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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.

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/

Current methods for the preparation of composite powder feedstock for selective laser melting (SLM) rely on costly nanoparticles or yield inconsistent powder morphology. This study aims to develop a cost-effective Ti6Al4V-carbon feedstock, which preserves the parent Ti6Al4V particle’s flowability, and produces in situ TiC-reinforced Ti6Al4V composites with superior traits.

Ti6Al4V particles were directly mixed with graphite flakes in a planetary ball mill. This composite powder feedstock was used to manufacture in situ TiC-Ti6Al4V composites using various energy densities. Relative porosity, microstructure and hardness of the composites were evaluated for different SLM processing parameters.

Homogeneously carbon-coated Ti6Al4V particles were produced by direct mixing. After SLM processing, in situ grown 100–500 nm size TiC nanoparticles were distributed within the α-martensite Ti6Al4V matrix. The formation of TiC particles refines the Ti6Al4V β grain size. Relative density varied between 96.4% and 99.5% depending on the processing parameters. Hatch distance, exposure time and point distance were all effective on relative porosity change, whereas only exposure time and point distance were effective on hardness change.

This work introduces a novel, cost-effective powder feedstock preparation method for SLM manufacture of Ti6Al4V-TiC composites. The in situ SLM composites achieved in this study have high relative density values, well-dispersed TiC nanoparticles and increased hardness. In addition, the feedstock preparation method can be readily adapted for various matrix and reinforcement materials in future studies.

The purpose of this study is to reveal the friction reduction performance and mechanism of granular flow lubrication during the milling of difficult-to-machining materials and provide a high-performance lubrication method for the precision cutting of nickel-based alloys.

The milling tests for Inconel 718 superalloy under dry cutting, flood lubrication and granular flow lubrication were carried out, and the milling force and machined surface quality were used to evaluate their friction reduction effect. Furthermore, based on the energy dispersive spectrometer (EDS) spectrums and the topographical features of machined surface, the lubrication mechanism of different granular mediums was explored during granular flow lubrication.

Compared with flood lubrication, the granular flow lubrication had a significant force reduction effect, and the maximum milling force was reduced by about 30%. At the same time, the granular flow lubrication was more conducive to reducing the tool trace size, repressing surface damage and thus achieving better surface quality. The soft particles had better friction reduction performance than the hard particles with the same particle size, and the friction reduction performance of nanoscale hard particles was superior to that of microscale hard particles. The friction reduction mechanism of MoS₂ and WS₂ soft particles is the mending effect and adsorption film effect, whereas that of SiO₂ and Al₂O₃ hard particles is mainly manifested as the rolling and polishing effect.

Granular flow lubrication was applied in the precision milling of Inconel 718 superalloy, and a comparative study was conducted on the friction reduction performance of soft particles (MoS₂, WS₂) and hard particles (SiO₂, Al₂O₃). Based on the EDS spectrums and topographical features of machined surface, the friction reduction mechanism of soft and hard particles was explored.

This paper aims to identify the optimal forming angle for the selective laser melting (SLM) process and evaluate the mechanical properties of the SLM-formed GH3536 alloy in the aero-engine field.

Forming the samples with optimized parameters and analyzing the microstructure and properties of the block samples in different forming angles with scanning electron microscope, XRD, etc. so as to analyze and reveal the laws and mechanism of the block samples in different forming angles by SLM.

There are few cracks on the construction surface of SLM formed samples, and the microstructure shows columnar subgrains and cellular subgrains. The segregation of metal elements was not observed in the microstructure. The pattern shows strong texture strength on the (111) crystal plane. In the sample, the tensile strength of 60° sample is the highest, the plasticity of 90° forming sample is the best, the comprehensive property of 45° sample is the best and the fracture mode is plastic fracture. The comprehensive performance of the part is the best under the forming angle of 45°. To ensure the part size, performance and support structure processing, additional dimensions are added to the part structure.

