Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy.

The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.

Machine Learning Modelling and Feature Engineering in Seismology Experiment.

This article aims to discusses machine learning modelling using a dataset provided by the LANL (Los Alamos National Laboratory) earthquake prediction competition hosted by Kaggle. The data were obtained from a laboratory stick-slip friction experiment that mimics real earthquakes. Digitized acoustic signals were recorded against time to failure of a granular layer compressed between steel plates. In this work, machine learning was employed to develop models that could predict earthquakes. The aim is to highlight the importance and potential applicability of machine learning in seismology The XGBoost algorithm was used for modelling combined with 6-fold cross-validation and the mean absolute error (MAE) metric for model quality estimation. The backward feature elimination technique was used followed by the forward feature construction approach to find the best combination of features. The advantage of this feature engineering method is that it enables the best subset to be found from a relatively large set of features in a relatively short time. It was confirmed that the proper combination of statistical characteristics describing acoustic data can be used for effective prediction of time to failure. Additionally, statistical features based on the autocorrelation of acoustic data can also be used for further improvement of model quality. A total of 48 statistical features were considered. The best subset was determined as having 10 features. Its corresponding MAE was 1.913 s, which was stable to the third decimal point. The presented results can be used to develop artificial intelligence algorithms devoted to earthquake prediction.

Modelling the Kerf Angle, Roughness and Waviness of the Surface of Inconel 718 in an Abrasive Water Jet Cutting Process.

An experimental study of the abrasive water jet cutting process of Inconel 718 alloy samples with varying values of cutting speed, abrasive flow rate and cutting material height was carried out. Surface roughness and waviness were measured at different cutting depths, and the variation of the kerf angle was studied. It was shown that the depth of cut has the greatest effect on roughness and waviness. The height of the sample has no impact on the roughness and waviness at a particular depth of cut. As the depth of cut increases, in most cases, roughness and waviness increase as well. It has been proven that the cutting speed has a negligible effect on surface roughness, but it has a significant effect on surface waviness. The waviness, on the other hand, depends only slightly on the abrasive flow. It has been proven that the kerf angle does not depend on the abrasive flow. The kerf angle depends mainly on the height of the sample. The models were developed for the parameters of roughness Ra and Rz, waviness Wa and Wz and kerf angle. All models were calculated without separating the surface into smooth and rough cutting regions.

On the Microstructure, Microhardnessand Wear Behavior of Gray Cast Iron Surface Layer after Laser Strengthening.

Cast iron is one of the most common structural materials and is widely used in mechanical engineering production. Taking into account its rather low mechanical properties, different technologies are currently used in industry, among other areas, for the mechanical and thermal strengtheningof the surface layer, as well as surface alloying of workpieces. The aim of this study was a comprehensive analysis of changes in the microstructure, microhardness of the surface layer and its wear resistance under lubrication friction conditions and changed surface energy density in order to ensure the effectiveness of laser strengthening of gray cast iron. In this research, the efficiency of gray cast iron GJL200 laser strengthening was described. The basic properties of the surface layer of gray cast iron under laser strengthening, including the microstructure, microhardness, tribological and wear behavior, were compared with the properties of cast iron in the initial state. It was found that laser strengthening under the right choice of the surface energy density ensured a five-to-tenfold increase in the wear resistance of gray cast iron in comparison with the initial state. This was due to forming unconventional pseudo-vermicular graphite shapes at the friction zone, as well as a spongy-capillary effect appearance. The appropriate selection of surface energy density values provided stable and low coefficients of friction and a very significant increase in the wear resistance compared with the values reached for a cast iron in the initial state. This fact is new and very important for the engineering practice. The values of the surface energy density can be easily controlled, which means that different parts can be operated efficiently after laser strengthening.

Development of an Oxide Layer on Al 6061 Using Plasma Arc Electrolytic Oxidation in Silicate-Based Electrolyte.

