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The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra).

The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments.

The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results.

The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity.

No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/

Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

This study aims to provide a salt ready-mix to instant fried noodles manufacturers.

Response surface methodology was used to get optimized salt ready-mix based on carbonate salt, disodium phosphate, tripotassium phospahte, sodium hexametaphosphate and sodium chloride. Peak viscosity of flour and yellowness, cooking loss and hardness of noodles were considered as response factors for finding optimized salt formulation.

The results showed that salts have an important role in governing quality of noodles. Optimum levels of five independent variables of salts, namely, carbonate salt (1:1 mixture of sodium to potassium carbonate), disodium phosphate, sodium hexametaphosphate, tripotassium phosphate and sodium chloride were 0.64%, 0.29%, 0.25%, 0.46% and 0.78% on flour weight basis, respectively.

To the best of the authors’ knowledge, this is the first study to assess the effect of different combinations of different salts on the quality of noodles. These findings will also benefit noodle manufacturers, assisting in production of superior quality noodles.

Gears are prone to instantaneous failure when operating under extreme conditions, affecting the machinery’s service life. With numerous types of gear meshing and complex operating conditions, this study focuses on the gear–rack mechanism. This study aims to analyze the effects and optimization of biomimetic texture parameters on the line contact tribological behavior of gear–rack mechanisms under starvation lubrication conditions.

Inspired by the microstructure of shark skin surface, a diamond-shaped biomimetic texture was designed to improve the tribological performance of gear–rack mechanism under starved lubrication conditions. The line contact meshing process of gear–rack mechanisms under lubrication-deficient conditions was simulated by using a block-on-ring test. Using the response surface method, this paper analyzed the effects of bionic texture parameters (width, depth and spacing) on the tribological performance (friction coefficient and wear amount) of tested samples under line contact and starved lubrication conditions.

The experimental results show an optimal proportional relationship between the texture parameters, which made the tribological performance of the tested samples the best. The texture parameters were optimized by using the main objective function method, and the preferred combination of parameters was a width of 69 µm, depth of 24 µm and spacing of 1,162 µm.

The research results have practical guiding significance for designing line contact motion pairs surface texture and provide a theoretical basis for optimizing line contact motion pairs tribological performance under extreme working conditions.

With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

This experimental study aims to deal with the improvement of process performance of electric discharge drilling (EDD) for fabricating true blind holes in titanium alloy Ti6Al4V. Micro EDD was performed on Ti6Al4V and blind holes were drilled into the workpiece.

The effects of input parameters (i.e. voltage, capacitance and spindle speed) on responses (i.e. material removal rate, tool wear rate and surface roughness [SR]) were evaluated through response surface methodology. The data was analyzed using analysis of variance and multi-optimization was performed for the optimized set of parameters. The optimized process parameters were then used to drill deeper blind holes.

Blind holes have few characteristics such as SR, taper angle and corner radius. The value of corner radius reflects the quality of the hole produced as well as the amount of tool roundness. The optimized process parameters suggested by the current experimental study lower down the response values (i.e. SR, taper angle and corner radius). The process is found very effective in producing finished blind holes.

This experimental study establishes EDD as a feasible process for the fabrication of truly blind holes in Ti6Al4V.

This study aims to examine whether customer trust is influenced by the congruence and incongruence between customers' perceptions of two types of omnichannel integration—perceived transactional integration (PTI) and perceived relational integration (PRI). The authors further considered the perceived effectiveness of e-commerce institutional mechanisms (PEEIM) as the boundary condition of omnichannel integration's effect.

Drawing upon the stereotype content model, this study hypothesizes the influences of PTI and PRI on customer trust wherein PEEIM moderates the relationships. The research model was empirically examined based on the responses surface analysis of survey data collected from 311 omnichannel customers.

Results showed that when PTI and PRI are congruent, customers are inclined to trust brands that have high levels of PTI and PRI rather than low levels of PTI and PRI. Moreover, the incongruence between PTI and PRI is positively related to customer trust. PEEIM was found to weaken the congruence effect while strengthening the incongruence effect. The authors also examined customer distrust as another relational outcome to provide a robust check.

This study uncovers customer cognition of omnichannel integration and examines the influences on customer trust, therefore contributing to our understanding of omnichannel integration's effect from the customer perspective. Findings from this research provide insights for brand managers on deploying channel integration strategies and institutional mechanisms to manage customer trust.

Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.

In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.

The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.

By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.

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.

This paper introduced the simple synthesis process of self-cleaning coating with fog-resistance property using hydrophobic polydimethylsiloxane (PDMS) polymer and nano-calcium carbonate (nano-CaCO₃) and titanium dioxide (TiO₂).

The synthesis method of PDMS/nano-CaCO₃-TiO₂ is based on sol-gel process. The crosslinking between PDMS and nanoparticles is driven by the covalent bond at temperature of 50°C. The 3-Aminopropyltriethoxysilane is used as binder for nanoparticles attachment in polymer matrix. Two fabrication methods are used, which are dip- and spray-coating methods.

The prepared coated glass fulfilled the requirement of standard self-cleaning and fog-resistance performance. For the self-cleaning test BS EN 1096-5:2016, the coated glasses exhibited the dust haze value around 20%–25% at tilt angle of 10°. For the antifog test, the coated glasses showed the fog haze value were below 2% and the gloss value were above 85%. The obtained results completely achieved the standard antifog value ASTM F659-06 protocol.

Findings will provide an infrastructure support for the building glass to enhance building’s energy efficiency, cleaning performance and friendly environment.

This study proposed the simple synthesis method using hydrophobic polymer and nano-CaCO₃ and nano-TiO₂, which can achieve optimum self-cleaning property at low tilt angle and fog-resistance performance for building glass.

The research findings have high potential for building company, cleaning building company and government sector. The proposed project capable to reduces the energy consumption about 20% per annum due to labor cost, time-consuming and safety during manual cleaning.

The novel method to develop self-cleaning coating with fog-resistance using simple synthesis process and fabrication method for building glass application.