Optimal Parameter Settings Research Articles

Mechanical and tribological behaviours of friction stir welding using various strengthening techniques

The present study investigates the research conducted on friction stir welding of aluminium alloys AA6061. The experiments were conducted under optimal parametric settings, and the specimens were subsequently subjected to post-processing techniques such as heat treatment and shot peening. The enhancement of weldment strength is contingent upon secondary processing, eliminating internal stress and improving surface qualities. Subsequently, the weldment will undergo mechanical testing and tribometer testing. The conventional testing protocol was implemented to assess the hardness, yield, and tensile strength. The wear and friction test examined the effects of various process factors. The load conditions were set at 10, 30, 50, and 70 N, while the sliding distance was set at 700 m and 1400 m. The experiments were carried out in dry sliding conditions at room temperature and the resulting data were recorded as average values. The welded AA6061 samples exhibited superior hardness and ultimate tensile strength, with the highest average values observed after heat treatment and subsequent shot peening. Attributed to the strengthening effect and achieving an ultra-fine grain size. The results of this inquiry indicate that the yield strength and ultimate tensile strength of the welded sample were significantly increased by heat treatment and shot peening. In particular, when compared to the as-welded AA6061 aluminium alloy sample, the sample's yield and ultimate tensile strength were 39.49 % and 38.7 % greater, respectively. The tribometer test revealed that a rise in load and sliding distance results in an increase in wear rate and a drop in the coefficient of friction. Under a load of 70 N and a sliding distance of 1400 m. s, the heat-treated and shot-peened AA6061 specimen showed a 27.3 % lower wear rate and a 29.6 % lower coefficient of friction compared to the as-welded AA6061 specimen.

Machinability of gas metal arc based 3D printed Al-Mg 5356 alloy using wire-EDM

Wire-Arc Additive Manufactured (WAAM) is relatively new method of metal 3D printing in which the raw material is heated by the gas metal arc and the molten metal pool is deposited layer-by-layer using a computer numerically controlled axis drive system. Since WAAM needs a finishing process for attaining the final dimensions of the components, there is a need to investigate the machinability aspects of WAAM fabricated materials. This work investigates the machinability of Al-Mg 5356 alloy test samples fabricated by WAAM process using wire-electric discharge machining (Wire-EDM). The test samples were subjected to Wire-EDM and the obtained material removal rate (MRR), kerf width (KW) and surface roughness (Ra) were investigated at different Wire-EDM process settings of voltage, current, pulse-on time (Ton), pulse-off time (Toff) and wire speed (Ws). Statistical analysis revealed that current had a significant influence on MRR. Ton had a strong influence on KW and Ra, whereas Toff exhibited a considerable impact on all these responses. Notably, Ws demonstrated a significant impact on Ra. However, voltage was found to have statistically negligible impact on all the machining responses. Microstructural investigations and compositional analysis were conducted providing valuable information on the cut surfaces. The results derived from the present investigation are useful for predicting the optimum process parameter settings for machining of WAAM-based 3D printed Al-Mg alloy in various manufacturing industries.

Optimization of an Industrial Recycling Line: The Effect of Processing Parameters on Mechanical Properties of Recycled Polyethylene (PE) Blends

This study concerns the optimization of an industrial recycling line; in other terms, this paper aims to find the optimal processing parameters that allow for a decrease in the loss of stress crack resistance (SCR) using a notched crack ligament stress (NCLS) test and an increase in the gain of the elongation at break, flexural modulus, and Izod impact strength of a polyethylene (PE) blend before and after recycling. The recycling line is composed mainly of a mono- and twin-screw extruder and a filtration system. Hence, the research question is as follows: How can we optimize the recycling process, without compromising the mechanical properties of recycled polyethylene (PE) blends? To answer the research question, Taguchi’s design of experiment and grey relational analysis (GRA) for multiobjective optimization was applied. Experiments were performed according to L16 standard orthogonal array based on five process parameters: mono-screw design, screw speed of the mono- and twin-screw extruder, melt pump pressure, and filter mesh size. Based on grey relational analysis (GRA), the optimal setting of process parameters was identified, and a barrier screw and a higher screw speed for both extruders were allowed to have optimal mechanical properties. Furthermore, the analysis of variance (ANOVA) indicated that the mono-screw design and screw speed of the mono- and twin-screw extruder significantly impact the mechanical properties of recycled polyethylene (PE) blends.

