Significant Input Parameters Research Articles

Enhancing machine learning models with metaheuristic Optimization techniques for accurate prediction of PSC in FRP-reinforced concrete slabs

The present research examines the utilization of metaheuristic optimization algorithms to enhance the predictive accuracy of the Extreme Gradient Boosting (XGBoost) machine learning model in predicting the capacity for punching shear of Fiber Reinforced Polymer (FRP) reinforced concrete slabs in the absence of shear reinforcement. A database including 610 experimental results is constructed, and the efficiency of twenty metaheuristic algorithms is assessed in optimizing the hyperparameters of the XGBoost model. The enhanced models undergo evaluation and comparison with existing prediction algorithms, showcasing their better accuracy. The study reveals that the DE, MVO, EOA, and VCS algorithms yield the highest precision among the investigated optimization techniques out of which VCS model performed better. Furthermore, the significance of input parameters on the punching shear capacity is analyzed by implementing the Shapley Additive Explanation (SHAP) method. The findings emphasize the potential of metaheuristic optimization in enhancing the estimating abilities of machine learning models for complex engineering problems and provide valuable insights into the key factors influencing the punching shear strength of concrete slabs reinforced with FRP.

Прогнозирование помех электростатического разряда в электронном устройстве с использованием искусственной нейронной сети

Electrostatic discharge is a dangerous source of natural electromagnetic interference to the operation of modern electronic devices. Although measures have been taken to reduce and remove electrostatic discharge from the operating area of electronic devices, the absence of such discharge cannot be guaranteed. Therefore, when designing modern electronic devices, it is necessary to take into account the possibility of such electrostatic discharges in advance and take protective measures. The article proposes a method for predicting the amplitude of interference in the communication line of an electronic device when exposed to an electrostatic discharge on its metal case. The technique is based on the use of an artificial neural network. The technique includes the analysis of significant input parameters that affect the amount of interference in an electronic device; development of an experimental stand for measuring interference; choosing the structure and parameters of a neural network for predicting interference; choosing a training method for an artificial neural network; choosing a metric for assessing the quality of training; normalization of training data; training an artificial neural network using experimental noise data; predicting the amplitude of interference in the communication line of an electronic device when exposed to an electrostatic discharge on its body; where necessary, selecting and implementing electrostatic discharge protection measures. Examples of training an artificial neural network based on experimental data are given. The training was carried out for 572 epochs. For the training and testing sets, the discrepancy between the predicted data and the average measured noise values was 3.61% and 3.95%, respectively. Examples are given of predicting the amplitude of interference when exposed to electrostatic discharge. The results obtained indicate the possibility of practical use of an artificial neural network to solve problems of electromagnetic interference analysis.

Prediction equations for detecting COVID-19 infection using basic laboratory parameters.

Coronavirus disease 2019 (COVID-19) emerged as a global pandemic during 2019 to 2022. The gold standard method of detecting this disease is reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR has a number of shortcomings. Hence, the objective is to propose a cheap and effective method of detecting COVID-19 infection by using machine learning (ML) techniques, which encompasses five basic parameters as an alternative to the costly RT-PCR. Two machine learning-based predictive models, namely, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), are designed for predicting COVID-19 infection as a cheaper and simpler alternative to RT-PCR utilizing five basic parameters [i.e., age, total leucocyte count, red blood cell count, platelet count, C-reactive protein (CRP)]. Each of these parameters was studied, and correlation is drawn with COVID-19 diagnosis and progression. These laboratory parameters were evaluated in 171 patients who presented with symptoms suspicious of COVID-19 in a hospital at Kharagpur, India, from April to August 2022. Out of a total of 171 patients, 88 and 83 were found to be COVID-19-negative and COVID-19-positive, respectively. The accuracies of the predicted class are found to be 97.06% and 91.18% for ANN and MARS, respectively. CRP is found to be the most significant input parameter. Finally, two predictive mathematical equations for each ML model are provided, which can be quite useful to detect the COVID-19 infection easily. It is expected that the present study will be useful to the medical practitioners for predicting the COVID-19 infection in patients based on only five very basic parameters.

