Accurate Design Research Articles

Integrated design optimization method for pavement structure and materials based on further development of finite element and particle swarm optimization algorithm

The maintenance design for existing asphalt pavements, characterized by their typical structure and material composition, falls short of meeting the diverse requirements of pavements. There is an urgent need for a more rational design approach that can achieve accurate pavement design while also reducing construction costs. This paper focuses on selected conventional road surface structures and conducts an analysis of how pavement structure and materials impact fatigue life and construction expenses. It employs Python to perform three-dimensional finite element road modeling, analysis, and data extraction at specific locations, using normalized fatigue equation to calculate pavement fatigue life. The optimization variables selected are the modulus E and thickness h, with pavement maintenance cost serving as the optimization target function. The particle swarm optimization algorithm is employed for optimizing these variables. A comparative analysis is carried out for five improved optimization algorithms, optimizing combinations of inertia weight, learning factors, particle speed, number of particles, and iteration times. The results reveal that the enhanced optimization algorithms significantly improve iteration trends, featuring velocity limitations and natural selection mechanisms. Between 0 and 20 iterations, all algorithms search for the optimal solution over a wider interval with greater fluctuations. Between 20 and 30 iterations, each algorithm gradually converges towards its solution, stabilizing in the later stages. Based on optimization speed and actual engineering cost benchmarks, the NDPSO algorithm is selected, resulting in a 52.8% reduction in actual engineering costs. Adjustments to the particle count and iteration number of the NDPSO algorithm are made based on computational cost and optimization accuracy, thus choosing 20 particles and 50 iterations as the recommended parameter combination. By combining automated finite element modeling, analysis, and particle swarm optimization, this paper offers a novel and effective automated design reference for extending the lifespan of asphalt pavement structures.

An integrated design concept evaluation model based on interval valued picture fuzzy set and improved GRP method.

The objective of this research is to enhance the precision and efficiency of design concept assessments during the initial stages of new product creation. Design concept evaluation, which occurs at the end of the conceptual design phase, is a critical step in product development. The outcome of this evaluation significantly impacts the product's eventual success, as flawed design concepts are difficult to remedy in later stages. However, the evaluation of new product concepts is a procedure that encompasses elements of subjectivity and ambiguity. In order to deal with the problem, a novel decision-making method for choosing more logical new product concepts is introduced. Basically, the evaluation process is outlined in three main phases: the construction of evaluation index system for design concept alternatives, the calculation of weights for evaluation criteria and decision-makers, the selection of the best design concept alternatives. These stages are composed of a hybrid method based on kano model, multiplicative analytic hierarchy process (AHP) method, the entropy of IVPFS and improved grey relational projection (GRP) under interval-valued picture fuzzy set (IVPFS). The novel approach integrates the strength of interval-valued picture fuzzy number in handling vagueness, the advantage of multiplicative AHP and the merit of improved GRP method in modelling multi-criteria decision-making. In final, the effectiveness of the proposed model is validated through comparisons with other models. The potential applications of this study include but are not limited to product development, industrial design, and innovation management, providing decision-makers with a more accurate and comprehensive design concept evaluation tool.

Perancangan Enterprise Arsitektur Menggunakan TOGAF ADM Di Bea Cukai Tanjung Pandan pada Aplikasi SIMPORA

The web-based SIMPORA Customs Tanjung Pandan application is an efficient solution for optimizing annual budgeting business processes. Customers can easily get the latest information about the services provided in real-time. So it is hoped that it can improve performance significantly. To develop it, several data collection techniques were used, namely by conducting in-depth interviews, making observations and documenting them. Meanwhile, the research methodology is TOGAF ADM 9.1. This method is known to be the best method that can develop architecture in a structured and reliable manner. Several types of diagrams that help in designing this system are Value Chain, BPMN, Context Diagram, Use Case, and Class Diagram. It is hoped that this enterprise architecture design will enable budgeting to be monitored and improved immediately by stakeholders. The result of this research is an enterprise architecture design to support business processes at Tanjung Pandan Customs and Excise. It is hoped that various budgeting errors and design inaccuracies can be reduced and eliminated with this application, so that it can support the optimization of this institution's business processes.