Optimizing the SPIF parameters for enhancing microhardness and surface quality in Inconel 625 superalloy components

Inconel 625, a high-strength superalloy with excellent corrosion resistance, discovers wide applications in critical sectors such as aerospace, nuclear, marine, and petrochemical. Single-Point Incremental Forming (SPIF) has appeared as an adequate fabricating process for shaping superalloy components. This work used a systematic approach, implementing Response Surface Methodology (RSM) to explore the influence of SPIF process variables, including tool nose diameter, step size, tool spindle speed, and wall angle, on microhardness (MH) and surface roughness (SR). The experimental results have been analyzed to determine the influencing process variables and their optimal settings, which simultaneously enhance MH and reduce SR of Inconel 625 superalloy truncated cones using analysis of variance and the desirability function analysis approach. The most significant parameters that influence MH and SR were TND and SS, respectively. The development of quadratic models for MH and SR has been completed successfully. The maximum MH of 486.8 HV and minimum SR of 0.432 µm have been obtained at the optimal parametric setting of TND = 15 mm, SS = 0.4 mm, TSS = 900 rpm, and WA = 57.5° with the desirability values for MH, SR and combined are 0.832, 0.933, 0.881 respectively. The F-value of the MH model = 109.18 and SR Model = 78.24 implies that both models are significant and both models have excellent prediction strength. The percentage error between predicted and actual values of MH and SR in confirmation run results is less than 5 %.

Waste-to-energy power plants: Multi-objective analysis and optimization of landfill heat and methane gas by recirculation of leachate

Elevated thermal energy trapped in landfill voids is a potential threat to humans since, fires might get ignited, eventually releasing poisonous gases, contaminated fluids (leachate), and abnormal quantity of heat. This study explores the practical potential of landfills to produce heat and methane gas through a leachate recirculation process, which involves returning nutrient-rich fluids to landfills to maximize electricity generation. This paper presents an experimental investigation of the physio-chemical properties of landfills by recirculating leachate to achieve sustainable performance characteristics of landfill models. Five input parameters i.e. waste particle size (WPS in mm), waste addition (WA in Ktoe), inorganic matter content (IO in %), leachate recirculation rate (LRR in liters/day), and age of landfill (LA in years) each at five levels is considered. The investigation is accomplished on the central composite rotating design (CCRD) matrix with a 5-level factor. The results reveal that WPS at D10, WA at 52.32 Ktoe, IO at 2%, LRR at 300 l/day, and LA at 4.07 year is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. An innovative method is studied for simultaneously lowering the internal temperature of landfills to ideal levels while also enhancing the rate of methane production. Energy transfer to the ORC system is 2.3 GJ in winters while 1.5 GJ in summers. Landfills equipped with leachate recirculation facilities provide 2.2 times more energy with stabilization occurring in 4th year with an energy content of 950 MJ/m3 of waste.

Multicriteria decision-making for optimization of welding parameters in cold metal transfer and pulse metal-inert gas weld bead of AA2099-T86 alloy using CRITIC and ROV methods

PurposeThe criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) combined methods were used to determine a single index for all multiple responses.Design/methodology/approachThis paper used cold metal transfer (CMT) and pulse metal-inert gas (MIG) welding processes to study the weld-on-bead geometry of AA2099-T86 alloy. This study used Taguchi's approach to find the optimal setting of the input welding parameters. The welding current, welding speed and contact-tip-to workpiece distance were the input welding parameters for finding the output responses, i.e. weld penetration, dilution and heat input. The L9 orthogonal array of Taguchi's approach was used to find out the optimal setting of the input parameters.FindingsThe optimal input welding parameters were determined with combined output responses. The predicted optimum welding input parameters were validated through confirmation tests. Analysis of variance showed that welding speed is the most influential factor in determining the weld bead geometry of the CMT and pulse MIG welding techniques.Originality/valueThe heat input and weld bead geometry are compared in both welding processes. The CMT welding samples show superior defect-free weld beads than pulse MIG welding due to lesser heat input and lesser dilution.

Parallel Computation of Piecewise Linear Morse-Smale Segmentations.

