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The goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.

In the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.

The suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.

The findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

This study aims to comparatively analyze the cut parts obtained as a result of cutting the Ni-based Inconel 625 alloy, which is widely used in the aerospace industry, with the wire electro-discharge machining (WEDM) and abrasive water jet machining (AWJM) methods in terms of macro- and microanalyses.

In this study, calipers, Mitutoyo SJ-210, Nikon SMZ 745 T, scanning electron microscope and energy dispersive X-ray were used to determine kerf, surface roughness and macro- and microanalyses.

Considering the applications in the turbine industry, it has been determined that the WEDM method is suitable to meet the standards for the machinability of Inconel 625 alloy. In contrast, the AWJM method does not meet the standards. Namely, while the kerf angle was formed because the hole entrance diameters of the holes obtained with AWJM were larger than the hole exit diameters, the equalization of the hole entry and exit dimensions, thanks to the perpendicularity and tension sensitivity of the wire electrode used in the holes drilled with WEDM ensured that the kerf angle was not formed.

It is known that the surface roughness of the parts used in the turbine industry is accepted at Ra = 0.8 µm. In this study, the average roughness value obtained from the successful drilling of Inconel 625 alloy with the WEDM method was 0.799 µm, and the kerf angle was obtained as zero. In the cuts made with the AWJM method, thermal effects such as debris, microcracks and melted materials were not observed; an average surface roughness of 2.293 µm and a kerf of 0.976° were obtained.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

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 paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.

In order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.

Finally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.

Based on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.

Even though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.

The purpose of this study is analysis of deformation and vibrations of turbine blades produced by high electrolyte pressure during electrochemical machining.

An experimental setup was designed, experiments were conducted and the obtained results were compared with the finite element results. The deformations were measured according to various flow rates of electrolyte. In finite element calculations, the pressure distribution created by the electrolyte on the blade surface was obtained in the ANSYS® (A finite element analysis software) Fluent software and transferred to the static structural where the deformation analysis was carried out. Three different parameters were examined, namely blade thickness, blade material and electrolyte pressure on blade disk caused by mass flow rate. The deformation results were compared with the gap distances between cathode and anode.

Large deformations were obtained at the free end of the blade and the most curved part of it. The appropriate pressure values for the electrolyte to be used in the production of blisk blades were proposed numerically. It has been determined that high pressure applications are not suitable for gap distance lower than 0.5 mm.

When the literature is examined, it is required that the high speed flow of the electrolyte is desired in order to remove the parts that are separated from the anode from the machining area during electrochemical machining. However, the electrolyte flowing at high speeds causes high pressure in the blisk blades, excessive deformation and vibration of the machined part, and as a result, contact of the anode with the cathode. This study provides important findings for smooth electro chemical machining at high electrolyte flows.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

Three-dimensional (3D) printed parts usually have poor surface quality due to layer manufacturing’s “stair casing/stair-stepping”. So post-processing is typically needed to enhance its capabilities to be used in closed tolerance applications. This study aims to examine abrasive flow finishing for 3D printed polylactic acid (PLA) parts.

A new eco-friendly abrasive flow machining media (EFAFM) was developed, using paper pulp as a base material, waste vegetable oil as a liquid synthesizer and natural additives such as glycine to finish 3D printed parts. Characterization of the media was conducted through thermogravimetric analysis and Fourier transform infrared spectroscopy. PLA crescent prism parts were produced via fused deposition modelling (FDM) and finished using AFM, with experiments designed using central composite design (CCD). The impact of process parameters, including media viscosity, extrusion pressure, layer thickness and finishing time, on percentage improvement in surface roughness (%ΔRa) and material removal rate were analysed. Artificial neural network (ANN) and improved grey wolf optimizer (IGWO) were used for data modelling and optimization, respectively.

The abrasive media developed was effective for finishing FDM printed parts using AFM, with SEM images and 3D surface profile showing a significant improvement in surface topography. Optimal solutions were obtained using the ANN-IGWO approach. EFAFM was found to be a promising method for improving finishing quality on FDM 3D printed parts.