Diffusion of curcumin in PLGA-based carriers for drug delivery: a molecular dynamics study

Context:The rapid growth and diversification of drug delivery systems have been significantly supported by advancements in micro- and nano-technologies, alongside the adoption of biodegradable polymeric materials like poly(lactic-co-glycolic acid) (PLGA) as microcarriers. These developments aim to reduce toxicity and enhance target specificity in drug delivery. The use of in silico methods, particularly molecular dynamics (MD) simulations, has emerged as a pivotal tool for predicting the dynamics of species within these systems. This approach aids in investigating drug delivery mechanisms, thereby reducing the costs associated with design and prototyping. In this study, we focus on elucidating the diffusion mechanisms in curcumin-loaded PLGA particles, which are critical for optimizing drug release and efficacy in therapeutic applications.Methods:We utilized MD to explore the diffusion behavior of curcumin in PLGA drug delivery systems. The simulations, executed with GROMACS, modeled curcumin molecules in a representative volume element of PLGA chains and water, referencing molecular structures from the Protein Data Bank and employing the CHARMM force field. We generated PLGA chains of varying lengths using the Polymer Modeler tool and arranged them in a bulk-like environment with Packmol. The simulation protocol included steps for energy minimization, T and p equilibration, and calculation of the isotropic diffusion coefficient from the mean square displacement. The Taguchi method was applied to assess the effects of hydration level, PLGA chain length, and density on diffusion.Results:Our results provide insight into the influence of PLGA chain length, hydration level, and polymer density on the diffusion coefficient of curcumin, offering a mechanistic understanding for the design of efficient drug delivery systems. The sensitivity analysis obtained through the Taguchi method identified hydration level and PLGA density as the most significant input parameters affecting curcumin diffusion, while the effect of PLGA chain length was negligible within the simulated range. We provided a regression equation capable to accurately fit MD results. The regression equation suggests that increases in hydration level and PLGA density result in a decrease in the diffusion coefficient.

Optimisation of printing parameters of fused filament fabrication and uniaxial compression failure analysis for four-point star-shaped structures

PurposeThe purpose of this paper is to present an optimisation of four-point star-shaped structures produced through additive manufacturing (AM) polylactic acid (PLA). The study also aims to investigate the compression failure mechanism of the structure.Design/methodology/approachA Taguchi L9 orthogonal array design of the experiment is adopted in which the input parameters are resolution (0.06, 0.15 and 0.30 mm), print speed (60, 70 and 80 mm/s) and bed temperature (55°C, 60°C, 65°C). The response parameters considered were printing time, material usage, compression yield strength, compression modulus and dimensional stability. Empirical observations during compression tests were used to evaluate the load–response mechanism of the structures.FindingsThe printing resolution is the most significant input parameter. Material length is not influenced by the printing speed and bed temperature. The compression stress–strain curve exhibits elastic, plateau and densification regions. All the samples exhibit negative Poisson’s ratio values within the elastic and plateau regions. At the beginning of densification, the Poisson’s ratios change to positive values. The metamaterial printed at a resolution of 0.3 mm, 80 mm/s and 60°C exhibits the best mechanical properties (yield strength and modulus of 2.02 and 58.87 MPa, respectively). The failure of the structure occurs through bending and torsion of the unit cells.Practical implicationsThe optimisation study is significant for decision-making during the 3D printing and the empirical failure model shall complement the existing techniques for the mechanical analysis of the metamaterials.Originality/valueTo the best of the authors’ knowledge, for the first time, a new empirical model, based on the uniaxial load response and “static truss concept”, for failure mechanisms of the unit cell is presented.

