Search

The objective of this study is to improve the mechanical properties of dissimilar joint between 316 stainless steel and GTD-111 superalloy obtained by laser welding. Hence, the input variables of pulse frequency, welding speed and laser power were employed to optimize the output variables of weld penetration, hardness and tensile strength through the desirability function technique and response surface methodology. In order to study the changes of microstructure in the welded samples, the optical and scanning electron microscopies were applied. It was observed that an increase in the laser power from 1500 to 2500 W enhanced the penetration depth of weld, while it worsened the hardness and tensile strength owing to nucleation of solidification cracks and formation of Laves phase caused by segregation of Ta, Ti, Nb, W, and Mo elements. Moreover, the faster welding speed up to 1.5 mm/s resulted in a decrease in the penetration depth and an increase in the hardness and tensile strength, which is due to the nucleation of a small dendritic structure and the nucleation of fewer Laves phase owing to lesser heat input. When the variables of pulse frequency and laser power increased simultaneously, the liquation cracks were formed within HAZ of GTD-111 superalloy due to nucleation of γ′-γ eutectic phase, while the cracks and shrinkage voids were observed within HAZ of 316 stainless steel. The results of desirability function technique indicated that the optimum mechanical properties have been obtained at a pulse frequency of 15 Hz, welding speed of 0.8 mm/s and a laser power of 1550 W.

In recent years, the use of artificial neural networks (ANN) for modeling and optimizing metal forming processes has gained popularity. Numerous benefits are provided by ANNs, which can only be attained by constructing a high-performance ANN model. However, selecting an ANN’s appropriate training and architectural parameters remains challenging. Typically, these parameters are chosen using a trial-and-error approach in which many ANN models are produced and evaluated. This article describes the use of the Taguchi method for optimizing an ANN model trained on architectural parameters by the Levenberg–Marquardt algorithm and the additive ratio assessment method for selecting the optimized parameter. The method’s operation is demonstrated using the Altair Inspire form, the Forming Limit Diagram, and the Maximum Thinning Rate. The ANN model was trained using the orthogonal array, and its prediction performance was compared to the experimental validation. Analysis of variance assisted in determining whether the model was significant. The analysis of means helped us in deciding the appropriate ANN level for each parameter. Taguchi orthogonal array, ANN optimized for additive ratio assessment method. This model was developed and was found to be the most accurate. Through learning and testing, it was demonstrated that a systematic method for selecting the optimal ANN training and architecture parameters saves time and money by avoiding trial and error. The Taguchi-based additive ratio assessment method algorithm can then be used to determine the ideal processing parameters.

Electromechanical impedance (EMI) is one of the modern techniques employed in the field of structural health monitoring (SHM) and has witnessed a great expansion in many domains due to its ability to ensure continuous monitoring and high reliability with a lower cost. Hence, the purpose of this study is to investigate the effectiveness of EMI in monitoring and detecting pre-existing cracks in the silicon layer of the photovoltaic (PV) solar cells. Indeed, a detailed finite element analysis (FEA) on the effect of piezoelectric lead zirconate titanate (PZT) patch shape on the EMI of crystalline-silicon (c-Si) layer of the PV solar cells were performed. The typical structure (c-Si + PZT) of different scenarios were stimulated by giving a lower voltage and high frequency as input data to extract the EMI signature as an explainable output result. Different models were investigated based on PZT patch types and crack position in the silicon layer. The results indicate that the EMI technique is effective in detecting crack, even in its incipient stage. The analysis suggests using a higher frequency range (350–650 kHz) for characterizing damage with an invisible depth due to the higher sensitivity of the EMI technique in this specific frequency.

Predicting the results of multi-stage deep drawing in determining the thickness distribution and thinning of the workpiece are measures that will help reduce production costs by saving materials and production time. Machine Learning (ML) technique is a promising method for predicting the thickness distribution (TD) of drawn copper metal. Macro and micro products are currently used in sensitive electronic and structural applications. In this study, ML defines the relationship between the scaled thickness and the respective number of stages governed by the scaling law. It includes the development of Artificial Neural Network (ANN) tools based on machine learning to model the relationship between thickness by scaling law and stages based on multi-stage deep-drawn cups. TD is a measure of consistency. The TD is measured from the initial blank of 1500 mm long, 750 mm wide, and 6 mm thick Cu strip, reduced by 50% in nine successive steps until a final thickness of 0.1875 mm. Two ANN models used for TD prediction are Bayesian regularization (BR) and Levenberg-Marquardt (LM) algorithms. A trained machine learning model can successfully predict and verify the unseen data of 1.5 and 0.38 mm TD. ANN is used to predict the Finite Element Analysis (FEA) results and confirm them through the experimental results. The developed model can predict the TD of the multi-stage cup with the die design parameters. The difference between the TD predicted value and the measured value is based on the simulation results of multi-stage cups using the finite element method. When the predicted and measured TD, the difference in cup drawing depth is 0.5%–2.0%. The results show that the LM model is suitable for predicting the TD of formed copper cups following the scaling law.

