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This paper aims to analyze deeply the effect of surface roughness conditions of the common interface of the two-layered riveted cantilever beams on their frictional damping during free lateral vibration at first mode. Here, the product, (µ × α), and damping ratio, ξ, are the parameters whose variations are analyzed in this investigation. For this, the influencing parameters considered are the natural frequency of vibration, f; the amplitude of initial excitation, y; and surface roughness value, Ra.

For experimentally evaluating logarithmic damping decrement, d, the frequency response function analyzer for the case of free lateral vibrations was used. Later, for evaluating the product, µ × α (where µ is the kinematic coefficient of friction and α is the dynamic slip ratio), and then, the damping ratio, ξ, the empirical relation suggested for logarithmic damping decrement, d, of riveted cantilever beams was used. After this, the full and reduced quadratic models of the product, µ × α, ξ, response surface methodology (RSM) with the help of Design Expert 11 software was used. Corresponding main effects plots, surface plots and prediction comparison plots were obtained to observe the variations of the product, µ × α, ξ for the variations of influencing parameters: f, y and Ra. Finally, a machine learning technique such as artificial neural networks (ANNs) using “nntool” present in MATLAB R13a software was used to predict the ξ for the different combinations of f, y and Ra.

The full and reduced quadratic regression models for the product, (µ × α) and the damping ratio, ξ of riveted cantilever beams for free lateral vibrations of the first mode in terms of the parameters: f, y and Ra were obtained. In addition, the main effects plots, surface plots and prediction comparison plots for the product, µ × α, ξ, with the corresponding experimental values of the product, µ × α, ξ, were obtained. Also, the execution of ANNs using MATLAB R13a software is proved to be the more accurate tool for the prediction of damping ratios in comparison to quadratic regression equations obtained from Design Expert 11 software. In the end, the assumption that the effect of surface roughness value on the product, (µ × α), and the damping ratio, ξ, is negligible is proved to be true using the main effects plots for the product, (µ × α) and ξ obtained from the Design Expert 11 software.

Obtaining the full and reduced quadratic regression equations for the product, (µ × α), and ξ of the two-layered riveted cantilever beams in terms of parameters: f, y and Ra was done. In addition, the conditions for the corresponding minimum and maximum values of the product, (µ × α), and ξ were obtained. Later, the main effects plots, surface plots and comparison plots of the predicted product, (µ × α), and ξ versus experimental product, (µ × α), and ξ were also obtained. Finally, the predicted values of the product, (µ × α), and ξ using the ANNs tool are observed to be the more accurate values in comparison to that obtained from RSM using the Design Expert 11 software.

The surface characteristics of thermally and chemically modified wood, such as surface roughness, surface free energy (SFE) and wettability, are important properties that influence further manufacturing processes such as gluing and coating. The aim of this paper was to determine the influence of the surface roughness of thermally and chemically modified teak wood on their SFE, wettability and bonding quality for water-based acrylic and solvent-based alkyd varnishes. In addition, durability against subterranean termites in the field of these modified teak woods was also investigated to give a valuable information for their further application.

The woods tested in this study were fast-growing teak woods that were prepared in untreated and treated with furfuryl alcohol (FA), glycerol maleic anhydride (GMA) and thermal. SFE values were calculated using the Rabel method. The wettability values were measured based on the contact angle between varnish liquids and wood surfaces using the sessile drop method, and the Shi and Gardner model model was used to evaluate the wettability of the varnishes on the wood surface. The bonding quality of the varnishes was measured using a cross-cut test based on ASTM 3359-17 standard. In addition, durability against subterranean termites in the field of these modified teak woods was also investigated according to ASTM D 1758-06.

