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The paper's goal is to examine and illustrate the useful uses of submodeling in finite element modeling for topology optimization and stress analysis. The goal of the study is to demonstrate how submodeling – more especially, a 1D approach – can reliably and effectively produce ideal solutions for challenging structural issues. The paper aims to demonstrate the usefulness of submodeling in obtaining converged solutions for stress analysis and optimized geometry for improved fatigue life by studying a cantilever beam case and using beam formulations. In order to guarantee the precision and dependability of the optimization process, the developed approach will also be validated through experimental testing, such as 3-point bending tests and 3D printing. Using 3D finite element models, the 1D submodeling approach is further validated in the final step, showing a strong correlation with experimental data for deflection calculations.

The authors conducted a literature review to understand the existing research on submodeling and its practical applications in finite element modeling. They selected a cantilever beam case as a test subject to demonstrate stress analysis and topology optimization through submodeling. They developed a 1D submodeling approach to streamline the optimization process and ensure result validity. The authors utilized beam formulations to optimize and validate the outcomes of the submodeling approach. They 3D-printed the optimized models and subjected them to a 3-point bending test to confirm the accuracy of the developed approach. They employed 3D finite element models for submodeling to validate the 1D approach, focusing on specific finite elements for deflection calculations and analyzed the results to demonstrate a strong correlation between the theoretical models and experimental data, showcasing the effectiveness of the submodeling methodology in achieving optimal solutions efficiently and accurately.

The findings of the paper are as follows: 1. The use of submodeling, specifically a 1D submodeling approach, proved to be effective in achieving optimal solutions more efficiently and accurately in finite element modeling. 2. The study conducted on a cantilever beam case demonstrated successful stress analysis and topology optimization through submodeling, resulting in optimized geometry for enhanced fatigue life. 3. Beam formulations were utilized to optimize and validate the outcomes of the submodeling approach, leading to the successful 3D printing and testing of the optimized models through a 3-point bending test. 4. Experimental results confirmed the accuracy and validity of the developed submodeling approach in streamlining the optimization process. 5. The use of 3D finite element models for submodeling further validated the 1D approach, with specific finite elements showing a strong correlation with experimental data in deflection calculations. Overall, the findings highlight the effectiveness of submodeling techniques in achieving optimal solutions and validating results in finite element modeling, stress analysis and optimization processes.

The originality and value of the paper lie in its innovative approach to utilizing submodeling techniques in finite element modeling for structural analysis and optimization. By focusing on the reduction of finite element models and the creation of smaller, more manageable models through submodeling, the paper offers designers a more efficient and accurate way to achieve optimal solutions for complex problems. The study's use of a cantilever beam case to demonstrate stress analysis and topology optimization showcases the practical applications of submodeling in real-world scenarios. The development of a 1D submodeling approach, along with the utilization of beam formulations and 3D printing for experimental validation, adds a novel dimension to the research. Furthermore, the paper's integration of 1D and 3D submodeling techniques for deflection calculations and validation highlights the thoroughness and rigor of the study. The strong correlation between the finite element models and experimental data underscores the reliability and accuracy of the developed approach. Overall, the originality and value of this paper lie in its comprehensive exploration of submodeling techniques, its practical applications in structural analysis and optimization and its successful validation through experimental testing.

The purpose of this research is to establish a robust numerical framework for the calibration of macroscopic constitutive parameters, based on the analysis of polycrystalline RVEs with computational homogenisation.

This framework is composed of four building-blocks: (1) the multi-scale model, consisting of polycrystalline RVEs, where the grains are modelled with anisotropic crystal plasticity, and computational homogenisation to link the scales, (2) a set of loading cases to generate the reference responses, (3) the von Mises elasto-plastic model to be calibrated, and (4) the optimisation algorithms to solve the inverse identification problem. Several optimisation algorithms are assessed through a reference identification problem. Thereafter, different calibration strategies are tested. The accuracy of the calibrated models is evaluated by comparing their results against an FE2 model and experimental data.

In the initial tests, the LIPO optimiser performs the best. Good results accuracy is obtained with the calibrated constitutive models. The computing time needed by the FE2 simulations is 5 orders of magnitude larger, compared to the standard macroscopic simulations, demonstrating how this framework is suitable to obtain efficient micro-mechanics-informed constitutive models.

This contribution proposes a numerical framework, based on FE2 and macro-scale single element simulations, where the calibration of constitutive laws is informed by multi-scale analysis. The most efficient combination of optimisation algorithm and definition of the objective function is studied, and the robustness of the proposed approach is demonstrated by validation with both numerical and experimental data.

This study aims to identify the location of cracks in composite plates using a normalized mode shape curve algorithm. Crack in any structure is a destructive occurrence. Detecting these cracks early is pivotal for ensuring safety and preventing potential accidents. To prevent failure of structures, it is crucial to detect these cracks effectively and take the necessary precautions. Hence, crack detection and localization techniques are used to avoid sudden failures of structures while in operation.

