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The purpose of this paper is to illustrate the new technical demands and reliability challenges to printed circuit board (PCB) designs, materials and processes when the transmission frequency increases from Sub-6 GHz in previous generations to millimeter (mm) wave in fifth-generation (5G) communication technology.

The approach involves theoretical analysis and actual case study by various characterization techniques, such as a stereo microscope, metallographic microscope, scanning electron microscope, energy dispersive spectroscopy, focused ion beam, high-frequency structure simulator, stripline resonator and mechanical test.

To meet PCB signal integrity demands in mm-wave frequency bands, the improving proposals on copper profile, resin system, reinforcement fabric, filler, electromagnetic interference-reducing design, transmission line as well as via layout, surface treatment, drilling, desmear, laminating and electroplating were discussed. And the failure causes and effects of typical reliability issues, including complex permittivity fluctuation at different frequencies or environments, weakening of peel strength, conductive anodic filament, crack on microvias, the effect of solder joint void on signal transmission performance and soldering anomalies at ball grid array location on high-speed PCBs, were demonstrated.

The PCB reliability problem is the leading factor to cause failures of PCB assemblies concluded from statistical results on the failure cases sent to our laboratory. The PCB reliability level is very essential to guarantee the reliability of the entire equipment. In this paper, the summarized technical demands and reliability issues that are rarely reported in existing articles were discussed systematically with new perspectives, which will be very critical to identify potential reliability risks for PCB in 5G mm-wave applications and implement targeted improvements.

This study aims to investigate the surface modification on 20CrMnTi gear steel individually treated by diamond-like carbon films and nitride coatings.

For this purpose, the mechanical properties of a-C:H, ta-C and AlCrSiN coatings are characterized by nano-indentation and scratch tests. The friction and wear behaviors of these three coatings are evaluated by ball-on-disc tribological experiments under dry contact conditions.

The results show that the a-C:H coating has the highest coating-substrate adhesion strength (495 mN) and the smoothest surface (Ra is about 0.045 µm) compared with the other two coatings. The AlCrSiN coating shows the highest mean coefficient of friction (COF), whereas the ta-C coating exhibits the lowest one (steady at about 0.16). The carbon-based coatings possess excellent self-lubricating properties compared with nitride ceramic ones, which effectively reduce the COF by about 64%. The major failure mode of carbon-based coatings in dry contact is slight abrasive wear. The damage of AlCrSiN coating is mainly adhesive wear and abrasive wear.

It is suggested that the carbon-based film can effectively improve the friction-reducing and wear resistance performance of the gear steel surface, which has a promising application prospect in the mechanical transmission field.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0129/

The purpose of this study is to develop a additive manufacturing (AM) process for the fabrication of ionic polymer–metal composite (IPMC) devices with complex designs that would be time-consuming to replicate using conventional manual methods. These IPMC devices have considerable potential in electroactive polymers (EAPs) and soft actuators.

This paper presents a novel three–dimensional (3D) AM technique to develop IPMCs. Digital light processing (DLP) fabrication of soft EAPs was undertaken using a vat-based AM method, followed by deposition of cost-effective outer silver electrodes.

DLP-fabricated devices were compared to conventional Nafion™-117 devices. DLP layer-by-layer fabrication of these devices allowed for good resolution for a range of printed objects. Electrical actuation of the DLP-produced IPMCs showed tip displacements of up to 3 mm, and greater actuation was seen in the presence of lithium rather than magnesium cations. The IMPCs showed good ion exchange capacities, while electrochemical analysis showed the reversible formation and removal of AgCl layers in addition to ion movement.

The AM of these devices allows for rapid prototyping as well as potential use in the development of multiple degrees of freedom actuators and devices.

An original resin formulation was developed for DLP 3D printing. This formula is chemically distinct from the conventional Nafion™-117 membranes that can be purchased. Additionally, this method allows for the manufacture of complex objects that would be difficult to machine by hand. These findings are of value to both the fields of polymer chemistry and AM.

This study aims to investigate the effect of V doping on the microstructure, chemical stability, mechanical and vacuum tribological behavior of sputtered MoS₂ coatings.

The MoS₂-V coatings are fabricated via tuning V target current by magnetron sputtering technique. The structural characteristic and elemental content of the coatings are measured by field emission scanning electron microscopy, X-ray diffractometer, electron probe X-ray micro-analyzer, Raman, X-ray photoelectron spectroscopy, high resolution transmission electron microscope and energy dispersive spectrometer. The hardness of the deposited coatings are tested by a nanoindentation technique. The vacuum tribological properties of MoS₂-V coatings are studied by a ball-on-disc tribometer.

