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This research introduces a novel methodology for mitigating defects in sheet metal forming processes. The proposed approach employs a segmented and variable blank holder force (S-VBHF) trajectory, adjusting the blank holder force (BHF) during the forming cycle, enhancing formability, and reducing failure, wrinkling and springback defects. Optimal process parameters, including the S-VBHF, friction coefficient and drawbead restraining force (DBRF), were determined through a systematic methodology integrating deep neural network, genetic algorithm and Monte Carlo simulation (DNN-GA-MCS) techniques. The design constraint, defined as sheet failure during the forming process, was quantitatively evaluated using the forming limit diagram (FLD) to ensure rigorous assessment. The proposed methodology was validated through numerical simulations using a cylindrical cup provided by NUMISHEET 2011 (BM1) as test samples. The simulation results demonstrated a significant improvement in the formed sheet quality, characterized by reductions of 8.33%, 10.81% and 5.88% in failure, wrinkling and springback defects, respectively. These findings underscore the potential of the proposed approach in enhancing the quality of sheet metal forming processes and mitigating defects.

Carbon fiber-reinforced polymers (CFRP) have been actively employed as lightweight materials; yet, evaluating the material’s reliability under multi-axis stress states is still challenging owing to their anisotropic nature. This paper investigates the fatigue failures of short carbon-fiber reinforced polyamide-6 (PA6-CF) and polypropylene (PP-CF) by analyzing the anisotropic behavior induced by the fiber orientation. The static and fatigue experiment and numerical analysis results of a one-way coupled injection molding structure have been obtained to develop the fatigue life prediction methodology. The maximum deviation between the experimental and calculated tensile results is 3.16%, indicating the accuracy of the numerical analysis model. The obtained data were utilized to develop the semi-empirical model based on the energy function, consisting of stress, strain, and triaxiality terms. Fiber breakage and matrix cracking occurred simultaneously during the fatigue fracture of PA6-CF. The PP-CF fiber was pulled out after matrix cracking due to weak interfacial bonding between the matrix and fiber. The reliability of the proposed model has been confirmed with high correlation coefficients of 98.1% and 97.9% for PA6-CF and PP-CF, respectively. In addition, the prediction percentage errors of the verification set for each material were 38.6% and 14.5%, respectively. Although the results of the verification specimen collected directly from the cross-member were included, the percentage error of PA6-CF was still relatively low at 38.6%. In conclusion, the developed model can predict the fatigue life of CFRPs, considering anisotropy and multi-axial stress states.

The design of a recycled-carbon fiber–reinforced plastic/metal hybrid (PMH) engine cradle is realized via combined optimization techniques. Topology optimization (TO) and free-size optimization (FSO) techniques are exploited in the design process to find the optimal rib configuration and location of metal inserts. To analyze the obtained design in terms of functionality, a material exchange technique that accounts for fiber orientation elementwise is employed. The robust design procedure is shown to be effective in developing PMH parts while considering injection molding (IM) restrictions from the early design stages. The PMH model provides a good balance between functionality and IM suitability, with a 36% weight reduction from the steel baseline model. The results contribute to the efficient multimaterial design of PMH components in the auto industry, creating light and strong structures produced by fast manufacturing processes and low-cost carbon fibers.

In this paper, the failure mechanism of metal skin (MS) and long carbon fibers reinforced core (CFRP) structures is investigated by means of tensile and bending tests, scanning electron microscope ...

This study provides a preform design approach for uniform strain distribution in forging products based on a convolutional neural network (CNN). The appropriate preform design prevents underfill problems by improving the material flow inside forging dies and achieving a uniform strain distribution in forging products. The forging deformation process and mechanical properties are improved with a uniform strain distribution. The forging and strain distribution results are analyzed through rigid–plastic finite element forging simulations with different initial geometries. The simulation data are fed into the CNN model as an input array, from which the geometric characteristics are extracted by convolution operations with filters (weight array). The extracted features are linked to the considered initial shapes, which are input into the CNN model as an output array. The presented model derives the preform shape for a target forging with uniform strain distributions using the training weights. According to the training database, the proposed design method can be applied to different forging geometries without any iterations. By creating a number of low-level CNN (LC) models based on the training data, the efficiency of the preform design can be improved. The best preform among the derived preform candidates is chosen by comparing the forging results. Compared with previous studies using the same design criteria, the proposed model predicted the preform with a strain distribution improved by 16.3–38.4%.