An experimental modal analysis is conducted on composite plates with and without cracks to determine the natural frequencies and mode shapes. For this purpose, an impact hammer, uniaxial accelerometer and four-channel vibration analyzer are used to find the natural frequencies and mode shapes. Numerical modal analysis is performed on no crack and cracked composite plates using ANSYS software, and these are validated by the experimental modal analysis results. The normalized mode shapes algorithm is trained using test data of the first three natural frequencies collected from numerical modal analysis on different cracked composite plates for localization of crack.

The natural frequencies derived from both experimental modal analysis and numerical modal analysis exhibit a variance of 9.6%. The estimation of the crack location is achieved with exceptional precision by intersecting the first three normalized mode shapes. The first three normalized mode shape curve intersections provide a solid indication of the crack’s location. As the difference in error between the actual and estimated crack locations is only 0.9%.

This study introduces the first application of experimental modal analysis in conjunction with the normalized mode shape curve algorithm for localizing cracks in composite plates. The normalization process of mode shapes, derived from experimental modal analysis, forms a fundamental component of the mode shape curve algorithm specifically designed for crack localization. Combining experimental modal analysis with a specific algorithm of normalizing mode shapes is used to identify and locate cracks within these composite plates.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

This study aims to further refine the model, explore the influence of cutting parameters on the machining process, and apply it to practical engineering to improve the efficiency and quality of titanium alloy machining.

This paper establishes a comprehensive thermo-mechanical fully coupled orthogonal cutting model. This paper aims to couple the modified Johnson–Cook constitutive model, damage model and contact model to construct a two-dimensional orthogonal cutting thermo-mechanical coupling model for high-speed cutting of Ti6Al4V. The model considers the evolution of microstructures such as plastic deformation, grain dislocation rearrangement, dynamic recrystallization, as well as stress softening and hardening occurring continuously in Ti6Al4V metal during high-speed cutting. Additionally, the model incorporates friction and contact between the tool and the workpiece. It can be used to predict parameters such as cutting process, cutting force, temperature distribution, stress and strain in titanium alloy machining. The study establishes the model and implements corresponding functions by writing Abaqus VUMAT and VFRICTION subroutines.

The use of different material constitutive models can significantly impact the prediction of the cutting process. Some models may more accurately describe the mechanical behavior of the material, thus providing more reliable prediction results, while other models may exhibit larger deviations. Compared to the Tanh model, the proposed model achieves a maximum improvement of 8.9% in the prediction of cutting force and a maximum improvement of 20.9% in the prediction of chip morphology parameters. Compared to experiments, the proposed model achieves a minimum prediction error of 2.8% for average cutting force and a minimum error of 0.57% for sawtooth parameters. This study provides a comprehensive theoretical foundation and practical guidance for orthogonal cutting of titanium alloys. The model not only helps engineers and researchers better understand various phenomena in the cutting process but also serves as an important reference for optimizing cutting processes.

The originality of this research is guaranteed, as it has not been previously published in any journal or publication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0168/

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

This paper aims to investigate the effect of different beam distance by understanding laser beam influence on solder joint quality. The utilisation numerical-based simulations and experimental validation will help to minimise the formation of micro void in PTH that can lead to cracks and defects on passive devices.

The research uses a combination approach of numerical-based simulation using Finite Volume Method (FVM) and experimental validation to explore the impact of different laser beam distances on solder joint quality in PTH assemblies. The study visualises solder flow and identifies the optimal beam distance for placing a soldering workpiece and a suitable tolerance distance for inserting the solder wire.

The simulation results show the formation of micro void that occurs in PTH region with low volume fraction and unbalance heat concentration profile observed. The experimental results indicate that the focus point of the laser beam at a 99.0 mm distance yields the smallest beam size. Simulation visualisation demonstrates that the laser beam’s converging area at +4.6 mm from the focus point which provides optimal tolerance distances for placing the solder wire. The high-power laser diode exhibits maximum tolerance distance at 103.6 mm from the focus point where suitable beam distance for positioning of the soldering workpiece with 50% laser power. The simulation results align with the IPC-A-610 standard, ensuring optimal filling height, fillet shape with a 90° contact angle and defect-free.

This research provides implications for the industry by demonstrating the capability of the simulation approach to produce high-quality solder joints. The parameters, such as beam distance and power levels, offer practical guidelines for improving laser soldering processes in the manufacturing industry.

This study contributes to the field by combining high-power laser diode technology with numerical-based simulations to optimise the beam distance parameters for minimising micro void formation in the PTH region.