Моделирование помех в электронном устройстве при воздействии импульсного магнитного поля с использованием искусственной нейронной сети

Modern electronic means are quite sensitive to electromagnetic interference. At the same time, they must function reliably in the existing electromagnetic environment. Lightning discharges and industrial sources make a significant contribution to the formation of the electromagnetic environment around electronic equipment. In this case, pulsed magnetic fields with microsecond parameters most often arise. The most rational approach to ensuring electromagnetic compatibility of electronic means is the most complete accounting and protection from possible phenomena at the design stage. Different methods for modeling the consequences of exposure to electromagnetic interference have their own advantages and disadvantages. To develop a technique and modeling interference in electronic means based on an artificial neural network using the example of the influence of a pulsed magnetic field a the purpose of this work. A practical technique for calculation the magnitude of interference in electronic means using an artificial neural network the paper proposes. All stages of the technique are described: analysis of the main input parameters affecting the amount of interference in the electronic means; the use of a special experimental stand for measuring interference depending on significant input parameters; choosing the structure and parameters of an artificial neural network to modeling interference; choosing a training method for an artificial neural network; choosing a criterion for assessing the quality of training when solving a regression problem; normalization of training data; training an artificial neural network using measured data; modeling the amount of interference in the communication line of an electronic means when exposed to a pulsed magnetic field; assessment of consequences and selection of methods of protection against interference. As an example, we consider the problem of modeling the amount of interference in a communication line inside an electronic means when exposed to a pulsed magnetic field. The magnetic field has parameters recommended by the requirements of the regulatory document on electromagnetic compatibility of devices. In the problem under consideration, an acceptable discrepancy in results is achieved with an acceptable number of neural network training epochs.

Investigation of flow behaviour in the nozzle of a Pelton wheel: Effects and analysis of influencing parameters

The performance of a Pelton wheel is influenced by the jet created by the nozzle. Therefore, a Computational Fluid Dynamics (CFD) simulation was proposed. In this study, the significant output parameters (outlet velocity, outlet pressure, and tangential force component) and input parameters (different pressure and spear locations) were examined. In addition, the influencing parameters and their contributing percentages to the performance of the Pelton wheel were calculated using different optimisation techniques such as Taguchi Design of Experiments (DoE), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA) and Criteria Importance Through Intercriteria Correlation (CRITIC). The effect of input factors on the output response was examined with DoE, and the results show that the inlet pressure had the most significant impact (97.38%, 99.18%, and 97.38%, respectively, for all different spear sites with a 99% confidence level). In terms of preference values, the TOPSIS and GRA results are comparable (best ranks for simulation runs #24 and #25 and least ranks for simulations #2 and #3, respectively). The CRITIC results for the pressure parameter are in good agreement with the Taguchi ANOVA analysis. The last spear location (5 mm after the nozzle outlet), with an inlet pressure of 413685 Pa generated the best result when employing the TOPSIS and GRA techniques. The outlet pressure of the nozzle was found to have a significant impact on the flow pattern of the Pelton Wheel based on the analysis of the CRITIC, Taguchi, and CFD results.

Process optimization of chemical looping combustion of solid waste/biomass using machine learning algorithm

Chemical Looping Combustion (CLC) is a carbon capture technology that uses an oxygen carrier to transfer the oxidizing agent to the fuel for combustion. This study used different machine learning algorithms, Artificial neural network and Response surface methodology to estimate the surface region process performance and optimize the process condition for the CLC of different solid fuels waste paper, plastic waste, and sugarcane bagasse blends. Based on the combustion efficiency, CO2 yield and CO2 capture efficiency responses, A high performance correlation (R2 > 0.8) was obtained for all the combustion parameters analyzed. The perturbation plot derived from the RSM analysis indicated that the most significant input parameters include the steam to fixed carbon, blend ratio and the fuel reaction temperature.The CLC process was optimized using RSM. For blends of SCB/WP, the best operating conditions were found to be 800 °C, a solid flow rate of 197.7 kg/h, an oxygen carrier to fuel ratio of 1.1, a steam to fixed carbon ratio of 2.16, and a blend ratio of 1. Similarly, for blends of SCB/PW, the optimal operating conditions were 800 °C, a solid flow rate of 199.4 kg/h, an oxygen carrier to fuel ratio of 1.3, steam to fixed carbon ratio of 2, and a blend ratio of 0.3. The optimum combustion performance was found to be 0.98, 0.78, and 0.96 for SCB/WP and 0.99, 0.62, and 0.96 for SCB/PW, respectively.