The plasma electrolytic method is one of the techniques which can be used to form an oxide layer on the substrate material surface. This technique employs ion exchange by developing an electrolytic arc between the cathode and the anode. The strong bond at high temperatures promotes the formation of an oxide layer on the metal surface. The electrolyte composition has a strong influence on the metal surface characteristics. Hence, the addition of certain nanoparticles in an adequate amount can improve the surface properties like wear and corrosion resistance. In this study, a plasma electrolytic technique based on using a direct current and voltage approach is investigated. The plasma electrolytic technique is utilized to develop an oxide layer on the Al 6061 alloy substrate surface using a DC voltage input on a silicate-based electrolyte. The substrate surface is then investigated for the thickness of the oxide layer formed and the amount of carbon element absorbed, using the SEM and XRD analysis. The experimentation and the study of the results confirmed the presence of a substantial oxide layer on the surface. The influence of the process on the output parameters-direct voltage and electrode distance is studied with the significant changes obtained in the weight percentage of elements like C, Al, Si, and O as supported by SEM and EDAX analysis. Most changes occurred when using a 197 V and in the current range of 0.3 A to 1 A. This can be useful further to improve the mechanical properties of the metal alloy using the plasma arc oxidation method.

Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications.

Rotating machine is a common class of machinery in most of the industry and the main root cause of machinery failure is a faulty bearing. Bearings are most widely used in various types of machine elements ranging from small to heavy machinery and the common cause of machinery failure is a fault in bearings. Bearing faults can be external or internal which mainly depends on different operating conditions and these faults may cause severe damage to rotating components in machinery. Signal processing methods have traditionally been used to diagnose faults in tapered roller element bearings. A wavelet transform is the most common and effective tool for understanding and analyzing the vibration signal of bearings as it is responded quickly and observed sudden changes along with the transient impulses in the signal caused by faults in the different parts of bearing elements. In this article, localized fault's position and size on the outer ring of tapered roller bearing were investigated. Three different real values wavelets (DB2, Meyer, and Morlet) are analyzed as per Simple Sensitivity index criteria. Finally, experiments are carried out with four sets of bearing having fault on outer racing of bearing, and for the estimation of fault size, the setup was misaligned at ranging (0.00mm-1.50 mm) with a uniform deviation of 0.50 mm for each experiment. Shannon entropy was calculated for the identification of localized size of the faults with wavelets nomenclature, the result of DB2, Morlet, and Meyer wavelets at high-frequency zone are presented.The scanning electron microscope (SEM) has been taken for the estimation of size of the fault. The proposed method has been successfully implemented for measuring defect width and size. Also, it has been observed that with increased magnification level from 0.00 mm to 0.50 mm, the crack width of the faulty bearing was increased by 0.813 mm, and whenever on further increase in magnification level of 0.50, 1.00 mm and 1.50 mm the crack width of the faulty bearing was increased by 2.568 mm and 3.856 respectively.

Recent developments in the design, development, and analysis of the influence of external magnetic-field on gas-metal arc welding of non-ferrous alloys: review on optimization of arc-structure to enhance the morphology, and mechanical properties of welded joints for automotive applications.

"External magnetic-field (EMF)" has been proved as an additional process parameter like voltage and current affecting the weld arc form, molten metal-flow, microstructure, and characteristics of the weld joint. This article analyzed the research work that has been done to promote EMF application in welding and discussed the recent development trends and research in the design and fabrication of EMF setup to the controlled arc welding process. It is found that even after the successful application of EMF in welding. Still, there is no mass level initiation to integrate EMF with welding machines that hinder researchers and manufacturers to accept it as a regular process parameter to control weld quality.

Bioconversion Process of Polyethylene from Waste Tetra Pak® Packaging to Polyhydroxyalkanoates.