In this study, the temperature and thermal stress fields of an internal combustion hot blast stove were calculated and analysed. Turbulent, species transport, chemical reaction, radiation, and porous media models were implemented in a computational fluid dynamics model. Thermal boundary conditions on the structure of the hot blast stove were calculated based on the analytic adiabatic Y-plus method. A method to interpolate the thermal boundary conditions to a finite element mesh was developed, and the boundary conditions were mapped through the proposed method. In the on-gas period, the vortex was generated in the dome, and it made the variation of the temperature field in the checker chamber. The maximum temperature of the flue gas reached 1841 K in the on-gas period. In the on-blast period, the flow was considerably even compared to the on-gas period, and the average blast temperature reached 1345 K. The outer region of the checker chamber is shown to be continuously exposed to a higher temperature, which makes the region the main domain in managing the deterioration of the refractory linings. The shell temperature did not change during the operation due to the lower thermal diffusivity of the refractory linings, where the inner surface of the refractory had a maximum temperature change from 1441 K to 1659 K. The maximum temperature of the shell was 418.4 K at the conical region of the checker chamber side. The conical region had the higher maximum and middle principal thermal stresses due to the presence of a large temperature gradient around the conical region, where the largest maximum and middle principal stresses were 300.6 MPa and 192.0 MPa, respectively. The conical region was found to be a significant area of interest where it had a higher temperature and thermal stress.

As the interest in short-fiber reinforced polymer (SFRP) composites manufactured by injection molding increases, predicting the failure of SFRP structures becomes important. This study aims to systemize the prediction of failure of SFRP through mechanical property evaluation considering the anisotropy and strain rate dependency. To characterize the mechanical properties of polyamide-6 reinforced with carbon fiber of a weight fraction of 20% (PA6-20CF), tensile and compressive experiments were conducted with different load-applying directions and strain rates. Additionally, the results were discussed in detail by SEM image analysis of the fracture faces of the specimen. FE simulations based on the experimental condition were constructed, and the numerical model coefficients were derived through comparison with experimental results. The coefficients obtained were verified by bending tests of the specimens manufactured from composite cross members fabricated by injection molding. Predicting under static and high strain rate conditions, small errors of about 9.6% and 9.3% were shown, respectively. As a result, it proves that explained procedures allow for better failure prediction and for contribution to the systematization of structural design.

Sheets’ buckling instability, also known as oil canning, is an issue that characterizes the resistance to denting in thin metal panels. The oil canning phenomenon is characterized by a depression in the metal sheet, caused by a local buckling, which is a critical design issue for aesthetic parts, such as automotive outer panels. Predicting the buckling instability during the design stage is not straightforward since the shape of the component might change several times before the part is sent to production and can actually be tested. To overcome this issue, this research presents a robust prediction model based on the convolutional neural network (CNN) to estimate the buckling instability of automotive sheet metal panels, based on the major, minor, and Gaussian surface curvatures. The training dataset for the CNN model was generated by implementing finite element analysis (FEA) of the outer panels of various commercial vehicles, for a total of twenty panels, and by considering different indentation locations on each panel. From the implemented simulation models the load-stroke curves were exported and utilized to determine the presence, or absence, of buckling instability and to determine its magnitude. Moreover, from the computer aided design (CAD) files of the relevant panels, the three considered curvatures on the tested indentation points were acquired as well. All the positions considered in the FEA analyses were backed up by industrial experiments on the relevant panels in their assembled position, allowing to validate their reliability. The combined correlation of curvatures and load-displacement curves allowed correlating the geometrical features that create the conditions for buckling instability to arise and was utilized to train the CNN algorithm, defined considering 13 convolution layers and 5 pooling layers. The trained CNN model was applied to another automotive frame, not used in the training process, and the prediction results were compared with experimental indentation tests. The overall accuracy of the CNN model was calculated to be 90.1%, representing the reliability of the proposed algorithm of predicting the severity of the buckling instability for automotive sheet metal panels.