The results showed that furfurylated and GMA-thermal 220°C improved the durability of teak wood against termites. The furfurylated teak wood had the roughest surface with an arithmetic average roughness (Ra) value of 15.65 µm before aging and 27.11 µm after aging. The GMA-thermal 220°C treated teak wood was the smoothest surface with Ra value of 6.44 µm before aging and 13.75 µm after aging. Untreated teak wood had the highest SFE value of 46.90 and 57.37 mJ/m² before and after aging, respectively. The K values of untreated and treated teak wood increased owing to the aging treatment. The K values for the water-based acrylic varnish were lower than that of the solvent-based alkyd varnish. The untreated teak wood with the highest SFE produced the highest bonding quality (grades 4–5) for both acrylic and alkyd varnishes. The solvent-based alkyd varnish was more wettable and generated better bonding quality than the water-based acrylic varnish.

The originality of this research work is that it provides evaluation values of the durability and SFE. The SFE value can be used to quantitatively determine the wettability of paint liquids on the surface of wood and its varnish bonding quality.

The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.

This study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.

This study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.

This research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.

This paper aims to investigate the effects on coatings performance in the epoxy silicone resin system owing to the existence of the different chain length of open-chain epoxy monomer. In this paper, [4-Methylphenyl-(4–(2-methylpropyl) phenyl)]iodonium as photoinitiator was added into epoxy silicone resin by ultraviolet (UV)-cured polymerization to investigate the effects on coatings performance owing to the existence of the different chain length of open-chain epoxy monomer.

A simple hydrosilylation reaction was used to synthesize epoxy-based silicone prepolymers by using hydrogen-terminated polydimethylsiloxane, 1,2-epoxy-5-hexene, 1,2-epoxy-7-octene and 1,2-epoxy-9-decene as precursors.

The results revealed that the glass transition temperatures (Tg) and hydrophobicity increased with the chain length of open-chain epoxy monomer in the UV curable epoxy-based silicone coatings, and these films had excellent heat resistance, hydrophobicity, antigraffiti and ink removal properties.

The cationic photocuring systems are not susceptible to the effect of oxygen inhibition. However, the limitation of cationic light curing process is that it requires a long curing time.

The coatings prepared via the UV curing approach can provide superior antismudge effects, and thus they are promising candidates for use in various industries, especially in fields such as antismudge coatings and antigraffiti coatings.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

This paper aims to solve the problems molybdenum disulfide (MoS₂) nanosheets suffer from inadequate dispersion stability and form a weak lubricating film on the friction surface, which severely limits their application as lubricant additives.

MoS₂/C₆₀ nanocomposites were prepared by synthesizing molybdenum disulfide (MoS₂) nanosheets on the surface of hydrochloric acid-activated fullerenes (C₆₀) by in situ hydrothermal method. The composition, structure and morphology of MoS₂/C₆₀ nanocomposites were characterized. Through the high-frequency reciprocating tribology test, its potential as a lubricant additive was evaluated.

MoS₂/C₆₀ nanocomposites that were prepared showed good dispersion in dioctyl sebacate (DOS). When 0.5 Wt.% MoS₂/C₆₀ was added, the friction reduction performance and wear resistance improved by 54.5% and 62.7%, respectively.

MoS₂/C₆₀ composite nanoparticles were prepared by in-situ formation of MoS₂ nanosheets on the surface of C₆₀ activated by HCl through hydrothermal method and were used as potential lubricating oil additives.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0321/

This study aims to determine the ink removal efficiency of papers with different recycling numbers and to examine some electrophotographic printing properties.

The base papers prepared according to the INGEDE 11p standard are subjected to six recycling stages (RS) under equal conditions. The physical-optical properties of the papers obtained at the end of each RS are measured and CMYK (cyan, magenta, yellow, key) color measurement scales are printed on each paper with electrophotographic printing. Color measurements of the printed papers are measured using the X-Rite eXact spectrophotometer, adhering to the ISO 13655:2017 standard. According to the measurement results of the optical properties, the ink removal efficiency of each recycling step is determined as a percentage (%) using some formulas.

As general, according to DEMLab and IERIC data, it is determined that the ink removal efficiency increased as the recycling repetition increased. In DEMf factor values, the highest deinking efficiency is obtained after the fourth RS. There is no significant difference between the printing properties of the samples.

It has been a matter of curiosity that papers lose their properties after how many RS. Many studies have been carried out on this subject and it has been presented by experimental methods that the printability properties of papers increase or decrease after which RS. This study can be a pioneer for future studies.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The development of new advanced materials, such as photopolymerizable resins for use in stereolithography (SLA) and Ti6Al4V manufacture via selective laser melting (SLM) processes, have gained significant attention in recent years. Their accuracy, multi-material capability and application in novel fields, such as implantology, biomedical, aviation and energy industries, underscore the growing importance of these materials. The purpose of this study is oriented toward the application of new advanced materials in stent manufacturing realized by 3D printing technologies.

The methodology for designing personalized medical devices, implies computed tomography (CT) or magnetic resonance (MR) techniques. By realizing segmentation, reverse engineering and deriving a 3D model of a blood vessel, a subsequent stent design is achieved. The tessellation process and 3D printing methods can then be used to produce these parts. In this context, the SLA technology, in close correlation with the new types of developed resins, has brought significant evolution, as demonstrated through the analyses that are realized in the research presented in this study. This study undertakes a comprehensive approach, establishing experimentally the characteristics of two new types of photopolymerizable resins (both undoped and doped with micro-ceramic powders), remarking their great accuracy for 3D modeling in die-casting techniques, especially in the production process of customized stents.

A series of analyses were conducted, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, mapping and roughness tests. Additionally, the structural integrity and molecular bonding of these resins were assessed by Fourier-transform infrared spectroscopy–attenuated total reflectance analysis. The research also explored the possibilities of using metallic alloys for producing the stents, comparing the direct manufacturing methods of stents’ struts by SLM technology using Ti6Al4V with stent models made from photopolymerizable resins using SLA. Furthermore, computer-aided engineering (CAE) simulations for two different stent struts were carried out, providing insights into the potential of using these materials and methods for realizing the production of stents.

This study covers advancements in materials and additive manufacturing methods but also approaches the use of CAE analysis, introducing in this way novel elements to the domain of customized stent manufacturing. The emerging applications of these resins, along with metallic alloys and 3D printing technologies, have brought significant contributions to the biomedical domain, as emphasized in this study. This study concludes by highlighting the current challenges and future research directions in the use of photopolymerizable resins and biocompatible metallic alloys, while also emphasizing the integration of artificial intelligence in the design process of customized stents by taking into consideration the 3D printing technologies that are used for producing these stents.

This study aims to optimize the preparation conditions and modify the nanofiltration (NF) membranes to prepare high-performance polysulfone/sulfonated polysulfone composite nanofiltration (PSF/SPSF-NF) membranes through interfacial polymerization.

Investigating the impacts of anhydrous piperazine (PIP) concentration, trimesoyl chloride (TMC) concentration and basement membrane type on NF membrane performance, the optimal membrane was prepared. In addition, nano-SiO₂ was added to the active separation layer to modify the NF membranes.

The comprehensive performance of PSF/SPSF-NF membranes was optimized when the concentration of PIP was 0.75 Wt.% and the concentration of TMC was 0.15 Wt.%, at which time the water flux was 66.1 L·m⁻²·h⁻¹ and the retention rate of Na₂SO₄ was 98.1%. The comprehensive performance of polysulfone/sulfonated polysulfone-SiO₂ nanofiltration (PSF/SPSF-SiO₂-NF) membranes was optimized when the blending ratio of nano-SiO₂ to PIP was 2:3, with a pure water flux of 81.9 L·m⁻²·h⁻¹ and a Na₂SO₄ retention rate of 95.9%. Compared to polysulfone nanofiltration (PSF-NF) membranes and PSF/SPSF-NF membranes, NF membranes with nano-SiO₂ increased the flux recovery rate by 22.9% and 8.7%.

PSF/SPSF-SiO2-NF membrane exhibits excellent antifouling properties.

There is currently no literature available on the preparation of NF membranes using polysulfone/sulfonated polysulfone (PSF/SPFS) as a substrate. This provides a method for modifying NF membranes, starting with the modification of the basement membrane and then modifying the active separation layer.