A New Design Method for Obround Shells

Abstract The design of obround components has been difficult and complex due to the absence of a suitable theoretical solution. Current design codes use empirical formulas based on assumptions to determine the minimum required thickness for obround components. The design process involves iteration. The results obtained are far from accurate. Based on a recently proposed closed-form solution, a new design method for obround components is developed. In the new method, an obround shell is imagined as being stretched laterally from a cylindrical shell or parent cylinder. With the flattened shell increasing, the thickness of the obround shell should increase accordingly, to keep the maximum hoop stress of obround shell consistent with the maximum hoop stress of the parent cylinder. The minimum requested thickness (MRT), of the obround shell is related to the MRT of the parent cylinder and the flattened shell length of the obround shell. Through parametric studies, those relationships are investigated. Empirical expressions are used to describe those relationships. Using the proposed design method, the determination of the MRT of obround members becomes the determination of the MRT of the parent cylinders and additional parameters. The MRT of obround members is determined directly. The case study demonstrates that the proposed method is an efficient and accurate design process for obround members. The use of new method for fitness-for-service was explored. The new method can be used to reassess operational safety in the event of vessel materials' deterioration.

Design of press-braked stainless steel C-beams subjected to global-distortional interaction buckling

A detailed finite element (FE) model studying the global-distortional (G-D) interaction of press-braked stainless steel C-beams was reported herein. According to previous bearing capacity tests, the model incorporating measured material characteristics and precise geometric imperfections was validated by comparing to the failure mode, load-vertical displacement curve and bearing capacity. Following the calibration of the model, comprehensive parametric investigations were conducted to investigate the influence of the distribution and amplitude of geometric imperfections, material strain hardening coefficient and effect of cold working. It is found that the distribution of geometric imperfections has significant and complex impacts on the failure mode and ultimate capacity, while the amplitude of imperfection could have positive or negative effects on the ultimate capacity. The accuracy of the design methods provided by EN 1993–1-4, AS/NZS 4673 and ASCE 8–22 was assessed using the test data. All codes are found to account for distortional buckling by employing the Effective Width Method (EWM) to reduce the compression plate area. The calculation results were discrete. Therefore, based on the Direct Strength Method (DSM), a novel and accurate design method was developed for the G-D interaction of stainless steel C-beams. The strong correlation between the ratio of ultimate capacity to global buckling bearing capacity and the G-D interaction slenderness was verified by 260 numerical analyses prior to proposing the new design method. The proposed design method underwent a reliability analysis using first order reliability method to meet the requirement of ASCE 8–22.

Comparative study and validation of a new analytical method for hydraulic modelling of bidirectional low temperature networks

Bidirectional Low Temperature Networks (BLTNs) are an innovative technology of district heating and cooling systems. Because of their intrinsic feature of supplying simultaneous heating and cooling either directly from an energy source/sink or through distributed heat pumps and chillers, circulation pumps installed at each building extract water from the network and a distinct pressure cone is subsequently created. Variations in the amount (and direction) of the extracted water call for accurate hydraulic design and analysis to ensure safe and adequate network operation. This paper proposes a new analytical solution method for fast hydraulic modelling of BLTNs. The analytical method was compared against three numerical solution methods and validated using different current and future operating scenarios of a real-world bidirectional network. In all modelled cases, the calculated mass flows and pressure drops using the new analytical method yielded very small Normalised Mean Bias Error (NMBE) and Coefficient of Variation of the Root Mean Square Error (CV(RMSE)), suggesting excellent agreement between the analytical and numerical solution methods. The new analytical method offers better performance over its numerical counterparts due to its explicit nature, ease of implementation, and quick modelling time.

Adaptive Set-Membership Filter Based Discrete Sliding Mode Control for Pneumatic Artificial Muscle Systems With Hardware Experiments

