAo + hBM-MSCs group were significantly reduced in comparison to those in the MCAo group. In the in vitro OGD system, N1-acetylspermidine, spermidine, N1-acetylspermine, and spermine in cells of the OGD + hBM-MSCs group were significantly reduced compared to those of OGD group.

The levels of organic acids representing metabolic pathway end products are important indicators of physiological status, and may be associated with metabolic changes in cancer. The aim of this study is to investigate the levels of organic acids in cancerous and normal tissues from gastric cancer patients and to confirm the role of metabolic alterations in gastric carcinogenesis. Organic acids in normal and cancerous tissues from forty-five patients with gastric adenocarcinoma were investigated by gas chromatography-mass spectrometry in selected ion monitoring mode as methoxime/tert-butyldimethylsilyl derivatives. We analysed the significant differences in the levels of organic acids in normal and cancer tissues and investigated the correlation of these levels in cancer tissues with clinicopathological features. The levels of Krebs cycle components, including α-ketoglutaric acid, succinic acid, fumaric acid, malic acid and oxaloacetic acid, were significantly increased in cancer tissues compared to normal tissues. In addition, the levels of glycolytic products, including pyruvic acid and lactic acid, as well as the levels of ketone bodies, including 3-hydroxybutyric acid, were also significantly increased in cancer tissues compared to normal tissues. The levels of ketone bodies in cancer tissues with differentiated histology and in intestinal-type cancer tissues were significantly increased. The organic acid profiling analysis described here may be a generally useful clinical tool for understanding the complexity of metabolic events in gastric adenocarcinoma, and organic acids may have potential as metabolic markers for the future discovery of diagnostic and therapeutic modalities.