Presented herein are the results of a novel recycling method for waste Tetra Pak® packaging materials. The polyethylene (PE-T) component of this packaging material, obtained via a separation process using a "solvents method", was used as a carbon source for the biosynthesis of polyhydroxyalkanoates (PHAs) by the bacterial strain Cupriavidus necator H16. Bacteria were grown for 48-72 h, at 30 °C, in TSB (nitrogen-rich) or BSM (nitrogen-limited) media supplemented with PE-T. Growth was monitored by viable counting. It was demonstrated that C. necator utilised PE-T in both growth media, but was only able to accumulate 40% w/w PHA in TSB supplemented with PE-T. Only 1.5% w/w PHA was accumulated in the TSB control, and no PHA was detected in the BSM control. Extracted biopolymers were characterised by nuclear magnetic resonance (NMR), Fourier-transform infrared (FTIR) spectroscopy, electrospray tandem mass spectrometry (ESI-MS/MS), gel permeation chromatography (GPC), and accelerator mass spectrometry (AMS). The characterisation of PHA by ESI-MS/MS revealed that PHA produced by C. necator in TSB supplemented with PE-T contained 3-hydroxybutyrate, 3-hydroxyvalerate, and 3-hydroxyhexanoate co-monomeric units. AMS analysis also confirmed the presence of 96.73% modern carbon and 3.27% old carbon in PHA derived from Tetra Pak®. Thus, this study demonstrates the feasibility of our proposed recycling method for waste Tetra Pak® packaging materials, alongside its potential for producing value-added PHA, and the ability of 14C analysis in validating this bioconversion process.

In Situ Micro-Observation of Surface Roughness and Fracture Mechanism in Metal Microforming of Thin Copper Sheets with Newly Developed Compact Testing Apparatus.

A better understanding of material deformation behaviours with changes in size is crucial to the design and operation of metal microforming processes. In order to facilitate the investigation of size effects, material deformation behaviours needed to be determined directly from material characterizations. This study was aimed at the design and manufacture of a compact universal testing machine (UTM) compatible with a 3D laser-confocal microscope to observe the deformation behaviour of materials in real-time. In this study, uniaxial micro tensile testing was conducted on three different thin (0.05 mm, 0.1 mm, and 0.3 mm) copper specimens with characteristic dimensions at micro scales. Micro tensile experimental runs were carried out on copper specimens with varying grain sizes on the newly developed apparatus under a 3D laser-confocal microscope. Microscale experiments under 3D laser-confocal microscope provided not only a method to observe the microstructure of materials, but also a novel way to observe the early stages of fracture mechanisms. From real-time examination using the newly developed compact testing apparatus, we discovered that fracture behaviour was mostly brought about by the concave surface formed by free surface roughening. Findings with high stability were discovered while moving with the sample grasped along the drive screw in the graphical plot of a crosshead's displacement against time. Our results also showed very low mechanical noise (detected during the displacement of the crosshead), which indicated that there were no additional effects on the machine, such as vibrations or shifts in speed that could influence performance. The engineering stress-strain plots of the pure copper-tests with various thicknesses or samples depicted a level of stress necessary to initiate plastic flowing inside the material. From these results, we observed that strength and ductility declined with decreasing thickness. The influence of thickness on fracture-strain, observed during tensile testing, made it clear that the elongation-at-break of the pure-copper foils intensely decreased with decreases in thickness. The relative average surface-roughness Ra was evaluated, which showed us that the surface-roughness escalated with the increasing trend of plasticity deformation (plastic strain) ε. For better understanding of the effects of plastic strain on surface roughness prior to material fractures, micro tensile tests were performed on the newly developed machine under a 3D laser-confocal-microscope. We observed that homogeneous surface roughness was caused by plastic strain, which further formed the concave surface that led to the fracture points. Finally, we concluded that surface roughness was one of the crucial factors influencing the fracture behaviour of metallic sheet-strips in metal microforming. We found that this type of testing apparatus could be designed and manufactured within a manageable budget.

Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms.

Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants.

Novel insights on different treatment of magnesium alloys: A critical review.

Magnesium alloys are extensively used for weight reduction in automotive and aircraft applications. This research presents the effect of heat and cryogenic treatment on aluminium-zinc-based Mg alloys. Both treatments can improve the mechanical and corrosion properties of the AZ series Mg alloy. The review deals with a broad understanding of the microstructure changes that occur during heat and cryogenic treatment of Mg alloy. The mechanical and corrosion characteristics of heat and cryogenic treated AZ31, AZ91, AZ63, and AZ80 Mg alloys are discussed. The essential strengthening mechanisms of heat and cryogenic treated AZ series Mg are discussed with microstructure changes. This review has also shown a few gaps in research on the selection of suitable pre- and post-treatment processes for Mg alloy. The effects on grain refinement and the formation of secondary phase particles are discussed in detail. The related crystallographic plane, twining, and dislocation changes are out of the scope of this review. Finally, the correlations of the above changes to mechanical properties are the directions of the future.