Pneumatic artificial muscle (PAM), featuring good flexibility and safety, has been widely used in rehabilitation and bionic robots. However, the complex hysteretic nonlinearities and uncertainties of the PAM cause great difficulties and challenges to the accurate modeling and controller design, especially when confronted with unknown external disturbances in applications. This paper proposes a robust control strategy with disturbance compensation for the hysteresis compensation and trajectory tracking of PAMs. Considering the high hysteretic nonlinearity of the PAM, a modified Prandtl-Ishlinskii model is used as a feedforward hysteresis compensator. For the linearized system, adaptive set-membership filtering (ASMF) is used to estimate the nonlinear terms and external disturbances of the overall system. A sliding mode controller (SMC) with disturbance compensation is designed and cascaded to the feedforward hysteresis compensator in series. The stability of the closed-loop system is theoretically proved. The proposed method guarantees that the tracking error of the PAM system is bounded. Finally, the effectiveness and robustness of the proposed controller are verified via a series of experiments on an in-house built testbench for PAMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —With the increasing demand on human-robot interaction, the safety and compliance of robots have become one key requirement. PAM is a compliant actuator, exhibiting good flexibility, safety, and clean energy. PAM is widely used in rehabilitation robots, whereas its strong hysteresis nonlinearity and sensitivity to external disturbances affect its motion accuracy. This paper proposes an ASMF-based discrete SMC, which uses an inverse hysteresis model to compensate for the strong hysteresis of the PAM and uses ASMF to estimate the lumped disturbance of the system. Compared with the other filters, ASMF is unique in that its estimation error is bounded, which is very useful in the stability proof of the overall system. The effectiveness of the proposed controller is experimentally verified. Experimental results show that PAM’s hysteresis can be efficiently compensated, and the influence of external disturbances can be attenuated by the proposed controller, resulting in improved motion accuracy and robustness. In future work, efforts will be directed towards the modeling and control of PAMs in multi-DOF robots.

Flare: An FPGA-Based Full Precision Low Power CNN Accelerator with Reconfigurable Structure.

Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate the application of CNNs in power constrained environments, the development of dedicated CNN accelerators is crucial. Prior research has predominantly concentrated on developing low precision CNN accelerators using code generated from high-level synthesis (HLS) tools. Unfortunately, these approaches often fail to efficiently utilize the computational resources of field-programmable gate arrays (FPGAs) and do not extend well to full precision scenarios. To overcome these limitations, we integrate vector dot products to unify the convolution and fully connected layers. By treating the row vector of input feature maps as the fundamental processing unit, we balance processing latency and resource consumption while eliminating data rearrangement time. Furthermore, an accurate design space exploration (DSE) model is established to identify the optimal design points for each CNN layer, and dynamic partial reconfiguration is employed to maximize each layer's access to computational resources. Our approach is validated through the implementation of AlexNet and VGG16 on 7A100T and ZU15EG platforms, respectively. We achieve an average convolutional layer throughput of 28.985 GOP/s and 246.711 GOP/s for full precision. Notably, the proposed accelerator demonstrates remarkable power efficiency, with a maximum improvement of 23.989 and 15.376 times compared to current state-of-the-art FPGA implementations.

Cu-Doped CoZn@NC promotes catalytic activity of furfural hydrogenation at low temperature: Regulation of copper

The low-cost Cu-based catalysts have excellent selectivity in biomass hydrogenation, but it is challenging to improve their catalytic activity at low temperature. Herein, the trimetallic CuxCoZn@NC (where x = 0.1, 0.3, 0.5 mmol is the molar mass of Cu) were prepared by in suit pyrolysis using trimesic acid with stronger coordination ability. When it was applied to the furfural (FF) selective hydrogenation process, the catalytic yield of the optimal Cu0.3CoZn@NC reached >99 % under mild condition of 90 °C, which was 29 times superior to that of the CoZn@NC without Cu doping. The results demonstrate that the doping of Cu reduces the activation energy of CuxCoZn@NC and increases the catalytic activity at low temperature relative to CoZn@NC. The characterizations and density functional theory (DFT) calculations show that the incorporation of suitable Cu content enhances the interaction between Cu and Co. The amount of surface-active Co0 and the electron density are significantly enhanced, which effectively stimulates the dissociation of hydrogen. Moreover, the Lewis acid content is significantly increased to facilitate the adsorption and activation of FF. Meanwhile, the impact of Cu content variation on the performance of the multi-metal catalyst is discussed. This work is promising to establish a foundation for the accurate design of polymetallic catalysts through adjusting the Cu content to regulate the reaction activity and selectivity.