This article presents a well-scaling parallel algorithm for the computation of Morse-Smale (MS) segmentations, including the region separators and region boundaries. The segmentation of the domain into ascending and descending manifolds, solely defined on the vertices, improves the computational time using path compression and fully segments the border region. Region boundaries and region separators are generated using a multi-label marching tetrahedra algorithm. This enables a fast and simple solution to find optimal parameter settings in preliminary exploration steps by generating an MS complex preview. It also poses a rapid option to generate a fast visual representation of the region geometries for immediate utilization. Two experiments demonstrate the performance of our approach with speedups of over an order of magnitude in comparison to two publicly available implementations. The example section shows the similarity to the MS complex, the useability of the approach, and the benefits of this method with respect to the presented datasets. We provide our implementation with the paper.

A Taguchi-based hybrid multi-criteria decision-making approach for optimization of performance characteristics of diesel engine fuelled with blends of biodiesel-diesel and cerium oxide nano-additive

Given the pressing demand and ongoing necessity for fossil fuels, there is an imperative to actively seek alternative resources to replace petroleum-based fuels. The presents study considers a problem of experimentally investigating the effect of varying levels of important input parameters of a diesel engine fuelled with a novel blend of biodiesel-diesel and cerium oxide nano-additive on the sustainable performance characteristics of a diesel engine. Four input parameters, i.e., blend percentage (B in %), nanoparticle concentration (NPC in ppm), engine load (LD in %) and ignition pressure (IP in bar) each at four levels are considered. Experiments are conducted as per the Taguchi’s L16 standard orthogonal array and for each experiment, performance parameters (such as Brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC)), emission measures (Carbon monoxide (CO), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC) and Vibration level (VL)) of the diesel engine are collected. A hybrid multi-criteria decision-making (MCDM) approach, i.e., integrated MEREC-MARCOS method along with signal-to-noise (S/N) ratio and analysis of mean (ANOM) is employed to determine optimal setting of the input parameters that yield optimal multiple performance characteristics. The results reveal that B at 40%, NPC at 80 ppm, LD at 50% and IP at 200 bar is the optimal setting of the input parameters that produce optimum values of the output responses considered simultaneously. Further, results of the analysis of variance (ANOVA) show that Nanoparticle concentration percentage contribution is the maximum (79.63%) followed by engine load (8.40%), ignition pressure (6.28%), and blend percentage (2.11%). The optimization results are: BTE is 32.87%, BSEC is 0.285, CO is 0.018%, NOx is 559.6 ppm, UBHC is 28.1 ppm and VL= 19.57m2/sec which were validated with a confirmation test. Henceforth, such hybrid fuels provide sustainable energy solutions and environmental conservation simultaneously addressing the current and future demands.

Interaction of electric current with burnishing parameters in surface integrity assessment of additively manufactured Inconel 718

One of main problem associated with surface sever plastic deformation (SSPD) techniques like low plasticity burnishing (LPB) of metallic materials is limitation of surface integrity improvement at further loads (i.e. static force or pass number). It is due to interlocking of dislocation motion that limits further plastic deformation. As a plausible solution, in the present work, electric current assisted burnishing (ECAB) is introduced to use the concept of electroplasticity in surface integrity (roughness, hardness and residual stress) enhancement of Inconel 718 by additive manufacturing. Nevertheless, there are some researches which use this method to enhance performance of SSPD, the interaction effect of electrical parameter like current density with other parameters haven’t been studied well. Thus, to make the process optimized in terms of surface integrity aspects, parametric study of ECAB process with focus on interaction of current density with other LPB parameters merits further investigations. Thus, in the present work, an experimental study was carried out to identify the interaction effect of current density (as main parameter of ECAB) with other burnishing parameters (spindle speed, feed rate, current density, pass number and static force) on surface integrity aspects. Analysis of variances was further carried out to identify the contribution of each parameter on performance measures. Then, the interaction plots based on polynomial regression were developed, analyzed and discussed based on the physic of plastic deformation and some microscopic analysis like surface topography, scanning electron microscopy and X-ray diffraction analysis. It was found from the results that electric current density has the greatest effect on surface integrity aspect with contribution of 60 % on surface roughness, 68.5 % on hardness and 30 % on surface compressive residual stress. On the other hand it was found that this parameter has high interaction with process factors while applying different current density changes the optimum burnishing setting for each performance measures. Application of LPB at optimum parameter setting causes improvement of surface roughness of as-built material about 54 %; further improvement of 70 % and 85 % in roughness values were also achieved when the electric current with densities of 5A/mm2 and 10A/mm2, respectively were applied to burnishing process at optimum setting. It was also found that application of optimum LPB as well as optimum ECAB with densities of 5A/mm2 and 10A/mm2, results in improving the surface hardness about 17.5 %, 42 % and 68 %, respectively. In addition, under foresaid post-processing sequence, the surface compressive residual stress enhanced from 121 MPa (tensile type) for as-built material to −208 MPa, −345 MPa and −488 MPa (compressive type). Finally, the most optimum solution results in minimum surface roughness as well as maximum hardness and residual stress were identified by desirability approach function as 300RPM spindle speed, 0.08 mm/rev feed rate, 10A/mm2 current density, 3 pass number and 300 N force. To show the application of this optimization in a real life problem, fatigue resistance of optimum samples for high cycle and low cycle testing were obtained and compared with as-built material. It was found that following this post-treatment, the life cycle of samples improves from 160 % to 270 %, respectively that affirms practical application and manufacturing efficiency of proposed approach.