During forming process of the bimetallic pipe, the residual contact stress between the pipes is one of the most important technical issues. In recent study, the bimetallic pipe was manufactured by internal spinning forming technology and analyzed via different theories of elasto-plastic mechanics. It was assumed that the maximum contact stresses generated between the pipes when the inside surface of the outer pipe expanded to the elasto-plastic interface position. In addition, the maximum value of the residual contact stress occurred when the spinning pressure unloaded. The theoretical model of the elasto-plastic zone and the elasto-plastic boundary position of the outer pipe during the spinning process was proposed. The validity of the proposed model was verified by finite element simulation and experimentation, and the experimental results are in good agreement with those obtained from simulation. The obtained test results exhibit that the new technology is feasible and can be provided a theoretical reference for industrial applications.

The research presented in this paper deals with the development of an integrated numerical model for the estimation of the incremental surface wear and damage accumulation in linear slide rails. The target is the estimation of the progressive increment of the end-point deflection of the last member of the slide rail during the operational lifetime. The surface abrasion is accounted for by utilizing a modified Archard equation with the aim of estimating the amount of wear along the vertical direction of the slide rails members. In addition to that, the Lemaitre damage model is utilized for the estimation rolling contact fatigue (pitting), considering the total strain and not only the plastic strain. Experiments have been carried out on a small-scale slide rail testing machine in order to define the wear increment on the slide rail inner groove for increasing number of cycles and, accordingly, estimate the modified Archard model constants. In addition to that, the wear parameters for the Lemaitre damage model have been inversely calibrated from the results of tensile tests. A numerical model has been implemented in ABAQUS/Explicit and an external geometry-update subroutine has been employed to update the geometry of the slide rail groove for increasing number of cycles as a consequence of wear and roll contact fatigue. The comparison between numerical and experimental results on real rails have shown a maximum deviation equal to 12.9%, supporting the reliability of the proposed approach.

Energy prediction and starvation have become an essential part of process planning for the XXI century manufacturing industry due to cost-saving policies and environmental regulations. To this aim, the research presented in this paper details how machine learning-based algorithms can be an effective way to predict and minimize the energy consumptions in the widely spread radial-axial ring rolling (RARR) process. To analyze this bulk metal forming process, 380 numerical simulations have been developed using the commercial SW Simufact Forming 15 and considering three largely utilized materials, the 42CrMo4 steel, the IN 718 superalloy, and the AA6082 aluminum alloy. To create the database for both multi-variable regression and machine learning models, ring outer diameters ranging from 650 mm to 2000 mm and various process conditions including different sets of tool speeds and initial temperatures have been considered. For the case of the multi-variable regression model, to account for all the cross-influences between all the parameters, a second-order function including 26 parameters has been developed, resulting in a reasonable average accuracy (94 %) but also in an impractical huge equation. On the other hand, the machine learning model based on the Gradient Boosting (GB) approach allows obtaining a similar accuracy (96 %) but its compact form allows a more practical utilization and its training can be expanded almost indefinitely, by adding more results from both numerical simulations and experiments. The proposed approach allows to quickly and precisely predict the energy consumption in the RARR process and can be extended to other manufacturing processes.

In the present research work, a finite element model of the electro-resistance welding pipe forming process chain is developed using the ABAQUS/Explicit software. The forming process, which is composed of 22 tandem roll stations, has been fully modeled in the developed finite element simulation. In order to account for the Bauschinger effect on the pipe material properties as a consequence of the loading and the unloading during the process, a non-linear kinematic hardening model has been utilized in all the proposed finite element simulation models. The constants for the non-linear kinematic hardening model were estimated by means of cyclic experiments on the K55 steel pipe material. In order to properly simulate the electric arc welding (electro-resistance welding) operation, the ABAQUS welding interface has been utilized to account for the joining between the two edges of the formed pipe as well as to assess the influence of the welding-induced temperature field on the residual stresses on the pipe material. The sizing operation, which is the final station of the electro-resistance welding process, has been also accounted in the developed finite element method model and is composed of six tandem rolls. To export and import the results between two different modules, a mapping strategy has been utilized and allowed exporting the element results, in terms of stress, strain, and temperature, and importing them into the following simulation module. Finally, in order to estimate the influence of each process station on the yield strength of the material, a finite element simple tension test simulation has been implemented in ABAQUS/Static, mapping the results of each station on the tensile specimen. This mapping operation allowed to estimate the yield stress of the material after each of the three process stations, a consequence of the residual stresses present in the material, and has been carried out on eight circumferential locations around the pipe, evenly spaced with a 22.5° angle. The model has been validated by comparing the geometrical results, in terms of average pipe diameter and thickness, obtained from the finite element model with those of the relevant industrial production, showing deviations equal to 1.25% and 1.35% (forming) and 1.29% and 1.43% (sizing), respectively, proving the reliability of the proposed process chain analysis simulation. The results will show how the process-induced residual stresses arising on the pipe material make the material yield strength to vary from station to station as well as having different values along the circumferential direction of the pipe.