Spherical parallel manipulators (SPMs) have promising industrial applications due to their strong mechanical performance. However, they face challenges such as singularity avoidance, tight tolerance manufacturing requirements, and the need for versatile and dexterous end-effector functionalities. Additionally, SPMs lack customizability due to singularity and collision issues. This work introduces a novel design principle that addresses these challenges by benchmarking two analytical methods: structural synthesis using screw theory and dimensional synthesis using dimensionless parameters. A proof of concept of a 2-DoF SPM that can operate within a cylindrical constraint and realize a full workspace without collision is presented. This design allows for adjustable reachable workspace at the end-effector, despite significant geometric constraints. These findings on mechanical design solutions provide customizability for SPMs, and a 3D model with an aspect ratio of 1/12 is presented and discussed for potential practical applications. Overall, it is identified that both a simulation using Recurdyn and inverse kinematic model show the desired movements at the moving platform with the maximum error of 0.0013°.

The geometric intricacy of tiny gears makes nano-finishing difficult. In the current study, the magnetorheological (MR) polishing process is used for the nano-finishing of intricate surfaces of tiny gear components uniformly. For polishing, the technique employs a dynamic fluid recognized as magnetorheological polishing fluid (MRPF), that has the ability to stiffen in the presence of a magnetic field. Base media, iron and abrasive particles are utilized to synthesize the MRPF. Permanent magnets produce the necessary magnetic field in the finishing zone. Finite element analysis (FEA) is utilized to model the iron and abrasive particles to understand better how they would react in the external magnetic field. FEA is utilized to analyze the magnetic flux density (MFD) distributions and the amount of magnetic force exerting on gear profiles through iron particles (IPs). It has been observed that the IPs present close to the active abrasives are primarily accountable for indenting active abrasives into the workpiece surfaces. In addition, the influence of particle dimension on the stiffness of iron particle chains in MRPF has been investigated. A mathematical model for material removal is developed by utilizing normal finishing force analysis on active abrasives. Lastly, the finishing surface characteristics of gear profiles are examined using an optical profilometer, field emission scanning electron microscope (FESEM) and spectroscopic analysis. Finally, 92.68% improvement in the surface finish is observed.

The presence of SiC particulate in the newly developed SiCp/sisal fiber/epoxy composites on drilling, influence the wear in the drill bit. This article presents the experimental investigations of the effect of tool condition on delamination during drilling of composite by the time, frequency domain, and discrete wavelet packet transformation (DWPT) of acoustic emission and cutting force measured in process. It may further be helpful for determining and assessing the root cause of the induced delamination damage. The drill bits are made of high-speed steel (HSS), the condition is categorized into three different types namely as received, cryogenically treated (−80°C for 24 h), and cryogenically treated and tempered (−80°C for 24 h and 200°C for 2 h). Study of the frequency domain reveals that the dominant frequency of the acoustic emission (AE) signals falls between 100 and 300 kHz. The AE-specific energies of the decomposed signals are calculated based on DWPT analysis. It is noted from the AE specific energy that shearing occurs during drilling process. The cryogenically treated and tempered HSS drill contributes significantly to the reduction in delamination compared to other tools employed in this investigation.

Hexapod robots are widely used in applications such as search and rescue, exploration, and surveillance due to their improved mobility and stability on uneven terrain. However, conventional hexapod robots typically suffer from issues such as heavy weight and complex design, which limits their effectiveness in certain scenarios. In this paper, we propose a new hexapod robot that addresses the issues of weight and complexity often associated with conventional hexapod robots. Our proposed design uses telescopic legs connected to pivot joints at the hips, resulting in a two-DOFs-leg configuration. The robot’s six legs are aligned with a parallelepiped body, with each leg configured with a revolute joint and a prismatic joint. With a total of twelve degrees of freedom (DOFs), the proposed robot can move in a straight line or turn. Our solution for stabilizing the robot’s horizontally oriented body on uneven terrain, using analytical kinematics, inverse kinematics, and geometric analysis, is a valuable contribution. Additionally, we present simulation of path generation and gait design for various inclined planes and stairs using a virtual prototype modeled in MATLAB/Simulink Simscape Multibody. The results of our simulated and physical prototype experiments demonstrate that the proposed design and body control algorithm effectively maintain the orientation of the robot’s body usually stable while it moves on different types of surfaces. Overall, our proposed hexapod robot using telescopic legs offers a promising solution for achieving improved mobility and stability on uneven terrain, with reduced weight and complexity compared to conventional hexapod robots.