Multi-attribute decision making for deep learning-based typhoon disaster assessment

Emergency rescue decisions in case of a typhoon disaster can be considered multi-attribute decision-making problems. Considering the need for the timeliness and authenticity of decision-making information sources after such a disaster, this study proposed using learning methods to process real-time online data and interval-valued neutrosophic numbers (NNs) to express the classification results. Using Typhoon Hagupit as an example, a trained text classification model was used to classify real-time data (online comments), following which the classification results were used as weights to convert these data into interval-valued NNs. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) method was adopted to rank the extent of damage caused by the typhoon in each region; the sorting results were consistent with the official statistical data, proving the effectiveness of the proposed method. A detailed sensitivity analysis was conducted to determine the optimal parameter settings of the classification model. Furthermore, the proposed method was compared with existing methods in terms of data conversion and deep learning efficiency; the results confirmed the superior capabilities of the proposed method. Notably, the proposed method can provide support to disaster management professionals in their post-disaster emergency relief work.

Tribological properties optimization of nylon6 composites filled with SiC and Mica

Nylon is expanding tremendously on a global scale. This paper deals with the optimization of wear behaviour of nylon-6 hybrid polymer matrix composites. The composites were prepared with Nylon 6 as the matrix and SiC and Mica as the reinforcements. Twin screw extruder and injection moulding was used to fabricate the composites. Experiments were conducted on Pin on Disc apparatus. Reinforcement %, Sliding distance, Sliding speed and Load are the parameters used for the study. Experiments were designed using Taguchi and optimization was performed using Grey Relational Analysis.L9 orthogonal array was used. It was observed that 7.5 wt% of Mica, Load 5 N, 500 m sliding distance and 6 m/s Sliding speed were the optimum process parameter settings. Using SEM micrographs, the wear mechanisms of the worn discs were examined. Additionally, it was noted that some abrasive wear and very little adhesive wear had occurred on the Nylon 6 composites.

Performance analysis of high-spectral-resolution lidar with/without laser seeding technique for measuring aerosol optical properties

High-spectral-resolution lidar (HSRL) is a powerful tool for aerosol measurements. With/without laser seeding technique in the transmitted laser, the HSRL can be distinguished as the single-longitudinal-mode (SLM) HSRL or the multi-longitudinal-mode (MLM) HSRL, and the Mach-Zehnder interferometer (MZI) with periodic transmittance function can be used as the spectral discriminator in both the SLM HSRL and MLM HSRL. To in-depth knowledge of the respective advantages of the SLM HSRL and MLM HSRL for measuring aerosol optical properties, the working principle, optimal parameter setting, and detection performance of the SLM HSRL and MLM HSRL are analyzed and discussed in detail, respectively. The working principle of the SLM HSRL and MLM HSRL indicate that the effective transmittance of MZI is the important parameter of data retrieval, the main source of retrieval uncertainties, and the key factor of MZI optical path difference (OPD) settings. To ensure that the MZI can achieve the preferable separation for aerosol Mie scattering signals and molecular Rayleigh scattering signals, the optimal OPDs of MZI are set at 165 mm and 1000 mm in the SLM HSRL and MLM HSRL from the aspects of the effective transmittance of MZI and the spectral discrimination ratio (SDR). Besides, to analyze the influence of frequency difference and divergence angle for the detection performance of HSRL, the effective transmittance of MZI and SDR are simulated and the results show that the MLM HSRL has higher requirements for the environmental parameters and the echo beam collimation than the SLM HSRL. Moreover, the HSRLs with SLM and MLM transmitted lasers are constructed in Xi'an for measuring aerosol optical properties. The preliminary measurement results show that the range square corrected signal (RSCS) of Rayleigh channel is smaller than that of Mie channel in both the SLM HSRL and MLM HSRL, while the difference between RSCS of Rayleigh channel and RSCS of Mie channel in the SLM HSRL is larger than that in the MLM HSRL, and the detection range of the SLM HSRL is lower than that of the MLM HSRL.