A buckling of the thin plate after stamping process is called an oil canning phenomenon. This issue impacts the shape and induces vibration and noise effects on the outer surface of the stamped parts such as the automobile and white-good products. To answer the call for reducing the oil canning, the results of a number of experimental procedures carried out on two largely utilized steel, namely the AISI-1008 and AISI-1020 steel alloys are used to define an empirical-numerical model for the prediction of oil canning in the design stage. The results of the tests on those two steel are utilized for the definition of the process parameters which can be directly linked to the rise of the oil canning phenomenon on metal sheets. Accordingly, in this paper, the thickness, the curvature radius, the length, and the width of the panel are considered the most influencing parameters in the arising of oil canning issue. A stamping process is carried out, and an indentation test is applied to check the oil canning on the stamped sheets. In addition, the measurements on assigned points on the stamped panel for thickness and curvature radius are carried out. Afterward, based on the experimental results, numerical simulations have been implemented in LS-Dyna and the results are utilized for the definition of an oil canning index (OCI) model that is used to define the higher or lower risk of oil canning occurrence when a certain combination of material and process parameters are utilized. In the proposed model, an OCI close to zero defines a condition where oil canning is unlikely to happen whereas an OCI close to one or higher stands for high risk of oil canning to arise. The obtained oil canning values are correlated with the curvature radius results which found to be the most considerable effect on the oil canning occurrence.

In the research presented in this paper, a slip line-based model is proposed for the estimation of both radial and axial force in the radial-axial ring rolling (RARR) process. Based on the shape of the contact arcs between ring and tools in the two deformation gaps present in the ring rolling process, a recursive algorithm for the calculation of the two slip line fields starting from the two pairs of opposite tools is derived and implemented in a commercial spreadsheet software (MS Excel). By considering the stress boundary conditions applied to the portion of material undergoing the deformation, both for the radial and axial deformation gaps, the pressure factors those make the two slip line fields starting from the two opposite tools to intersect are calculated and utilized for the estimation of radial and axial forces, for each round of the process. The developed model has been validated by cross-comparing its results with those of laboratory experiment and numerical simulation. For the validation study case, the average deviations, in comparison to the experimental results, are calculated in 1.86% and 4.55% for the slip line force model whereas in 6.86% and 0.88% for the numerical simulation, for the radial and axial forces respectively. The proposed slip line model has been also utilized for the estimation of radial and axial forming forces of nine different study cases of flat rings having the outer diameter ranging from 800 mm to 2000 mm, observing a maximum deviation, in comparison to the relevant FEM simulation, of 4.92% (radial force) and 5.88% (axial force). The developed slip line force model allows estimating almost in real time and with a reasonable accuracy the process forces and, for this reason, it may be of interest for both industrial and academic researchers dealing with the set-up and control of the radial-axial ring rolling process.

A novel approach is proposed for evaluating equi-biaxial residual stresses (RS) from residual imprint displacement fields after spherical indentation. A unique region, where displacements are free from frictional effect, is first identified based on finite element analysis. Regression functions are proposed to relate imprint data with RS for a broad range of material properties. Digital image correlation (DIC) is employed for measuring the radial displacement fields. Experimental comparison of RS values from the present method and strain gauge showed the (maximum, mean) errors of only (10, 5) %. As the determined RS are accurate and reliable, the methodology can find more industrial applications compared with prior techniques.

A continuum damage model (CDM) based on local hardness in welds is developed to predict fracture of welded tubes. A regression function of hardness distribution in the welds is proposed to establish hardness continuously through hardness mapping method in the finite element model. A constitutive model of the weld zone is determined by the rule of mixture, which estimates the flow stress of the welds from the hardness ratio. Coefficients of the flow stress model in the welds are validated by comparing finite element analysis (FEA) solution and experimental load - displacement data of tensile specimens. Softening after necking is considered by Lemaitre CDM. A damage parameter is newly given as a function of hardness ratio varying with angular position. Coefficients of damage parameter are obtained by the inverse FEA of tensile and flaring tests. The proposed method finely predicts the ductile fracture of welded tubes by considering local plastic properties and damage evolution.