Estimation of Minimum Uncut Chip Thickness during Precision and Micro-Machining Processes of Various Materials-A Critical Review.

Evaluation of the phenomena characterizing the chip decohesion process during cutting is still a current problem in relation to precision, ultra-precision, and micro-machining processes of construction materials. The reliable estimation of minimum uncut chip thickness is an especially challenging task since it directly affects the machining process dynamics and formation of a surface topography. Therefore, in this work a critical review of the recent studies concerning the determination of minimum uncut chip thickness during precision, ultra-precision, and micro-cutting is presented. The first part of paper covers a characterization of the precision, ultra-precision, and micro-cutting processes. In the second part, the analytical, experimental, and numerical methods for minimum uncut chip thickness estimation are presented in detail. Finally, a summary of the research results for minimum uncut chip thickness estimation is presented, together with conclusions and a determination of further research directions.

Geometric Specification of Non-Circular Pulleys Made with Various Additive Manufacturing Techniques.

The paper presents the procedure of generating geometrical features on the contours of non-circular pulleys through the selection of materials and technological parameters for easy and efficient production of these parts. Based on the models designed in the computer aided design (CAD) system, several prototype non-standard pulleys were made, which were assessed for functional characteristics and correct operation of non-linear gears. The effect of additive technology on the geometric specification of non-circular pulleys was also assessed. The results showed that thanks to the use of additive methods, the need for costly manufacturing of such wheels with subtractive methods was eliminated. Additionally, it is not necessary to design specialized cutting tools or to use conventional or numerically controlled machine tools to manufacture these wheels. The test results showed that in case of selective laser sintering (SLS) the highest accuracy of mapping (0.01 mm) of geometrical features of the surface was obtained. This result is confirmed by the assessment of the morphology of the surface of the teeth of gears made with this technique, characterized by a uniform structure of the working surface of the wheel while maintaining a high tolerance of the outer profile of gear for selective laser sintering at the level of ±0.03 mm. Research has shown that most of the additive methods used to manufacture non-circular pulleys meet the required geometrical features and due to the short production time of these pulleys, these methods also facilitate quick verification of the designed pulley geometry.

High-Performance Face Milling of 42CrMo4 Steel: Influence of Entering Angle on the Measured Surface Roughness, Cutting Force and Vibration Amplitude.

High feed Milling is a new milling method, which allows to apply high feed rates and increase machining efficiency. The method utilizes face cutters with a very small entering angle, of about 10°-20°. Thus, the cut layer cross-section is different than in traditional milling. In order to examine the high feed milling (HFM), experimental tests were conducted, preceded by an analysis of cutting zones when milling with an HF face cutter. The face milling tests of 42CrMo4 steel with the use of an HF cutter characterized by an entering angle, dependent on axial depth of cut ap and insert radius r values, as well as with a conventional face cutter with the entering angle of 45° were performed. The study focused on analyzing the vibration amplitude, cutting force components in the workpiece coordinate system, and surface roughness. The experimental tests proved, that when milling with constant cut layer thickness, the high feed cutter allowed to obtain twice the cutting volume in comparison with the conventional face cutter. However, higher machining efficiency resulted in an increase in cutting force components. Furthermore, the results indicate significantly higher surface roughness and higher vibration amplitudes when milling with the HF cutter.

Integration of Fuzzy AHP and Fuzzy TOPSIS Methods for Wire Electric Discharge Machining of Titanium (Ti6Al4V) Alloy Using RSM.

