J. Manuf. Mater. Process., Volume 8, Issue 5 (October 2024) – 52 articles

The work that is being presented demonstrates that there is a critical point at which the engineering stress–strain diagram’s elastic–plastic region transitions to yield and fracture stresses. This transition is demonstrated using thin-walled specimens made using selective laser melting technology from high-strength aluminum alloys (AlSi10Mg) that have undergone preliminary heat treatment. It was discovered that the strain-hardening coefficient, which was determined in the section from yield strength to fracture strength, and the critical point have a highly statistically significant association (0.83 by Spearman and 0.93 by Pearson). It was possible to derive a regression equation that connected the strain-hardening coefficient with the crucial transition point. The type of stress distribution in the elastic–plastic region changes (the Weibull distribution changes to a normal distribution) as the plasticity of the thin-walled samples increases. Additionally, the contribution of the probability density of the stress distribution described by the Cauchy distribution increases in a mode near the point at which the probability density of the fracture increases. Full article

For advancing manufacturing, arising AM, with an inverse philosophical approach compared to conventional procedures, has benefits that include intricate fabrication, reduced material waste, flexible design, and more. Regardless of its potential, AM must overcome several challenges due to multi-physical processes with miscellaneous physical stimuli in diverse materials systems and situations, such as anisotropic microstructure and mechanical properties, a restricted choice of materials, defects, and high cost. Unlike conventional experimental work that requires extensive trial and error resources and FEM, which generally consumes substantial computational power, the analytical approach based on physics is an exceptional choice. Understanding the relationship between the microstructure and material properties of the fabricated parts is a crucial focus in AM research. Texture is a vital factor in almost every modern industry. This study first proposed a physics-based model to foreshadow the multi-phase crystallographic orientation distribution in Ti-6Al-4V LPBF while considering the part boundary conditions due to the importance of part geometry in real industry. The thermal distribution obtained from this function operates as the information for the single-phase crystallographic texture model. In this model, we forerun and validate the orientations of single-phase materials utilizing three Euler Angles with the principles of CET and thermodynamics, as well as the intensity of the texture by approximating them with published results. Then, we transform the single-phase texture into a dual-phase texture in Bunge calculation, illustrating visualized by pole figures of both BCC and HCP phases. The tendency and appearances of both BCC and HCP phases in pole figures predicted agree well with the experimental results. This texture evolution model provides a new paradigm for future researchers to model the texture or microstructure evolution semi-analytically and save many computational resources in a real-world perspective. Others have not yet done this work about simulating the multi-phase texture in an analytical approach, so this work bridges the gap in this field. Furthermore, this paper establishes the foundation for future research on materials properties affected by microstructure or texture in academic and industrial environments. The precision and dependability of the results obtained through this method make it a valuable tool for ongoing research and advancement. Full article

This study investigated the influence of printing parameters and strategies on the morphological characteristics of austenitic stainless steel beads deposited on carbon steel substrates, using plasma directed energy deposition (DED). The experimental setup varied the welding current, wire feed speed, and torch travel speed, and we analyzed three printing strategies: simple-linear, overlapping, and oscillating. Moreover, advanced 3D scanning and computational analysis were used to assess the key morphological features, including bead width and height. The results showed that the computational model developed by using parabolic assumptions accurately predicted the geometric outcomes of the overlapping beads. The oscillating printing strategy was the one that showed improved morphological uniformity and bead substrate wettability, so these features were used for multi-layer component manufacturing. The use of equivalent wavelength–amplitude values resulted in maximum combinations of bead height and width. Moreover, cost-effective carbon steel substrates were feasibly used in microstructural and elemental analyses, with the latter ones confirming the alignment of the bead composition with the wire-fed material. Overall, this study provides practical insights for optimizing plasma DED processes, thus enhancing the efficiency and quality of metal component manufacturing. Full article