In this paper, how to make samples with different forming angles is described. Combined with the standard of forged GH3536 alloy, the microstructure and properties of the samples are analyzed, and the optimal forming angle is obtained.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

The purpose of this study is to examine the impact of MoS₂ on the microstructure and characteristics of micro-arc oxidized (MAO) ceramic coatings created on ZK60 magnesium alloy through the addition of varying concentrations of MoS₂ particles to the electrolyte, aiming to enhance the corrosion resistance of magnesium alloy.

The surface morphology, roughness and phase composition of the coatings were analyzed using scanning electron microscopy, a hand-held roughness tester and an X-ray diffractometer, respectively, and the corrosion resistance of the MAO coatings prepared by the addition of different contents of MoS₂ particles was tested and analyzed using an electrochemical workstation.

The results demonstrate that MoS₂/MgO composite coatings have been successfully prepared on the surface of magnesium alloys through micro-arc oxidation. Furthermore, the corrosion resistance of the ZK60 magnesium alloy prepared with the addition of 1.0 g/L MoS₂ was the best compared to the other samples.

MoS₂ particles were able to penetrate the coatings successfully during the micro-arc oxidation process, acting as a barrier in the micropores to prevent the corrosion medium from touching the surface, thus improving the corrosion resistance of the sample. The electrochemical workstation was used to study the corrosion resistance of the MoS₂/MAO coating on the ZK60 magnesium alloy.

This paper aims to examine the static compression characteristics of cell topologies in body-centered cubic with vertical struts (BCCZ) and face-centered cubic with vertical struts (FCCZ) along with novel BCCZZ and FCCZZ lattice structures.

The newly developed structures were obtained by adding extra interior vertical struts into the BCCZ and FCCZ configurations. The samples, composed of the AlSi10Mg alloy, were fabricated using the selective laser melting (SLM) additive manufacturing technique. The specific compressive strength and failure behavior of the manufactured lattice structures were investigated, and comparative analysis among them was done.

The results revealed that the specific strength of BCCZZ and FCCZZ samples with 0.5 mm strut diameter exhibited approximately a 23% and 18% increase, respectively, compared with the BCCZ and FCCZ samples with identical strut diameters. Moreover, finite element analysis was carried out to simulate the compressive response of the lattice structures, which could be used to predict their strength and collapse mode. The findings showed that while the local buckling of lattice cells is the major failure mode, the samples subsequently collapsed along a diagonal shear band.

An original and systematic investigation was conducted to explore the compression properties of newly fabricated lattice structures using SLM. The results revealed that the novel FCCZZ and BCCZZ structures were found to possess significant potential for load-bearing applications.

The purpose of this paper is to assess the feasibility of analytical models, specifically the radial basis function method, Akbari–Ganji method and Gaussian method, in conjunction with the finite element method. The aim is to examine the impact of processing parameters on temperature history.

Through analytical investigation and finite element simulation, this research examines the influence of processing parameters on temperature history. Simufact software with a thermomechanical approach was used for finite element simulation, while radial basis function, Akbari–Ganji and Gaussian methods were used for analytical modeling to solve the heat transfer differential equation.

The accuracy of both finite element and analytical methods was validated with about 90%. The findings revealed direct relationships between thermal conductivity (from 100 to 200), laser power (from 400 to 800 W), heat source depth (from 0.35 to 0.75) and power absorption coefficient (from 0.4 to 0.8). Increasing the values of these parameters led to higher temperature history. On the other hand, density (from 7,600 to 8,200), emission coefficient (from 0.5 to 0.7) and convective heat transfer (from 35 to 90) exhibited an inverse relationship with temperature history.

The application of analytical modeling, particularly the utilization of the Akbari–Ganji, radial basis functions and Gaussian methods, showcases an innovative approach to studying directed energy deposition. This analytical investigation offers an alternative to relying solely on experimental procedures, potentially saving time and resources in the optimization of DED processes.

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.