Surface wear and spalling phenomena are the most important factors affecting roller bearing performance and operational lifetime, in terms of number of cycles the bearing can operate. Although bearing life has been traditionally defined by the onset of spalling, as specified by international standard ISO 281, this definition does not always reflect the real operational conditions, since bearing wear can also represent a failure criterion when excessive run-out occurs. In such cases, bearing life needs to be predicted according to the amount of bearing run-out resulting from wear due to load and number of cycles. In the current study, the wear phenomenon has been modeled using the Archard wear and Lemaitre damage models, both included in a numerical simulation of a roller bearing assembly. Wear and damage models, along with the results of accelerated life experiments, have been used to develop a reliable method enabling bearing life prediction with a minimal number of material characterization tests. Accelerated life testing experiments on real bearings have been performed to confirm the reliability of the methodology, showing good correlation with numerical simulation results and proving that the developed model can be utilized for prediction of the lifetime of bearings.

Surface defects, known as pits, due to intervention of impurities are difficult to control during the production of sheet metals for atomic fuel spacer grids. These surface defects can increase the fracture risk during sheet metal forming (SMF) processes and lead to failure due to cracking. In this research, an effect of pits on the formability of zircaloy-4 sheets in two-step stamping process is numerically investigated. Experimentally measured pits with averaged sizes are introduced into finite element (FE) models to investigate the effect pit size and the location on plastic strain distribution around weaker sections. By performing one-step and two-step stamping analyses, strains at the pit locations are compared with an experimental forming limit curve (FLC). Although the two-step stamping model reduces the susceptibility to cracking of the atomic fuel spacer grids over one-step stamping model, this study reveals that the fracture risk is higher, if a pit with relatively small radius and deep depth is located near the edge of the metal sheets. Therefore, it is recommended to identify the pits with critical size around the weaker sections before the SMF processes.

The purpose of this work was to obtain the accurate constants in ductile fracture criteria based on the M-K theory, which determined the condition of convergence in numerical simulations and predicted the ductile fracture during the tube free bulging process. In this paper, the equivalent stress was analyzed based on the M-K theory, and the constitutive equation of the tube free bulging was established. The ductile fracture criterion constants were obtained by the integral on the basis of the Freudenthal, Normalized C&L and Oyane theories. The comparison between the experimental and the simulation results regarding the free bulging, indicated that the Oyane ductile fracture criterion was more precise than the other criteria by the perimeter increase trend, the burst locations of the risky area and the crack length. The burst defects prediction became significantly accurate by the Oyane ductile fracture criterion utilization based on the M-K theory.

Acrylonitrile-Butadiene-Styrene (ABS), also known as a thermoplastic polymer, is extensively utilized for manufacturing home appliances products as it possess impressive mechanical properties, such as, resistance and toughness. However, the aforementioned properties are affected by operating temperature and atmosphere humidity due to the viscoelasticity property of an ABS polymer material. Moreover, the prediction of optimum working conditions are the little challenging task as it influences the final properties of product. This present study aims to develop the finite element (FE) models for predicting the creep behavior of an ABS polymeric material. In addition, the material constants, which represent the creep properties of an ABS polymer material, were predicted with the help of an interpolation function. Furthermore, a comparative study has been made with experiment and simulation results to verify the accuracy of developed FE model. The results showed that the predicted value from FE model could agree well with experimental data as well it can replicate the actual creep behavior flawlessly.

In this paper, the influence of the stress triaxiality on the failure strain of short fiber-reinforced composite, manufactured by the injection molding process, is investigated by tensile tests on notched ASTM-D638 specimen and ASTM E1820-01 U-Shape specimens made of a polyamide-6 thermoplastic polymer reinforced with 20% of short carbon fibers (PA6-20CF). From the combined experimental and FEA investigations, a function correlating the failure strain and the stress triaxiality has been developed can be utilized as a failure criterion for complex components made of short fibers reinforced polymers during the design stage, where no direct testing is a viable option.