Titanium and its alloys exhibit numerous uses in aerospace, automobile, biomedical and marine industries because of their enhanced mechanical properties. However, the machinability of titanium alloys can be cumbersome due to their lower density, high hardness, low thermal conductivity, and low elastic modulus. The wire electrical discharge machining (WEDM) process is an effective choice for machining titanium and its alloys due to its unique machining characteristics. The present work proposes multi-objective optimization of WEDM on Ti6Al4V alloy using a fuzzy integrated multi-criteria decision-making (MCDM) approach. The use of MCDM has become an active area of research due to its proven ability to solve complex problems. The novelty of the present work is to use integrated fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal situation (TOPSIS) to optimize the WEDM process. The experiments were systematically conducted adapting the face-centered central composite design approach of response surface methodology. Three independent factors-pulse-on time (Ton), pulse-off time (Toff), and current-were chosen, each having three levels to monitor the process response in terms of cutting speed (VC), material removal rate (MRR), and surface roughness (SR). To assess the relevance and significance of the models, an analysis of variance was carried out. The optimal process parameters after integrating fuzzy AHP coupled with fuzzy TOPSIS approach found were Ton = 40 µs, Toff = 15 µs, and current = 2A.

Image Processing of Mg-Al-Sn Alloy Microstructures for Determining Phase Ratios and Grain Size and Correction with Manual Measurement.

The study of microstructures for the accurate control of material properties is of industrial relevance. Identification and characterization of microstructural properties by manual measurement are often slow, labour intensive, and have a lack of repeatability. In the present work, the intermetallic phase ratio and grain size in the microstructure of known Mg-Sn-Al alloys were measured by computer vision (CV) technology. New Mg (Magnesium) alloys with different alloying element contents were selected as the work materials. Mg alloys (Mg-Al-Sn) were produced using the hot-pressing powder metallurgy technique. The alloys were sintered at 620 °C under 50 MPa pressure in an argon gas atmosphere. Scanning electron microscopy (SEM) images were taken for all the fabricated alloys (three alloys: Mg-7Al-5Sn, Mg-8Al-5Sn, Mg-9Al-5Sn). From the SEM images, the grain size was counted manually and automatically with the application of CV technology. The obtained results were evaluated by correcting automated grain counting procedures with manual measurements. The accuracy of the automated counting technique for determining the grain count exceeded 92% compared to the manual counting procedure. In addition, ASTM (American Society for Testing and Materials) grain sizes were accurately calculated (approximately 99% accuracy) according to the determined grain counts in the SEM images. Hence, a successful approach was proposed by calculating the ASTM grain sizes of each alloy with respect to manual and automated counting methods. The intermetallic phases (Mg17Al12 and Mg2Sn) were also detected by theoretical calculations and automated measurements. The accuracy of automated measurements for Mg17Al12 and Mg2Sn intermetallic phases were over 95% and 97%, respectively. The proposed automatic image processing technique can be used as a tool to track and analyse the grain and intermetallic phases of the microstructure of other alloys such as AZ31 and AZ91 magnesium alloys, aluminium, titanium, and Co alloys.

Corrosion Resistance and Surface Bioactivity of Ti6Al4V Alloy after Finish Turning under Ecological Cutting Conditions.

The influence of cooling conditions and surface topography after finish turning of Ti6Al4V titanium alloy on corrosion resistance and surface bioactivity was analyzed. The samples were machined under dry and minimum quantity lubrication (MQL) conditions to obtain different surface roughness. The surface topographies of the processed samples were assessed and measured using an optical profilometer. The produced samples were subjected to electrochemical impedance spectroscopy (EIS) and corrosion potential tests (Ecorr) in the presence of simulated body fluid (SBF). The surface bioactivity of the samples was assessed on the basis of images from scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) analysis. The inspection of the surfaces of samples after turning under dry and MQL conditions revealed unevenly distributed precipitation of hydroxyapatite compounds (Ca/P) with a molar ratio in the range of 1.73-1.97. Regardless of the cutting conditions and surface roughness, the highest values of Ecorr ~0 mV were recorded on day 7 of immersion in the SBF solution. The impedance characteristics showed that, compared to the MQL conditions, surfaces machined under dry conditions were characterized by greater resistance and the presence of a passive layer on the processed surface. The main novelty of the paper is the study of the effect of ecological machining conditions, namely, dry and MQL cutting on the corrosion resistance and surface bioactivity of Ti6Al4V titanium alloy after finish turning. The obtained research results have practical significance. They can be used by engineers during the development of technological processes for medical devices made of Ti6Al4V alloy to obtain favorable functional properties of these devices.

