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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.

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 study presents a method for finite element (FE) simulation of a deep drawing process of a cold-rolled carbon steel (SPCC) sheet material based on the graphical method. First, uniaxial tensile specimens were prepared and experimental tests were conducted to determine the flow stress curves. The calculation of the fracture points at special strain modes (plane strain, uniaxial tensile strain, and biaxial tensile strain) was presented using the modified maximum force criterion (MMFC). After that, the graphical method was adopted for the estimation of the forming limit curve (FLC) based on several hardening laws. FE models for a deep drawing process of the SPCC sheet were then built using the calculated FLCs. Using FE simulations, the fracture heights of cylinder cups formed by the deep drawing process were finally determined and compared with those from experiments. The results showed a good agreement between simulated and measured fracture height with a maximum of 3.6 % deviation. Additionally, simulations and corresponding experiments were performed to investigate the effects of the blank holder force, punch corner radius, and drawing ratio on the fracture height of cylinder cups.

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 research investigates the impact of high-speed and thermal-assisted machining (HS-TAM) on tool wear and surface roughness during the milling of SKD11 steel. The goal is to identify high-speed and elevated temperature zones that can improve machining efficiency, enhance surface quality, minimize costs, and extend tool life. The study involves the high-speed milling of SKD11 steel at various temperature conditions to evaluate the effect of temperature on tool wear and surface roughness. Additionally, experiments are conducted at the highest allowable support temperature with increased high-speed cutting to examine the effect of high speed on tool wear and surface roughness. The study demonstrates the correlation between cutting-tool wear and surface roughness at various high-speed cutting conditions and TAM environments and provides recommendations for cutting speeds and heating temperatures for different quality and productivity objectives. The findings indicate that high-speed milling of SKD11 at 600 m/min and 500 °C can decrease cutting tool-wear height (wear volume) and surface roughness by 82.47% (95.74%) and 91.08%, respectively, compared to machining at room temperature. Furthermore, the higher-speed modes at 500 °C result in a slight increase in wear height and surface roughness for high-speed cutting below 800 m/min, but reduces surface roughness for high-speed cutting beyond 800 m/min, reaching a value of 0.158 µm at the high-speed cutting limit of 1000 m/min.

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.

In order to predict correctly stress-strain curve for tensile tests at elevated and cooling temperatures, a modification of a Johnson–Cook (J-C) model and a new method to determine (J-C) material parameters are proposed. A MATLAB tool is used to determine material parameters by fitting a curve to follow Ludwick and Voce's hardening law at various elevated temperatures. Those hardening law parameters are then utilized to determine modified (J-C) model material parameters. The modified (J-C) model shows the better prediction compared to the conventional one. An FEM tensile test simulation based on the isotropic hardening model for metal sheet at elevated temperatures was carried out via a user-material subroutine, using an explicit finite element code. The simulation results at elevated temperatures were firstly presented and then compared with the measurements. The temperature decrease of all elements due to the air cooling process was then calculated when considering the modified (J-C) model and coded to VUMAT subroutine for tensile test simulation. The modified (J-C) model showed the good comparability between the simulation results and the corresponding experiments.

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.

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]