The present study is focused on finishing FDM printed crescent prism parts using AFM. Future research may be done on more complex shapes and could explore the impact of different materials, such as thermoplastics and composites for different applications. Also, implication of other techniques, such as chemical vapour smoothing, mechanical polishing may be explored.

In the biomedical field, the use of 3D printing has revolutionized the way in which medical devices, implants and prosthetics are designed and manufactured. The biodegradable and biocompatible properties of PLA make it an ideal material for use in biomedical applications, such as the fabrication of surgical guides, dental models and tissue engineering scaffolds. The ability to finish PLA 3D printed parts using AFM can improve their biocompatibility, making them more suitable for use in the human body. The improved surface quality of 3D printed parts can also facilitate their sterilization, which is critical in the biomedical field.

The use of eco-friendly abrasive flow finishing for 3D printed parts can have a positive impact on the environment by reducing waste and promoting sustainable manufacturing practices. Additionally, it can improve the quality and functionality of 3D printed products, leading to better performance and longer lifespans. This can have broader economic and societal benefits.

This AFM media constituents are paper pulp, waste vegetable oil, silicon carbide as abrasive and the mixture of “Aloe Barbadensis Mill” – “Cyamopsis Tetragonoloba” powder and glycine. This media was then used to finish 3D printed PLA crescent prism parts. The study also used an IGWO to optimize experimental data that had been modelled using an ANN.

Erosion and abrasion are the prominent wear mechanisms reducing the lifetime of machine components. Both wear mechanisms are playing a role meanwhile, generating a synergy, leading to a material removal on the target. The purpose of study is to create a mathematical expression for erosive abrasive wear.

Many factors such as environmental cases and material character have an influence in erosive abrasive wear. In the work, changes in abrasive size and material hardness have been analyzed. As an abrasive particle, quartz sand has been used. All tests have been done in 20 wt.% slurry. Heat treatment has been applied to different steel specimens (steel grades C15, St 37 and Ck45) to change hardness value, which ranged from 185 to 880 Vickers hardness number.

After the four-hour test, it is determined that by an increase in abrasive size and decrease in material hardness, wear rate increases. Worn surfaces of the targets have been examined to figure out the wear mechanisms at different conditions under scanning electron microscopy. The results indicate that by an increase in material hardness, the number and diameter of micro-craters on the worn surfaces decrease. The diameters of micro-craters have been about 3–8 µm in hard materials and about 120–140 µm in soft materials.

It is determined that by an increase in abrasive size and decrease in material hardness, wear rate increases. The results indicate that by an increase in material hardness, the number and diameter of micro-craters on the worn surfaces decrease.

The study enables to indicate the dominant factor in worn steel used in mechanical components.

After analyzing the test results, a novel mathematical expression, considering both abrasive size and material hardness, has been developed.

In this study, experimental studies were carried out using different process parameters of the soft pneumatic gripper (SPG) fabricated by the fused deposition modeling method. In the experimental studies, the surface quality of the gripper was examined by determining four different levels and factors. The experiment was designed to estimate the surface roughness of the SPG.

The methodology consists of an experimental phase in which the SPG is fabricated and the surface roughness is measured. Thermoplastic polyurethane (TPU) flex filament material was used in the fabrication of SPG. The control factors used in the Taguchi L16 vertical array experimental design and their level values were determined. Analysis of variance (ANOVA) was performed to observe the effect of printing parameters on the surface quality. Finally, regression analysis was applied to mathematically model the surface roughness values obtained from the experimental measurements.

Based on the Taguchi signal-to-noise ratio and ANOVA, layer height is the most influential parameter for surface roughness. The best surface quality value was obtained with a surface roughness value of 18.752 µm using the combination of 100 µm layer height, 2 mm wall thickness, 200 °C nozzle temperature and 120 mm/s printing speed. The developed model predicted the surface roughness of SPG with 95% confidence intervals.

It is essential to examine the surface quality of parts fabricated in additive manufacturing using different variables. In the literature, surface roughness has been examined using different factors and levels. However, the surface roughness of a soft gripper fabricated with TPU material has not been examined previously. The surface quality of parts fabricated using flexible materials is very important.