Optimization of FFF Process Parameters by Naked Mole-Rat Algorithms with Enhanced Exploration and Exploitation Capabilities.

Fused filament fabrication (FFF) has numerous process parameters that influence the mechanical strength of parts. Hence, many optimization studies are performed using conventional tools and algorithms. Although studies have also been performed using advanced algorithms, limited research has been reported in which variants of the naked mole-rat algorithm (NMRA) are implemented for solving the optimization issues of manufacturing processes. This study was performed to scrutinize optimum parameters and their levels to attain maximum impact strength, flexural strength and tensile strength based on five different FFF process parameters. The algorithm yielded better results than other studies and successfully achieved a maximum response, which may be helpful to enhance the mechanical strength of FFF parts. The study opens a plethora of research prospects for implementing NMRA in manufacturing. Moreover, the findings may help identify critical parametric levels for the fabrication of customized products at the commercial level and help to attain the objectives of Industry 4.0.

Analysis and Optimization of Dimensional Accuracy and Porosity of High Impact Polystyrene Material Printed by FDM Process: PSO, JAYA, Rao, and Bald Eagle Search Algorithms.

High impact polystyrene (HIPS) material is widely used for low-strength structural applications. To ensure proper function, dimensional accuracy and porosity are at the forefront of industrial relevance. The dimensional accuracy cylindricity error (CE) and porosity of printed parts are influenced mainly by the control variables (layer thickness, shell thickness, infill density, print speed of the fused deposition modeling (FDM) process). In this study, a central composite design (CCD) matrix was used to perform experiments and analyze the complete insight information of the process (control variables influence on CE and porosity of FDM parts). Shell thickness for CE and infill density for porosity were identified as the most significant factors. Layer thickness interaction with shell thickness, infill density (except for CE), and print speed were found to be significant for both outputs. The interaction factors, i.e., shell thickness and infill density, were insignificant (negligible effect) for both outputs. The models developed produced a better fit for regression with an R2 equal to 94.56% for CE, and 99.10% for porosity, respectively. Four algorithms (bald eagle search optimization (BES), particle swarm optimization (PSO), RAO-3, and JAYA) were applied to determine optimal FDM conditions while examining six case studies (sets of weights assigned for porosity and CE) focused on minimizing both CE and porosity. BES and RAO-3 algorithms determined optimal conditions (layer thickness: 0.22 mm; shell thickness: 2 mm; infill density: 100%; print speed: 30 mm/s) at a reduced computation time equal to 0.007 s, differing from JAYA and PSO, which resulted in an experimental CE of 0.1215 mm and 2.5% of porosity in printed parts. Consequently, BES and RAO-3 algorithms are efficient tools for the optimization of FDM parts.

Special Issue: Advances in Hard-to-Cut Materials: Manufacturing, Properties, Process Mechanics and Evaluation of Surface Integrity.

The rapid growth of a modern industry results in a growing demand for construction materials with excellent operational properties. However, the improved features of these materials can significantly hinder their manufacturing, therefore they can be defined as hard-to-cut. The main difficulties during the manufacturing/processing of hard-to-cut materials are attributed to their high hardness and abrasion resistance, high strength at room or elevated temperatures, increased thermal conductivity, as well as their resistance to oxidation and corrosion. Nowadays the group of hard-to-cut materials includes the metallic materials, composites, as well as ceramics. This special issue, "Advances in Hard-to-Cut Materials: Manufacturing, Properties, Process Mechanics and Evaluation of Surface Integrity" provides a collection of research papers regarding the various problems correlated with hard-to-cut materials. The analysis of these studies reveals primary directions regarding the developments in manufacturing methods, and the characterization and optimization of hard-to-cut materials.