Multiobjective optimization of process parameters of AZ91D/AgNPs/TiO2 composite fabricated by friction stir processing using response surface methodology and desirability

PurposeThis paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.Design/methodology/approachAn empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.FindingsThe relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.Practical implicationsAZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.Originality/valueIn terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.

A novel hybrid multi-criteria optimization of 3D printing process using grey relational analysis (GRA) coupled with principal component analysis (PCA)

Fused deposition modeling (FDM) is renowned as a prominent approach in the realm of 3D printing, where objects are built layer by layer using a heated nozzle to extrude melted materials. This research was conducted to identify the most effective FDM process variables to enhance tensile strength while simultaneously reducing surface roughness. Polylactic Acid (PLA) was chosen to fabricate test samples, showcasing the applications of 3D printing. In the course of this research, we conducted a series of 27 experiments to investigate the fundamental relationship between the parameters and the corresponding responses. The central aim of this study lies in optimizing the input variables viz. printing speed, layer thickness, and carbon deposition (C-deposition) for the technological manufacturing process of embossing parts in the context of Industry 4.0. To enhance both tensile strength and surface roughness simultaneously, a new hybrid method has been suggested. This approach integrates grey relational analysis (GRA) with principal component analysis (PCA) to determine the optimal combination of process parameters in the 3D printing process. Notably, the experiment trial exhibited the highest grey relational grade (GRG), indicating optimal process parameter settings at a printing speed of 100 mm s−1, layer thickness of 0.1 mm, and C-deposition of 15 mg respectively. Additionally, mathematical models are created through response surface methodology to explore the impact of FDM parameters on the grey relational grade. The findings from this study can be utilized in various industries and applications where FDM 3D printing is employed.

Suppression of Railway Catenary Galloping Based on Structural Parameters’ Optimization

Railway catenary galloping, induced by aerodynamic instability, poses a significant threat by disrupting the electric current connection through sliding contact with the contact wire. This disruption leads to prolonged rail service interruptions and damage to the catenary’s suspension components. This paper delves into the exploration of optimizing the catenary system’s structure to alleviate galloping responses, addressing crucial parameters such as span length, stagger dropper distribution, and tension levels. Employing a finite element model, the study conducts simulations to analyze the dynamic response of catenary galloping, manipulating structural parameters within specified ranges. To ensure accurate and comprehensive exploration, the Sobol sequence is utilized to generate low-discrepancy, quasi-random, and super-uniform distribution sequences for the high-dimensional parameter inputs. Subsequent to the simulation phase, a genetic algorithm based on neural networks is employed to identify optimal parameter settings for suppressing catenary galloping, taking into account various constraints. The results gleaned from this investigation affirm that adjusting structural parameters can effectively diminish the galloping amplitude of the railway catenary. The most impactful strategy involves augmenting tension and reducing span length. Moreover, even when tension and span length are fixed, adjusting other parameters demonstrates efficacy in reducing galloping amplitudes. The adjustment of messenger-wire tension, dropper distribution, and stagger can achieve a 22.69% reduction in the maximum vertical galloping amplitude. Notably, maintaining a moderate stagger value and a short steady arm–dropper distance is recommended to achieve the minimum galloping amplitude. This research contributes valuable insights into the optimization of railway catenary systems, offering practical solutions to mitigate galloping-related challenges and enhance overall system reliability.

Numerical Optimization on Aircraft Wake Vortex Decay Enhancement

Blowing air at the end of the airport runway can accelerate the decay of the near-ground aircraft wake vortex, thereby reducing the negative impact of the vortex on the following aircraft. However, the benefits of accelerating wake dissipation vary for different blowing parameters, so it is necessary to set appropriate parameters in order to obtain better acceleration results. Because of the high cost of traditional optimization methods, this research uses a Kriging surrogate model to optimize the blowing parameters. The results show that the current optimization algorithm can deal with the global optimization problem well. After obtaining 205 sample points, the response surface model of the blowing parameters and blowing yield was accurately established. A relatively optimal parameter setting range was given, and numerical simulation shows that the current parameter setting can obtain improved benefits from accelerated vortex dissipation. In addition, since the optimization process is partially dimensionless, the above optimization results can be used to achieve multi-objective design, that is, the same set of blowing devices can efficiently accelerate the dissipative process of the tail vortices of different aircraft types, thus improving the engineering feasibility of the current blowing method.