Introducing V into the MoS₂ coatings results in a more compact microstructure. The hardness of the coatings increases with the doping of V. The MoS₂-V coating deposited at a current of 0.2 A obtains the lowest friction coefficient (0.043) under vacuum. As the amount of V doping increases, the wear rate of the coating decreases first and then increases, among which the coating deposited at a current of 0.5 A has the lowest wear rate of 2.2 × 10–6 mm³/N·m.

This work elucidates the role of V doping on the lubrication mechanism of MoS₂ coatings in a vacuum environment, and the MoS₂-V coating is expected to be applied as a solid lubricant in space environment.

The metallic alloys and their components fabricated via laser powder bed fusion (LPBF) suffer from the microvoids formed inevitably due to the extreme solidification rate during fabrication, which are impossible to be removed by heat treatment. This paper aims to remove those microvoids in as-built AlSi10Mg alloys by hot forging and enhance their mechanical properties.

AlSi10Mg samples were built using prealloyed powder with a set of optimized LPBF parameters, viz. 350 W of laser power, 1,170 mm/s of scan speed, 50 µm of layer thickness and 0.24 mm of hatch spacing. As-built samples were preheated to 430°C followed by immediate pressing with two different thickness reductions of 10% and 35%. The effect of hot forging on the microstructure was analyzed by means of X-ray diffraction, scanning electron microscopy, electron backscattered diffraction and transmission electron microscopy. Tensile tests were performed to reveal the effect of hot forging on the mechanical properties.

By using hot forging, the large number of microvoids in both as-built and post heat-treated samples were mostly healed. Moreover, the Si particles were finer in forged condition (∼150 nm) compared with those in heat-treated condition (∼300 nm). Tensile tests showed that compared with heat treatment, the hot forging process could noticeably increase tensile strength at no expense of ductility. Consequently, the toughness (integration of tensile stress and strain) of forged alloy increased by ∼86% and ∼24% compared with as-built and heat-treated alloys, respectively.

Hot forging can effectively remove the inevitable microvoids in metals fabricated via LPBF, which is beneficial to the mechanical properties. These findings are inspiring for the evolution of the LPBF technique to eliminate the microvoids and boost the mechanical properties of metals fabricated via LPBF.

AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity.

The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity.

The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm³ and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm³. The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains.

The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications.

To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

This study aims to discuss the influences of surface severe plastic deformation (S²PD) on the electrochemical corrosion, pitting corrosion, intergranular corrosion, stress corrosion cracking of aluminum (Al) alloys and attempt to correlate the microstructural/compositional changes with the performances.

This study provides a novel gradient design of structure/composition caused by S²PD for the purpose of enhancing Al alloys’ corrosion resistance.

S²PD has a significant effect on corrosion behavior of Al alloys through tuning the grain size, residual stress, composition, grain boundary phase and second phase particle distribution.

Although Al alloys are known to form a protective Al₂O₃ film, corrosion is a major challenge for the longevity of Al structures across numerous industries, especially for the infrastructures made of high-strength Al alloys. Traditional strategies of improving corrosion resistance of Al alloys heavily relied on alloying and coatings. In this review, gradient design of structure/composition caused by S²PD provides a novel strategy for corrosion protection of Al alloys, especially in the enhancement of localized corrosion resistance.

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.

This study aims to outline the current challenges in ultrasonic additive manufacturing (AM). AM has revolutionized manufacturing and offers possible solutions when conventional techniques reach technological boundaries. Ultrasonic additive manufacturing (UAM) uses mechanical vibrations to join similar or dissimilar metals in three-dimensional assemblies. This hybrid fabrication method got attention due to minimum scrap and near-net-shape products.

This paper reviews significant UAM areas in process parameters such as pressure force, amplitude, weld speed and temperature. These process parameters used in different studies by researchers are compared and presented in tabular form. UAM process improvements and understanding of microstructures have been reported. This review paper also enlightens current challenges in the UAM process, process improvement methods such as heat treatment methods, foil-to-foil overlap and sonotrode surface roughness to increase the bond quality of welded parts.

Results showed that UAM could solve various problems and produce net shape products. It is concluded that process parameters such as pressure, weld speed, amplitude and temperature greatly influence weld quality by UAM. Post-weld heat treatment methods have been recommended to optimize the mechanical strength of ultrasonically welded joints process parameters. It has been found that the tension force is vital for the deformation of the pre-machined structures and for the elongation of the foil during UAM bonding. It is recommended to critically investigate the mechanical properties of welded parts with standard test procedures.

This study compiles relevant research and findings in UAM. The recent progress in UAM is presented in terms of material type, process parameters and process improvement, along with key findings of the particular investigation. The original contribution of this paper is to identify the research gaps in the process parameters of ultrasonic consolidation.

Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.

In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.

The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.

This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.