This work proposed to integrate intelligent techniques in an adaptive equivalent consumption minimization strategy (AECMS) for a robust equivalence factor correction based on the demand torque and battery state of charge (SOC). The two intelligent approaches of fuzzy logic and genetic algorithmically optimized adaptive neuro-fuzzy inference system (ANFIS) have been employed. The entire performance is validated using the prepared three hybrid standard driving cycles and also for training the fuzzy inference system (FIS). The proposed ANFIS-ECMS delivers a narrow battery charging and discharging profile within the optimal battery utilization zone. Also, it delivers the higher fuel economy for the three driving cycles in the D1 4.20%, D2 1.175%, and D3 0.90% improvisation is occurred compared to fuzzy-PI AECMS. Even in the reliability assessment of ANFIS-AECMS, the performance has been tested on the self-developed real-world driving cycle. The fuel economy increases by 1.26% and 0.80% are compared to rule-based and fuzzy-PI AECMS. The entire results of this work evidence that the proposed strategy achieves significant improvement in battery and fuel energy utilization and also the reduction in emissions compared with rule-based, conventional fixed PI ECMS and fuzzy-PI based ECMS.

Hybrid Chemo-Magnetorheological Finishing(HC-MRF) process is an advanced surface finishing process that utilizes the interaction of Magnetorheological Finishing and chemical etching to reduce the surface roughness of the cemented carbide cutting tools. Herein, Murakami’s reagent is used as a Magnetorheological (MR) fluid carrier medium to trigger chemical etching and mechanical abrasion. The synergic action reduces surface finishing time and increases process efficiency. However, optimizing process parameters during the HC-MRF process is required to improve process efficiency further. Response Surface Methodology(RSM) is used to develop the regression model to study the impact of process parameters (i.e. spindle speed, feed rate, and vol.% of Murakami’s reagent in MR fluid) on the surface quality. Three surface roughness parameters, namely average surface roughness (Ra), skewness (Rsk), and kurtosis (Rk), are opted to analyze the surface characteristics of the polished surface. Furthermore, optimization is performed with the help of a Genetic Algorithm(GA) to achieve the best surface quality following input parameters. The surface roughness (Ra) is reduced by 88.97% as the initial Ravalue decreases to 34.50 nm from 312.87 nm after the HC-MRFprocess. The results obtained from RSMand GAare in good agreement with the experimental results. The optimum value of feed rate, spindle speed, and vol.% of Murakami’s reagent in the MR fluid while achieving minimum surface roughness value (i.e. 31.26 nm) through GA are 1.2 mm/min, 23 rpm, and 53%, respectively. The measured kurtosis (Rku) value of 0.64 at the optimum process parameter condition represents flat peaks on the surface roughness irregularities of the polished surface. Similarly, a skewness (Rsk) value of 1.46 signifies that the number of peaks is higher than the number of valleys on the polished surface. Furthermore, it is observed that the Vol.% of Murakami’s reagent in MR fluid is the most significant process parameter compared with other parameters among all responses.

Aircraft composite structures are widely joined using bolts. Differences in the tightening process parameters for tightening bolts can significantly affect the tightening torque (T)-preload (F) relationship. Some of the more significant process parameters include lubrication conditions, tightening speed, tightening area, sealant and washer. The experimental study compared the differences in T-F curves, friction coefficients, torque coefficients and yield preload during the tightening process for different values of the process parameters. The focus was on the state of the thread surface, the state of the bearing surface, the diffusion of the lubricating medium and sealant with preload, etc. The distribution of the T-F curve was obtained for all possible values of the process parameters. As the tightening speed increases, both the inter-thread friction coefficient and the macroscopic increase in the coefficient of friction on the bearing surface are observed when tightening the bolt head. The presence of sealant reduces the inter-thread friction coefficient. The size of the T-F curve area reflects the sensitivity of the bolt tightening process to the various process parameters. The size of the T-F curve area reflects the sensitivity of the bolt tightening process to the various process parameters.