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

The engineering sector is undergoing digital transformation (DT) alongside shifts in labor patterns. This study concentrates on piping design within plant engineering, aiming to develop a system for optimal piping route design using artificial intelligence (AI) technology. The objective is to overcome limitations related to time and costs in traditional manual piping design processes. The ultimate aim is to contribute to the digitalization of engineering processes and improve project performance. Initially, digital image processing was utilized to digitize piping and instrument diagram (P&amp;ID) data and establish a line topology set (LTS). Subsequently, three-dimensional (3D) modeling digital tools were employed to create a user-friendly system environment that visually represents piping information. Dijkstra’s algorithm was implemented to determine the optimal piping route, considering various priorities during the design process. Finally, an interference avoidance algorithm was used to prevent clashes among piping, equipment, and structures. Hence, an auto-routing system (ARS), equipped with a logical algorithm and 3D environment for optimal piping design, was developed. To evaluate the effectiveness of the proposed model, a comparison was made between the bill of materials (BoM) from Company D’s chemical plant project and the BoM extracted from the ARS. The performance evaluation revealed that the accuracy in matching pipe weight and length was 105.7% and 84.9%, respectively. Additionally, the accuracy in matching the weight and quantity of fittings was found to be 99.7% and 83.9%, respectively. These findings indicate that current digitalized design technology does not ensure 100% accurate designs. Nevertheless, the results can still serve as a valuable reference for attaining optimal piping design. This study’s outcomes are anticipated to enhance work efficiency through DT in the engineering piping design sector and contribute to the sustainable growth of companies.

Global optimization for the design of shell and tube horizontal thermosyphon reboiler

Shell and tube horizontal thermosyphon reboilers (STHTs) find wide applications in the process industry particularly due to their inherent advantages in handling large duties, high viscosity fluids and low fouled susceptibility. However, current design commercial packages suffer from obvious limitations including extensive user manipulations, trial-and-error necessity, increased human factor influence. To overcome these limitations, we develop in this work a rigorous MINLP model for STHT sizing, by achieving automated selection of detailed exchanger geometries with optimal overall heat transfer coefficient. The corrected minimum cross-flow area in shell-side is incorporated to accommodate full cross-flow patterns in tube bundle, thus handling popular service of X shell without baffle construction. A dependable convective boiling heat transfer model is proposed to obtain the shell-side heat transfer coefficient, validated with Aspen EDR. The critical variable, heat flux, which affects nucleate boiling heat transfer performance, is optimized through a simultaneous equation-solving process using a global solver, reducing excessive unit overdesign. Pressure balance between STHT and distillation column is fully considered by a proposed optimization model incorporating pressure drop and pipe sizing. By employing global optimization techniques and automating the design process, this innovative approach minimizes manual adjustments and provides more accurate and cost-effective designs.

Unified machine-learning-based design method for normal and high strength steel I-section beam–columns

High strength steel is regarded as a promising construction material due to its superior mechanical properties. However, the codified failure load predictions for high strength steel welded I-section beam–columns are not accurate in some cases owing to the lack of relevant design codes for high strength steel structures. In addition, current design codes ignore the interaction effect of the cross-section plate elements, leading to the inaccuracy of codified predictive ultimate resistances for both normal and high strength steel beam–columns. In this paper, a unified and accurate design method was proposed based on machine learning for both normal and high strength steel welded I-section beam–columns failing by different failure modes. Firstly, a total of 812 experimental and numerical data on welded I-section beam–columns with various steel grades, geometric dimensions, including cross-section dimensions and member lengths, and failure modes, including global buckling, local buckling and local–global interactive buckling were collected to establish a database. Seven machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbour, Adaptive Boosting, Extreme Gradient Boosting and Categorical Boosting were applied to develop regression models to predict failure loads. Based on the established database, each machine learning model was then trained and key hyperparameters were optimised. The model performance was evaluated through a series of statistical indicators and the feature importance analysis, and the evaluated results indicated that the XG-trained regression model had the highest level of accuracy. Finally, the predictions given by the XG-trained regression model were compared with those of the existing codified design provisions, as given in Eurocode and American codes. The evaluation results indicated that the codified predictive bearing capacities of I-section beam–columns were scattered and inaccurate, while the XG-trained regression model provided substantially improved failure load predictions.