This study proposes a method for evaluating elastic limit and strain hardening exponent by a single spherical indentation test. The method extracts the plastic properties from radial displacement fields and vertical profile of a residual imprint. Based on finite element (FE) and regression analyses, an evaluation algorithm is developed by relating the imprint data with plastic properties. Digital image correlation (DIC) method is used for measurements of the radial displacement fields. The errors of the properties determined with our method were less than 12 % for elastic limit stress, 0.6 for strain hardening exponent compared with those from tensile tests. The sensitivity of the inverse analysis algorithm is also shown to be acceptable.

In this paper, multiaxial fatigue experiments on a hyperelastic rubber-like material made of polychloroprene rubber (CR) reinforced with tungsten nano-particles have been carried out on notched specimens and hourglass specimens, utilized for limiting dome height fatigue tests. Based on the uniaxial (Choi et al., 2020) and multiaxial fatigue experiments, a semi-empirical e-N fatigue model is proposed, allows accounting for both material anisotropy and complex stress states, showing an average error of 20.7%. Furthermore, six machine learning models have been employed for the fatigue life prediction and shown that the Deep Neural Network is the most accurate, with an average error equal to 14.3%.

A new method is proposed to obtain the coefficients of plastic constitutive laws of weld metal via tensile test and hardness test. In this method, a general expression between hardness ratio and flow stress model coefficients is established. The predicted flow stresses for the differential areas are integrated over the entire section of tensile specimen to predict the tensile load. The optimal regression coefficients that minimize the difference between the predicted and experimental loads are derived using the Nelder-Mead simplex direction search algorithm. The constraint for the diffuse necking strain is applied in the convergence procedure to avoid the ‘mystical’ material property shown as non-unique solution. The material constants obtained from the proposed method are independent of the width of tensile specimen. The difference of tensile loads between finite element analysis results using the determined material constants and the experiment was less than 8 %. As the derived material constants were shown to be consistent and reliable, the methodology can be applied to general line welded parts.

Ductile fracture prediction is often considered as a challenging task in complex stress state applications, such as metal joining process. A systematic method for predicting the joint strength in the hot shear joining process is presented in this paper by considering two different ductile damage models, namely Johnson-Cook and modified Lemaitre damage models. Since the crack formation in metalworking is considered to be a combination of both load conditions and metallurgical properties, the microstructure evolution is taken into account by characterizing the considered material, AISI-1045, at different temperatures and for different strain rates and by testing different specimen shapes, namely round bar, joint bar, flat and notched specimens. The derived material properties have been inversely calibrated in order to be used in the developed numerical model. A shear joining process simulation has been implemented in DEFORM and have been validate with shear joining experiments carried out at 950°C, allowing to validate the accuracy of the proposed model in simulating the hot shear joining, both in terms of joining strength and identification of the damage locations. Thanks to a precise material characterization, and by utilizing the proposed numerical model, both the joining tool design and the process condition can be studied and improved.

The present research work focusses on both the material characterization and the preliminary test applications for hybrid structures made of high strength steel skin and sheet molding compound (SMC) core, aimed for the production of lightweight automotive body frames. A full material characterization has been carried out in order to determine the properties of the single materials, high strength steel and sheet molding compound. In addition, laminate specimens made of steel skin and SMC core have been manufactured utilizing the compression molding process and the relevant stress-strain behavior have been tested. By means of shear test, the shear strength resistance of the adhesive, utilized for the cohesion between these two materials, have been also characterized. In order to test the proposed hybrid material for the application in the automotive industry, the case of a commercial vehicle B-pillar component have been considered and tested by means of FEM analysis. By simulating the severe load conditions to which the B-pillar has to stand, the numerical analysis implemented in ANSYS will show how the developed material is able to withstand the same load conditions of the original full-steel one while granting a considerable reduction of weight.

Thin-multilayer Ti/Al laminates were prepared by one-step explosive bonding method to investigate the interface bonding and mechanical properties owing to their potential application in aerospace and auto industry. It was found that Ti/Al laminates prepared at the detonation velocity of 2100 m/s exhibited superior surface quality without any defects in comparison with other detonation velocities owing to the optimum explosive pressure and impact velocity. The interface morphologies and element distribution of Ti/Al laminates prepared at the detonation velocity of 2100 m/s were characterized by means of SEM and EDS. The results indicated that the linear and wavy bonding interface coexisted and element diffusion occurred in the interfacial zone. Furthermore, thin-multilayer Ti/Al laminates possessed high microhardness, excellent interlaminar shear and tensile properties.