Design optimization of three‐dimensional geometry of a micro horizontal axis wind turbine blade using the response surface method

AbstractThis study presents an aerodynamic design optimization of a micro micro‐horizontal‐axis wind turbine (HAWT). To obtain an optimal design, it is essential to understand the design parameters and select the responsible factors that affect the blade efficiency. The aim of this work is to redesign a 3D micro‐HAWT to improve aerodynamic performance, through improving the distribution of chord length and twist angle along the blade. Performance analysis and flow visualization of the initial design and the optimal design were carried out using CFD analyzer. In the blade optimization design, eight significant input parameters were selected, five to characterize the chord length distribution and three to represent the twist angle along the blade. To maximize the efficiency, design points that are created by Design of Experiment (DoE) are evaluated through (Multi‐Objective‐Genetic Algorithm) MOGA method. The results showed a reduction on the separation effect area on the optimal blade surface compared to the initial one. The use of response surface optimization (RSO), when combined with CFD simulation, has proven beneficial in selecting the optimal HAWT design. Finally, the open source software (Qblade) was used to investigate and to compare the performance of the initial and optimum design. An efficiency enhancement of approximately 3.6% is achieved at tip speed ration TSR = 3.4.

Static analysis of a sandwich cylindrical panel composed of porous core sandwiched by graphene-reinforced copper matrix

A higher-order shear deformable model is extended in this paper for static analysis of a sandwich cylindrical panel composed of porous core sandwiched by graphene-reinforced copper matrix. Principle of virtual work is used to derive governing equations of static bending through the computation of strain energy and external work. A higher-order shear deformable model is used to develop the kinematic relations. The sandwich cylindrical panel is subjected to mechanical and thermal loads. The governing equations are solved using Eigenvalue–Eigenvector method for clamped–clamped boundary conditions. The eigenvalues of the characteristic equation are obtained, and the solution is obtained for real and complex roots. The boundary conditions are applied to obtain integration constants. The axial and radial variation of the displacement, strain, and stress components are presented in terms of significant input parameters such as volume fraction, and folding angle of the graphene origami, various distribution patterns, porosity coefficient, thermal and mechanical loads, and geometric parameters. The results indicate that the stress, strain, and deformation components are increased with an increase in porosity coefficient and folding angle of the graphene. Furthermore, an enhancement in the volume fraction of the graphene leads to a significant decrease in various stress, strain, and deformation components. The results of this work can be used in aerospace science and technology specially in airplanes.

Determining the significant input parameters of a forecasting model for material resources of residential construction projects at the investment feasibility assessment stage

Any investor/developer spends considerable time to make a construction decision in analyzing various parameters, such as the building site area, height restrictions, total area, quantity of necessary materials, etc. First of all, this is due to a high level of uncertainty and the complexity of estimating costs for the initial stages of project implementation. The unavailability of detailed design data at the investment justification stage makes it impossible to conduct a sufficiently accurate assessment of the value and period of construction to make highly accurate managerial decisions. However, one of the key issues, namely determining the significant parameters that have the greatest impact on future project-related decisions, has not been studied sufficiently. This article identifies the most significant parameters that impact the future characteristics of residential buildings based on an expert assessment using the a priori ranking method. The reliability of the assessment is confirmed using the coefficient of concordance. A conclusion is provided about their applicability as the input values of a forecasting model that uses machine learning and artificial intelligence to determine various technical and economic characteristics of residential buildings.