A shorter inverted repeat length in a hairpin RNA results in a stronger silencing efficacy

RNA interference (RNAi), particularly hairpin RNA (hpRNA) constructs, is a potent genetic manipulation tool, influencing traits that are crucial for crop improvement. Typical hpRNA constructs consist of inverted repeat sequences derived from the mRNA of the target gene, separated by a non-complementary spacer. This configuration initiates the formation of a double-stranded RNA structure due to the complementary nature of sense and antisense sequences in the transcribed RNA. In this research, two hpRNA constructs, namely hp-TFL1-S and hp-TFL1-L, with distinct lengths of inverted repeats, were designed to target TFL1, a pivotal regulator of both flowering timing and inflorescence structure. hp-TFL1-L construct encompasses all the 150 nucleotide sequences of the hp-TFL1-S construct and an extra 135 nucleotides. These constructs included a chalcone synthase intron as a spacer, resulting in hairpin double-stranded RNA molecules of 150 and 285 base pair after transcription. The PCR and qRT-PCR analysis results confirmed the accurate design of both constructs and their successful suppression of TFL1 mRNA expression in transgenic Arabidopsis. Interestingly, Arabidopsis lines carrying hp-TFL1-S and hp-TFL1-L exhibited distinct phenotypic features. hp-TFL1-S induced more pronounced phenotypic changes, resulting in earlier flowering compared to the hp-TFL1-L and control group. Moreover, hp-TFL1-S had a significant impact on inflorescence architecture, distinguishing it from both hp-TFL1-L and control plants. This study provides an example where inverted repeat length alters the efficacy of silencing, and in this case, surprisingly the shorter construct appears to have a stronger silencing efficacy. This argues multiple sized hpRNAs should be tested in RNAi-based gene silencing experiments.

Error Analysis of Normal Surface Measurements Based on Multiple Laser Displacement Sensors.

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling.

Computing in-memory reconfigurable (accurate/approximate) adder design with negative capacitance FET 6T-SRAM for energy efficient AI edge devices

Computing in-memory (CiM) is an alternative to von-Neumann architectures for energy efficient AI edge computing architectures with CMOS scaling. Approximate computing in-memory (ACiM) techniques have also been recently proposed to further increase the energy efficiency of such architectures. In the first part of the work, a negative capacitance FET (NCFET) based 6T-SRAM CiM accurate full adder has been proposed, designed and performance benchmarked with equivalent baseline 40 nm CMOS design. Due to the steep slope characteristics of NCFET, at an increased ferroelectric layer thickness, T fe of 3 nm, the energy consumption of the proposed accurate NCFET based CiM design is ∼82.48% lower in comparison to the conventional/Non CiM full adder design and ∼85.27% lower energy consumption in comparison to the equivalent baseline CMOS CiM accurate full adder design at V DD = 0.5 V. This work further proposes a reconfigurable computing in-memory NCFET 6T-SRAM full adder design (the design which can operate both in accurate and approximate modes of operation). NCFET 6T-SRAM reconfigurable full adder design in accurate mode has ∼4.19x lower energy consumption and ∼4.47x lower energy consumption in approximation mode when compared to the baseline 40 nm CMOS design at V DD = 0.5 V, making NCFET based approximate CiM adder designs preferable for energy efficient AI edge CiM based computing architectures for DNN processing.

A Systematic Review on Heat Transfer and Pressure Drop Correlations for Natural Refrigerants

Due to environmental concerns, natural refrigerants and their use in refrigeration and air conditioning systems are receiving more attention from manufacturers, end users and the scientific community. The study of heat transfer and pressure drop is essential for accurate design and more energy efficient cycles using natural refrigerants. The aim of this work is to provide an overview of the latest outcomes related to heat transfer and pressure drop correlations for ammonia, propane, isobutane and propylene and to investigate the current state of the art in terms of operating conditions. Available data on the existing correlations between heat transfer coefficients and pressure drops for natural refrigerants have been collected through a systematic search. Whenever possible, validity intervals are given for each correlation, and the error is quantified. It is the intention of the authors that this paper be a valuable support for researchers and an aid to design, with particular reference to heat pumps. A procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was adopted, and the Scopus database was used to query the relevant literature. A total of 135 publications qualified for inclusion in the survey; 34 articles report experimental investigations for unusual geometric conditions. Of the 101 selected papers related to usual geometric conditions, N = 50 deal only with HTC, N = 16 deal only with pressure drop and the remainder (N = 35) analyse both HTC and pressure drop. Among the 85 HTC papers, N = 53 deal with the evaporating condition, N = 30 with condensation and only N = 2 with the heat transfer correlations under both conditions. Most of the 101 articles concern propane and isobutane. The high temperatures are less widely investigated.