In this paper, an asymmetric 3T1R parallel mechanism is proposed that has higher stiffness in specific directions. The stiffness model is established by using virtual spring theory, and the Cartesian stiffness matrix is obtained. The stiffness performance evaluation indices are deduced by the dimensionless parameterization method. Under the condition of satisfying the high-speed performance and dexterity, the optimal size parameters of the mechanism are got by using the DE (differential evolution) algorithm with stiffness performance as the index. By comparing the stiffness performance atlases before and after the optimization, it is showed that the stiffness performance has been improved and the reachable workspace has been increased. The results verify the feasibility of the proposed performance indices and optimization algorithm.

When exposed to high temperatures, reinforced concrete members undergo major changes in their properties which in turn affects their structural performance. Fire accidents in frame structures at local or global level may lead to collapse of structures. Structural fire has dire consequences like collapse of structures, colour changes, spalling of concrete and failure of essential elements. It is essential to study the conduct of reinforced concrete frame under thermal expansion in order to understand fire-induced collapse of concrete structures. This study observes how a three dimensional single-bay single-storey reinforced concrete frame is performed under thermo-mechanical analysis in ABAQUS (FEA). The paper aims to note the effect of slab and infill wall with respect to thermal expansion. The paper also discusses the main findings regarding the structural performance, structural leading conditions and structural stiffness of the reinforced concrete frame with slab and infill wall under thermal expansion. The results found that the infill frame has better stiffness and performs well under fire conditions.

Exact closed-form solutions for the free vibration analysis of multiple cracked FGM nanobeams with arbitrary boundary conditions based on the Nonlocal Elasticity Theory (NET) and the Timoshenko beam theory are presented. The NET considering the size effect of nanostructures is applied. FGM properties vary nonlinearly along the height of the beam. A crack model using two springs with stiffness depending on the crack depth is applied. The proposed solutions are provided explicitly as functions of integration constants determined from the standard boundary conditions. Frequency equations, that established in the form of the determinant of the matrix 3 × 3 order for arbitrary boundary conditions, are applied to analyse free vibration. Expressions for the mode shapes are given explicitly. The effects of geometry, material, nonlocal and crack parameters on the free vibration of the multiple cracked nanobeam are then analysed in detail.

This study aims to design and calculate a model for a wall-climbing robot to replace humans in performing dangerous tasks at great heights in construction sites. The research simultaneously addresses three main issues in the process of calculation, design, and simulation of the vertically climbing robot model. Firstly, the study conducts calculations and analyzes the dynamics of the model in various working states. Specifically, it calculates the suction force of the fan in non-contact conditions to ensure the suction capability of the model during practical operations. Secondly, the core content of this method involves utilizing the airflow generated by the engine through a specially designed suction mechanism to increase the airflow velocity significantly, thus creating a low-pressure area capable of adhering to the wall. This suction force calculation method and other forces acting on the robot model are based on numerical simulation software. The boundary conditions in the calculation process for the wall-climbing robot model are derived from the tasks that the robot performs during real operations, replacing humans. Thirdly, the study designs the wall-climbing robot using the non-contact suction force method through a 3D-printed fan model to enhance the model’s durability and optimize the airflow under various conditions. In summary, through this research, the paper aims to construct a robot model using non-contact suction and complete it entirely with 3D printing technology. This model will generate cost-effective and highly efficient robot vehicles that can be widely applied in modern industrial environments.

Better insulation or material with low thermal conductivity is a crucial factor in new construction and retrofitting existing structures where energy efficiency is the objective. Effective energy savings need the careful selection of construction materials to accomplish this. For this purpose, in this research work, a typical residential flat is taken into account. Using the hourly analysis program (HAP 4.90) software, the cooling and heating load is determined. Eight influencing factors, including building orientation (A), overall heat transfer coefficient of wall (B), roof insulation material (C), roof insulation thickness (D), heat transfer coefficient of glass (E), window to wall ratio (F), air infiltration flow rate (G), and bypass factor of air-conditioner (H) at three levels, are chosen to optimize the cooling and heating load. An orthogonal L27 layout is considered for the optimization, and the cooling and heating load has been determined for each experimental attempt. At first using Taguchi method, ANOVA analysis is done separately for each response variable and later using GRA and TOPSIS based Taguchi analysis, multi-objective optimization has been done, and the results are compared with each other. By determining each factor’s percentage contribution to the cooling and heating load, the ANOVA approach is also used to identify the primary components that have a major impact on the cooling and heating load. The outcome demonstrates that A3B2C2D3E1F1G1H1 is the best possible combination of all eight variables, and overall heat transfer coefficient of wall is the most influencing parameter for the residential buildings.