Metallurgical Behaviour and Characterization on Polymer Metal with HCHCr Tool by using Friction Stir Welding Adopting L27 Taguchi Technique

Friction stir welding is a technique of combining harmless for the environment because it doesn’t use flux or any other shield gas and doesn’t produce hazardous gas. To study the FSW process on composite material this work offers an experimental enquiry and optimisation technique. In this investigated how input process operating factors like tool rotational speed, feed rate and depth of cut affect an output parameter like hardness of welded connections and Ultimate Tensile Strength (UTS), Microstructure and metallurgical on the metal. To achieve this L27 Taguchi approach orthogonal array used to optimise design tests on friction stir welding parameters and also HCHCr tool used to improve the good weld ability and to increase the heat input. The experimental setup is run with several combinations of process parameters such as 900, 1000, and 1200 rpm of Tool rotational speed 20 mm, 25 and 30 mm as feed rate by adjusting the depth of cut values of 5.4 mm, 5.6 mm and 5.8 mm respectively. Additionally, utilising the regression analysis equations (ANOVA) used to identify the significant input parameters how it effects for the output metal structure. Finally, by achieved effective evaluation of the metallurgical and microstructure on the nylon 6A metal by various analysis and Testing.

Machine Learning Aided Numerical and Experimental Investigation of Hydrodynamic Performance in the Circulating Fluidized Bed Boiler

Abstract This study attempts to illustrate the benefits of integrating the concepts of machine learning algorithms with the field of thermo-fluidic applications. The current work is aimed at identifying effective or significant hydrodynamic input parameters, which are capable of deriving full benefit of fluidization that could yield a better circulating fluidized bed (CFB) furnace design using the Apriori algorithm. For this, historical datasets from the literature are collected and pretreated based on the design under observation. Association analysis performed by this Apriori algorithm measures the comparative strength of parameters under consideration. Also, this algorithm is capable of identifying the right combinations of parameters that can produce maximum fluidization performance. The end results suggested by this Apriori algorithm are validated using the computational fluid dynamics package. For this, the transient behavior of a scaled-down (1:20) reactor model of a real-time industrial CFB boiler is simulated using ansys fluent 18.0. In specific, the effects of fluidizing velocities, inventory heights of the bed, and particle sizes recommended by the Apriori algorithm are investigated. Here, the effects are assessed in terms of volume fraction distribution and axial velocity profile distribution profiles. From the results of simulations, it was clearly found that 2 m/s inlet velocity produced good circulating fluidized bed patterns on a bed inventory height of 0.5 m for a mean particle size of 200 µm. The results obtained from the simulations are once again validated visually against snapshots obtained during real-time laboratory fluidization experimental runs. Among all the cases of comparisons, the best agreement is demonstrated by Apriori algorithm compared to the numerical and the experimentally obtained results. Also, it is found that the manual time taken to identify the right combinations of parameters is drastically reduced by this method compared to conventional optimization algorithm and trial error methods.

Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization

Recent advances in machine learning (ML) have transformed the landscape of energy exploration, including hydrocarbon, CO2 storage, and hydrogen. However, building competent ML models for reservoir characterization necessitates specific in-depth knowledge in order to fine-tune the models and achieve the best predictions, limiting the accessibility of machine learning in geosciences. To mitigate this issue, we implemented the recently emerged automated machine learning (AutoML) approach to perform an algorithm search for conducting an unconventional reservoir characterization with a more optimized and accessible workflow than traditional ML approaches. In this study, over 1000 wells from Alberta’s Athabasca Oil Sands were analyzed to predict various key reservoir properties such as lithofacies, porosity, volume of shale, and bitumen mass percentage. Our proposed workflow consists of two stages of AutoML predictions, including (1) the first stage focuses on predicting the volume of shale and porosity by using conventional well log data, and (2) the second stage combines the predicted outputs with well log data to predict the lithofacies and bitumen percentage. The findings show that out of the ten different models tested for predicting the porosity (78% in accuracy), the volume of shale (80.5%), bitumen percentage (67.3%), and lithofacies classification (98%), distributed random forest, and gradient boosting machine emerged as the best models. When compared to the manually fine-tuned conventional machine learning algorithms, the AutoML-based algorithms provide a notable improvement on reservoir property predictions, with higher weighted average f1-scores of up to 15–20% in the classification problem and 5–10% in the adjusted-R2 score for the regression problems in the blind test dataset, and it is achieved only after ~ 400 s of training and testing processes. In addition, from the feature ranking extraction technique, there is a good agreement with domain experts regarding the most significant input parameters in each prediction. Therefore, it is evidence that the AutoML workflow has proven powerful in performing advanced petrophysical analysis and reservoir characterization with minimal time and human intervention, allowing more accessibility to domain experts while maintaining the model’s explainability. Integration of AutoML and subject matter experts could advance artificial intelligence technology implementation in optimizing data-driven energy geosciences.

PARAMETRIC STUDY OF A CNC TURNING PROCESS USING DISCRIMINANT ANALYSIS

In the present day manufacturing scenario, computer numerical control (CNC) technology has evolved out as a cost effective process to perform repetitive, difficult and unsafe machining tasks while fulfilling the dynamic requirements of high dimensional accuracy and low surface finish. Adoption of CNC technology would help an organization in achieving enhanced productivity, better product quality and higher flexibility. In this paper, an endeavor is put forward to apply discriminant analysis as a multivariate statistical tool to investigate the effects of speed, feed, depth of cut, nose radius and type of the machining environment of a CNC turning center on surface roughness, tool life, cutting force and power consumption. Simultaneous discrimination analysis develops the corresponding discriminant function for each of the responses taking into account all the input parameters together. On the contrary, step-wise discriminant analysis develops the same functions while considering only those significant input parameters influencing the responses. Higher values of hit ratio and cross-validation percentage prove the application of both the discriminant functions as effective prediction tools for achieving enhanced performance of the considered CNC turning operation.

Experimental investigation on the machining performance of AISI D2 tool steel during ultrasonic-assisted turning under dry and MQL conditions

Ultrasonic-assisted turning (UAT) is a hybrid machining method that improves difficult-to-cut materials’ machinability and surface integrity. Minimum quantity lubrication (MQL) is a cooling condition employed in metal cutting operations to improve machining performance and environmental sustainability. This article presents the experimental investigations on the effect of machining and vibration parameters and cooling conditions on the surface roughness (Ra), flank wear (Vb), and surface hardness (HV) during UAT of AISI D2 tool steel. Experiments were designed based on L36 orthogonal array. Two levels of spindle speeds, feed rates, depth of cut, and three levels of percentage intensity of ultrasonic power (PIUP) were employed, and two cooling conditions, like dry and MQL. Analysis of variance (ANOVA) was used to find the significant input parameters that affect each response. ANOVA output revealed that feed rate was the most influential factor on Ra, the PUIP is the dominant factor on HV, and spindle speed is the significant factor on Vb. Optimisation is performed to find optimal parameters as per Taguchi multi-response method. The optimal parameters are 450 rpm spindle speed, 0.05 mm/rev feed rate, 0.1 mm depth of cut, 100 PIUP and MQL cooling resulted in minimum surface roughness of 0.439 μm, maximum hardness of 283 HV and minimum flank wear of 168 μm. Flank wear morphology results showed that abrasion and chipping are the tool wear mechanism in dry UAT, whereas minor abrasion alone was found in MQL UAT compared to CT. Chip morphology study showed that UAT produced continuous spiral-type chips under both cooling conditions compared to CT. A novel design approach is implemented to design the UAT tool. Along with the UAT tool, MQL is also given to improve the machinability of D2 tool steel. This UAT tool can machine intricate metals with a critical cutting velocity of less than 75.4 m/min.

Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer

The number of existing global solar radiation (GSR) observation stations is limited, and it is challenging to meet the demand for scientific research and production. Different forecasting horizons of solar radiation correspond to various applications. Therefore, it is critical to design realistic models to predict the GSR of varying sequence lengths. This study proposes a prediction model based on the analysis of the Pearson correlation between GSR and each input parameter and the establishment of different input combinations related to the result of Pearson analysis in four high-quality datasets. The proposed Informer model compares the results with five classical machine learning models on four datasets with R2, RMSE, and skill score (S) as evaluation metrics. This study examines the proposed model's prediction performance for five prediction lengths, four climate zones, three sampling frequencies, and two input types. The results showed that the Informer model performs well with the clearness index and pressure as the input. Besides, the RRMSE values are less than 10% under optimal input in long sequence forecasting. The findings suggested that the proposed advanced Informer model is a reliable alternative for GSR prediction due to its high predictive accuracy under diverse prediction lengths, sampling frequencies, climate zones, and the number of input parameters.

Pore pressure prediction assisted by machine learning models combined with interpretations: A case study of an HTHP gas field, Yinggehai Basin

Accurately estimating pore pressure in high-temperature and high-pressure (HTHP) formations is crucial to prevent drilling problems and engineering disasters. In this study, four machine learning models were developed, namely decision tree, random forest, gradient boosting decision tree, and extreme boosting decision tree models, along with the classical Eaton's method, to rapidly predict pore pressure in an HTHP gas field in the Yinggehai Basin using seven variables, including ‘depth’ and six common well-logging series. The results demonstrate that the machine learning models outperform the classical Eaton's method in pore pressure prediction. Among the models, the decision tree model exhibits the best performance, with the highest correlation coefficient (R2 = 0.98) and the lowest root mean squared error (0.61). Additionally, the Graphviz tool and Shapley additive explanations method were used for the visualization and interpretation of the decision tree model to gain a deeper understanding of the model's working mechanism and the reasons behind the obtained results. The interpretation revealed that the variable of ‘depth’ was the most significant input parameter in the decision-making process of the model. This study provides insights into the ‘black-box’ interpretation of machine learning models and demonstrates that tree-based machine learning models can accurately predict pore pressure in undersurface HTHP formations.

An integrated operations model for supply of relief items under path disruptions

The integration of relief supply chain operations is critical for the timely supply of relief items to the areas affected by disasters. This work considers a two stage relief supply chain providing relief kit to the areas affected by flood type disaster. Floods often cause path blockages that affect the supply of relief items. A mixed integer linear programming model is formulated for the integrated production and outbound distribution scheduling problem considered and is solved by using a mathematical solver. Since solvers have computational issues in solving realistically sized problems, a Selective Hybrid Genetic Algorithm (SHGA), which performs a selective local search, is proposed. The proposed SHGA produces near optimal solutions in relatively short computation times which is beneficial for quick decision making. A sensitivity analysis is also performed and the path disruption is found to be the most significant input parameter influencing the model.

Multi-objective optimization in electrochemical micro-drilling of Ti6Al4V using nature-inspired techniques

ABSTRACT Current study investigates the efficacy of electrochemical micro-drilling (ECMD) process during micro-hole fabrication on a Ti6Al4V alloy sheet of 1.2 mm thickness using a cylindrical-shaped micro-tool. Ethylene glycol-based electrolyte was selected to overcome excessive passivation. Experiments were conducted by altering voltage, feed rate (FR), and electrolyte concentration (EC) using a central-composite-design to observe their effects on the output parameters. The desired response parameters, such as radial overcut (ROC), and taper were examined while fabricating dimensionally accurate micro-holes including the volume removal rate (VRR) that occurred during the process. ANOVA results established voltage as the major significant input parameter that affects all the responses, followed by FR. In addition, different nature-inspired optimization techniques, namely Multi-Objective Grey-Wolf Optimizer (MOGWO), Multi-Objective Bonobo Optimizer (MOBO), and Multi-Objective Particle Swarm Optimization (MOPSO), were employed. MOBO reported the best Pareto-surface in terms of uniformity and spread measures and suggested a FR of 0.3 μm/s for optimal micro-drilling operations.