Kinetic, Thermodynamic, and Parametric Studies on Batch Synthesis of 1,1‐Diethoxybutane Catalyzed by Amberlyst‐15 Wet

Abstract1,1‐Diethoxybutane is a high‐potential biofuel additive that enhances various fuel properties, which include cetane number, lubricity, biodegradability, and flash point. This work presents a kinetic and thermodynamic study on the acetalization reaction between butanal and ethanol in the presence of Amberlyst‐15 wet in a batch reactor. The experiments were conducted in the temperature range 313–333 K at atmospheric pressure with three different initial molar ratios of reactants and catalyst loadings. An optimal parameter setting to achieve maximum equilibrium conversion of butanal was derived from a parametric study with these three factors. The equilibrium constant for the reaction was determined. Standard thermodynamic properties of the reaction, such as enthalpy, entropy, and Gibbs free energy, were experimentally evaluated. The activity‐based two‐parameter Langmuir‐Hinshelwood‐Hougen‐Watson rate expression was used for reaction kinetics. The activity coefficients were calculated by the UNIFAC method. The kinetic parameters and the activation energy were evaluated from the experimental data. The experimental data and data predicted by the model were found to be in good agreement.

Unmanned Aerial Vehicle–Light Detection and Ranging-Based Individual Tree Segmentation in Eucalyptus spp. Forests: Performance and Sensitivity

As an emerging powerful tool for forest resource surveys, the unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) sensors provide an efficient way to detect individual trees. Therefore, it is necessary to explore the most suitable individual tree segmentation algorithm and analyze the sensitivity of the parameter setting to determine the optimal parameters, especially for the Eucalyptus spp. forest, which is one of the most important hardwood plantations in the world. In the study, four methods were employed to segment individual Eucalyptus spp. plantations from normalized point cloud data and canopy height model generated from the original UAV-LiDAR data. And the parameter sensitivity of each segmentation method was analyzed to obtain the optimal parameter setting according to the extraction accuracy. The performance of the segmentation result was assessed by three indices including detection rate, precision, and overall correctness. The results indicated that the watershed algorithm performed better than other methods as the highest overall correctness (F = 0.761) was generated from this method. And the segmentation methods based on the canopy height model performed better than those based on normalized point cloud data. The detection rate and overall correctness of low-density plots were better than high-density plots, while the precision was reversed. Forest structures and individual wood characteristics are important factors influencing the parameter sensitivity. The performance of segmentation was improved by optimizing the key parameters of the different algorithms. With optimal parameters, different segmentation methods can be used for different types of Eucalyptus plots to achieve a satisfying performance. This study can be applied to accurate measurement and monitoring of Eucalyptus plantation.

Optimizing 3D-printed workpieces for radiotherapy application: modelling the CT number and print time

This study aims to analyze the influence of specific printing parameters, including infilling, print speed, and layer height, on the CT numbers and printing time of 3D-printed workpieces fabricated from Polylactic Acid (PLA). The primary objective is to optimize these parameters to attain desired CT numbers and print time for radiotherapy applications. To achieve this objective, we employed the Taguchi experimental design and regression analysis methodologies. A series of experiments were conducted to systematically assess the effects of varying infilling, print speed, and layer height values on the CT numbers and printing time of the PLA workpieces. The resulting data were then used to create mathematical models for predicting optimal parameter settings. Our investigations revealed that specific adjustments to infilling and layer height significantly influence the CT numbers and printing time of 3D-printed workpieces. By leveraging the developed mathematical models, precise predictions can be made to optimize independent parameters for the desired CT numbers and printing times, enhancing the efficacy of 3D-printed workpieces for radiotherapy applications. This research contributes to the advancement of 3D-printed workpieces utilized in radiotherapy, offering a pathway to enhance the accuracy and efficiency of treatment delivery. The optimization of printing parameters outlined in this study provides a valuable tool for clinicians and researchers in the field, ultimately benefiting patients undergoing radiotherapy treatments.

An augmentation aided concise CNN based architecture for COVID-19 diagnosis in real time

Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image pre-processing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception.