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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.

The purpose of this article is to prepare graphene/polyimide composite materials for use as bearing cage materials, improving the friction and wear performance of bearing cages.

The oil absorption and discharge tests were conducted to evaluate the oil content properties of the materials, while the mechanical properties were analyzed through cross-sectional morphology examination. Investigation into the tribological behavior and wear mechanisms encompassed characterization and analysis of wear trace morphology in PPI-based materials. Consequently, the influence of varied graphene nanoplatelets (GN) concentrations on the oil content, mechanical and tribological properties of PPI-based materials was elucidated.

The composites exhibit excellent oil-containing properties due to the increased porosity of PPI-GN composites. The robust formation of covalent bonds between GN and PPI amplifies the adhesive potency of the PPI-GN composites, thereby inducing a substantial enhancement in impact strength. Notably, the PPI-GN composites showed enhanced lubrication properties compared to PPI, which was particularly evident at a GN content of 0.5 Wt.%, as evidenced by the minimization of the average coefficient of friction and the width of the abrasion marks.

This paper includes implications for elucidating the wear mechanism of the polyimide composites under frictional wear conditions and then to guide the optimization of oil content and tribological properties of polyimide bearing cage materials.

In this paper, homogeneously dispersed PPI-GN composites were effectively synthesized by introducing GN into a polyimide matrix through in situ polymerization, and the lubrication mechanism of the PPI composites was compared with that of the PPI-GN composites to illustrate the composites’ superiority.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0415

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/

Material extrusion-based additive manufacturing is a prominent manufacturing technique to fabricate complex geometrical three-dimensional (3D) parts. Despite the indisputable advantages of material extrusion-based technique, the poor surface and subsurface integrity hinder the industrial application of this technology. The purpose of this study is introducing the hot air jet treatment (HAJ) technique for surface treatment of additive manufactured parts.

In the presented research, novel theoretical formulation and finite element models are developed to study and model the polishing mechanism of printed parts surface through the HAJ technique. The model correlates reflow material volume, layer width and layer height. The reflow material volume is a function of treatment temperature, treatment velocity and HAJ velocity. The values of reflow material volume are obtained through the finite element modeling model due to the complexity of the interactions between thermal and mechanical phenomena. The theoretical model presumptions are validated through experiments, and the results show that the treatment parameters have a significant impact on the surface characteristics, hardness and dimensional variations of the treated surface.

The results demonstrate that the average value of error between the calculated theoretical results and experimental results is 14.3%. Meanwhile, the 3D plots of Ra and Rq revealed that the maximum values of Ra and Rq reduction percentages at 255°C, 270°C, 285°C and 300°C treatment temperatures are (35.9%, 33.9%), (77.6%,76.4%), (94%, 93.8%) and (85.1%, 84%), respectively. The scanning electron microscope results illustrate three different treatment zones and the treatment-induced and manufacturing-induced entrapped air relief phenomenon. The measured results of hardness variation percentages and dimensional deviation percentages at different regimes are (8.33%, 0.19%), (10.55%, 0.31%) and (−0.27%, 0.34%), respectively.

While some studies have investigated the effect of the HAJ process on the structural integrity of manufactured items, there is a dearth of research on the underlying treatment mechanism, the integrity of the treated surface and the subsurface characteristics of the treated surface.

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.

Stereolithography (SLA) additive manufacturing (AM) technique has enabled the production of inconspicuous and aesthetically pleasing orthodontics that are also hygienic. However, the staircase effect poses a challenge to the application of invisible orthodontics in the dental industry. The purpose of this study is to implement chemical postprocessing technique by using isopropyl alcohol as a solvent to overcome this challenge.

Fifteen experiments were conducted using a D-optimal design to investigate the effect of different concentrations and postprocessing times on the surface roughness, material removal rate (MRR), hardness and cost of SLA dental parts required for creating a clear customized aligner, and a container was constructed for chemical treatment of these parts made from photocurable resin.

The study revealed that the chemical postprocessing technique can significantly improve the surface roughness of dental SLA parts, but improper selection of concentration and time can lead to poor surface roughness. The optimal surface roughness was achieved with a concentration of 90 and a time of 37.5. Moreover, the dental part with the lowest concentration and time (60% and 15 min, respectively) had the lowest MRR and the highest hardness. The part with the highest concentration and time required the greatest budget allocation. Finally, the results of the multiobjective optimization analysis aligned with the experimental data.

This paper sheds light on a previously underestimated aspect, which is the pivotal role of chemical postprocessing in mitigating the adverse impact of stair case effect. This nuanced perspective contributes to the broader discourse on AM methodologies, establishing a novel pathway for advancing the capabilities of SLA in dental application.

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.

The purpose of this study is to investigate the structural, surface roughness and corrosion properties of the zirconium oxide thin films deposited onto SS304 substrates using the direct current (DC) magnetron sputtering technique.

DC sputtering at different powers – 80, 100 and 120 W – was used to deposit ZrO₂ thin films onto different substrates (Si/SS304) without annealing of the substrate. Atomic force microscope (AFM), energy-dispersive X-ray spectroscopy (EDS), Tafel extrapolation and contact angle techniques were applied to investigate the surface roughness, chemical compositions, corrosion behavior and hydrophobicity of these films.

Results showed that the thickness of the deposited film increased with power increase, while the corrosion current decreased with power increase. AFM images indicated that the surface roughness decreased with an increase in DC power. EDS analysis showed that the thin film has a stoichiometric ZrO₂ (Zr:O 1:2) composition with basic uniformity. Water contact angle measurements indicated that the hydrophobicity of the synthesized films decreased with an increase in surface roughness.

DC magnetron sputtering technique is infrequently used to deposition thin films. The obtained thin films showed good hydrophobic and anticorrosion properties. Finally, results are compared with other deposition techniques.

The purpose of this study is to investigate the behaviour of M40 grade of self-compacting concrete (SCC) with high volume of ground granulated blast furnace slag (GGBS) (50%) and recycled concrete aggregate (RCA) content up to 100% to assess the mechanical properties of SCC. As per guidelines of IS: 383 – 2016, the RCA can be replaced up to 20% of natural coarse aggregate up to M25 grade of concrete. This study assesses the mechanical properties of SCC beyond 20% of RCA content. Based on the experimental investigations, the compressive strength of mixes decreases as the content of RCA increases. It is found that concrete mixes with 20% RCA and shows the maximum compressive strength at 56 days.

The fresh properties as per EFNARC and IS: 10262–2019 guidelines, ultrasonic pulse velocity testing, mechanical properties and microstructure analysis have been conducted to evaluate the performance of SCC with RCA for practical applications.

From the experimental investigations, it is found that up to 50% of recycled coarse aggregate can be used for structural applications.

The environmental pollution and dumping of waste on green land can be reduced by effective utilization of recycled coarse aggregate and GGBS in the production of SCC.