Limits for the Punching Shear Capacity of the Flat Slabs Reinforced with Transverse Reinforcement

One of the most effective and common ways of an increasing the punching shear capacity of the flat slabs is using of shear reinforcement. An important limit during the design is the determination of the maximum punching shear resistance. The fact that the calculation of such an important value is still clearly empirical has met with criticism from many experts in recent years. Consequently, a new design model for the calculation of the maximum punching shear resistance, based on the critical shear crack theory was developed. The second generation of the Eurocode 2 (prEC2) introduces a more accurate and sophisticated design model for the determination of the upper limit of the punching shear capacity and considers several parameters. The main aim of this paper is to describe the new methodology of calculation of the maximum punching shear strength and the parameters that could influence it. The paper deals also with an assessment of the suitability of new model by comparing it with experimental results, that have been selected from a database of specimens that failed by punching at the level of VR,max.

Parametric modeling and deep learning-based forward and inverse design for acoustic metamaterial plates

Acoustic metamaterials (AMMs), with extraordinary physical properties, could be used to suppress the specific frequencies elastic waves by altering the structure artificially. However, as the complexity of AMMs structures continues to rise, classical design methods are time-consuming and high-computational. Therefore, there is a pressing need to explore more efficient and accurate design methods for AMMs. In this work, a deep learning algorithm-based forward and inverse design method for acoustic metamaterial plates (AMPs) is proposed. First, the initial samples of AMPs are created with parametric model and the bandgaps properties of the AMPs are generated by the finite element method. The dataset consists of different structure parameters and corresponding bandgap characteristics. Then, A neural network model is constructed by concatenating a pretraining network and an inverse design network. Through inputting the dataset to the concatenated network, the mapping relationship between the structural parameters and the bandgap characteristics of the AMPs can be explored. Ultimately, the trained network enables both forward designs, yielding bandgap characteristics for given structural parameters, and inverse design, deducing structural parameters for specific bandgap characteristics. The accuracy of the proposed design methodology is verified through illustrative examples. The results demonstrate that the trained neuron networks can effectively replace the complex physical mechanisms between the structural parameters and bandgap characteristics.

On the Thermally Induced Interfacial Behavior of Thin Two-Dimensional Hexagonal Quasicrystal Films with an Adhesive Layer

The mechanical response of a quasicrystal thin film is strongly affected by an adhesive layer along the interface. In this paper, a theoretical model is proposed to study a thin two-dimensional hexagonal quasicrystal film attached to a half-plane substrate with an adhesive layer, which undergoes a thermally induced deformation. A perfect non-slipping contact condition is assumed at the interface by adopting the membrane assumption. An analytical solution to the problem is obtained by constructing governing integral–differential equations for both single and multiple films in terms of interfacial shear stresses that are reduced to a linear algebraic system via the series expansion of Chebyshev polynomials. The solution is compared to that without adhesive layers, and the effects of the aspect ratio of films, material mismatch, and the adhesive layer, as well as the interaction between films, are discussed in detail. It is found that the adhesive layer can soften the localized stress concentration. This study is instructive to the accurate safety assessment and functional design of a quasicrystal film system.

Production-related effects on the adhesive bondline performance of structural adhesives joining dissimilar materials

Adhesive bonding is a commonly used technology for joining dissimilar materials. However, production-related effects on the performance of the bondline have to be considered for an accurate joint design. In case of an adhesive joint in an automotive multi-material body in white, these effects arise from the so-called mismatch in thermal expansion coefficients, which leads to distortions of the adhesive in an uncured state or even damage in the cured one. The distortion of the uncured adhesive in the normal direction is called the ‘viscous fingering effect’, which reduces the adhesively bonded cross section by changing the adhesive bondline's shape to thin ‘fingers’ and therefore influences the materials properties. To investigate the effect of viscous fingering on the modulus, strength and energy release rate, linear butt bonded specimens and Tapered Double Cantilever Beams (TDCB) with different elongations of the adhesive bondline in the viscous state are investigated. The results are used to parameterize a cohesive zone model (CZM) and perform numerical analysis of the TDCB specimen for validation and to build up a model of a dissimilar joint consisting of a steel hatprofile and an adhesively bonded aluminum panel subjected to thermal distortions.