This paper aims to investigate the influence of composite solder joint preparation on the thermal properties of metal-oxide-semiconductor field-effect transistors (MOSFETs) and the mechanical strength of the soldered joint.

Reinforced composite solder joints with the addition of titanium oxide nanopowder (TiO₂) were prepared. The reference alloy was Sn99Ag0.3Cu0.7. Reinforced joints differed in the weight percentage of TiO₂, ranging from 0.125 to 1.0 Wt.%. Two types of components were used for the tests. The resistor in the 0805 package was used for mechanical strength tests, where the component was soldered to the FR4 substrate. For thermal parameters measurements, a power element MOSFET in a TO-263 package was used, which was soldered to a metal core printed circuit board (PCB) substrate. Components were soldered in batch IR oven.

Shear tests showed that the addition of titanium oxide does not significantly increase the resistance of the solder joint to mechanical damage. Titanium oxide addition was shown to not considerably influence the soldered joint’s mechanical strength compared to reference samples when soldered in batch ovens. Thermal resistance R_thj-a of MOSFETs depends on TiO₂ concentration in the composite solder joint reaching the minimum R_thj at 0.25 Wt.% of TiO₂.

Mechanical strength: TiO₂ reinforcement shows minimal impact on mechanical strength, suggesting altered liquidus temperature and microstructure, requiring further investigation. Thermal performance: thermal parameters vary with TiO₂ concentration, with optimal performance at 0.25 Wt.%. Experimental validation is crucial for practical application. Experimental confirmation: validation of optimal concentrations is essential for accurate assessment and real-world application. Soldering method influence: batch oven soldering may affect mechanical strength, necessitating exploration of alternative methods. Thermal vs mechanical enhancement: while TiO₂ does not notably enhance mechanical strength, it improves thermal properties, highlighting the need for balanced design in power semiconductor assembly.

Incorporating TiO₂ enhances thermal properties in power semiconductor assembly. Optimal concentration balancing thermal performance and mechanical strength must be determined experimentally. Batch oven soldering may influence mechanical strength, requiring evaluation of alternative techniques. TiO₂ composite solder joints offer promise in power electronics for efficient heat dissipation. Microstructural analysis can optimize solder joint design and performance. Rigorous quality control during soldering ensures consistent thermal performance and mitigates negative effects on mechanical strength.

The integration of TiO₂ reinforcement in solder joints impacts thermal properties crucial for power semiconductor assembly. However, its influence on mechanical strength is limited, potentially affecting product reliability. Understanding these effects necessitates collaborative efforts between researchers and industry stakeholders to develop robust soldering techniques. Ensuring optimal TiO₂ concentration through experimental validation is essential to maintain product integrity and safety standards. Additionally, dissemination of research findings and best practices can empower manufacturers to make informed decisions, fostering innovation and sustainability in electronic manufacturing processes. Ultimately, addressing these social implications promotes technological advancement while prioritizing consumer trust and product quality in the electronics industry.

The research shows the importance of the soldering technology used to assemble MOSFET devices.

The external confinement provided by the fiber-reinforced polymer (FRP) sheets leads to an improvement in the axial compressive strength (CS) and strain of reinforced concrete structural members. Many studies have proposed analytical models to predict the axial CS of concrete structural members, but the predictions for the axial compressive strain still need more investigation because the previous strain models are not accurate enough. Moreover, the previous strain models were proposed using small and noisy databases using simple modeling techniques. Therefore, a rigorous approach is needed to propose a more accurate strain model and compare its predictions with the previous models.

The present work has endeavored to propose strain models for FRP-confined concrete members using three different techniques: analytical modeling, artificial neural network (ANN) modeling and finite element analysis (FEA) modeling based on a large database consisting of 570 sample points.

The assessment of the previous models using some statistical parameters revealed that the estimates of the newly recommended models were more accurate than the previous models. The estimates of the new models were validated using the experimental outcomes of compressive members confined with carbon-fiber-reinforced polymer (CFRP) wraps. The nonlinear FEA of the tested samples was performed using ABAQUS, and its estimates were equated with the calculations of the analytical and ANN models. The relative investigation of the estimates solidly substantiates the accuracy and applicability of the recommended analytical, ANN and FEA models for predicting the axial strain of CFRP-confined concrete compression members.

The research introduces innovative methods for understanding FRP confinement in concrete, presenting new models to estimate axial compressive strains. Utilizing a database of 570 experimental samples, the study employs ANNs and regression analysis to develop these models. Existing models for FRP-confined concrete's axial strains are also assessed using this database. Validation involves testing 18 cylindrical specimens confined with CFRP wraps and FE simulations using a concrete-damaged plastic (CDP) model. A comprehensive comparative analysis compares experimental results with estimates from ANNs, analytical and finite element models (FEMs), offering valuable insights and predictive tools for FRP confinement in concrete.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.