Complex phase steels, known for their high levels of conformability, energy absorption, and deformation capacity, are among the more advanced high-strength steels. The objective of this study was to compare the mechanical properties of CPW 800-class complex phase steels, with and without laser welding. The analysis involved determining tensile strength, yield strength, elongation, and area reduction through tensile tests, in scenarios both with and without laser welding. Additionally, the number of cycles was assessed via fatigue tests, and absorbed energy was measured using impact tests. The non-parametric Kruskal–Wallis test, at a 5% significance level, revealed that tensile strength, yield strength, area reduction, and absorbed energy were statistically similar regardless of laser welding. However, elongation and the number of cycles showed significant differences. The fractured surface from axial fatigue tests exhibited ductile characteristics, with the additional presence of dimples or alveoli. Full article

The paper presents a comparative study of the erosion wear resistance of WC-10Co4Cr, Cr₃C₂-25NiCr and martensitic stainless steel (SS) coatings deposited onto an AlSi7Mg0.3 (Al) alloy substrate by high-velocity air‒fuel (HVAF) spraying. The influence of the abrasive type (quartz sand or granite gravel), erodent attack angle, thickness, and microhardness of the coatings on their and Al substrate’s wear resistance was comprehensively investigated under dry erosion conditions typical for fan blades. The HVAF-spraying process did not affect the Al substrate’s structure, except for when the near-surface layer was 20‒40 μm thick. This was attributed to the formation of a modified Al-Si eutectic with enhanced microhardness and strength in the near-substrate area. Mechanical characterization revealed significantly higher microhardness values for the cermet WC-10Co4Cr (~12 GPa) and Cr₃C₂-25NiCr (~9 GPa) coatings, while for the SS coating, the value was ~5.7 GPa. Erosion wear tests established that while Cr₃C₂-25NiCr and SS coatings were more sensitive to abrasive type, the WC-10Co4Cr coating exhibited significantly higher wear resistance, outperforming the alternatives by 2‒17 times under high abrasive intensity. These findings highlight the potential of HVAF-sprayed WC-10Co4Cr coatings for extending the service life of AlSi7Mg0.3-based fan blades exposed to erosion wear at normal temperatures. Full article

This paper investigates the potential of utilizing lubricated liquid carbon dioxide (LCO₂ + MQL) as an alternative to conventional flood cooling in grinding operations. This approach could facilitate a transition towards fossil-free production, which is a significant challenge in industry. The alternative cooling–lubrication method relies on pre-mixed LCO₂ and oil and a single-channel minimum quantity lubrication (MQL) delivery method, which has already demonstrated potential in machining with geometrically defined cutting edges. However, this method has been less explored in grinding. This study primarily evaluates the grindability of AISI 4140 steel, examining surface roughness, residual stresses, microhardness, grinding forces, and specific energy for different cooling–lubrication methods. The results indicate that LCO₂ + MQL is capable of attaining surface roughness and microhardness that is comparable to that of conventional flood cooling, especially in the case of less aggressive, finish grinding. Nevertheless, the presence of higher tensile residual stresses in rough grinding suggests that the cooling capability may be insufficient. While the primary objective was to evaluate the technological viability of LCO₂ + MQL in terms of grindability, a supplementary cost-effectiveness analysis (CEA) was also conducted to assess the economic feasibility of LCO₂ + MQL in comparison to conventional flood cooling. The CEA showed that the costs of both the cooling–lubrication methods are very similar. In conclusion, this study offers insights into the technological and economic viability of LCO₂ + MQL as a sustainable cooling–lubrication method for industrial grinding processes. Full article

To address issues such as large surface roughness, coarse grains, and poor mechanical properties in low-carbon steel parts produced through wire arc additive manufacturing (WAAM), this paper proposes a method combining multi-directional incremental forming with the WAAM process. The additive manufacturing and cooling processes were simulated using the finite element software Abaqus to analyze the effects of multi-directional additive manufacturing on the stress field of the fabricated parts. The results indicate that after multi-directional incremental forming, the residual stress in the fabricated parts shifts from tensile stress to compressive stress, thereby reducing the risk of defects such as cracks. Moreover, the equivalent plastic strain of the processed parts increases, and the surface microhardness improves, with the most significant impact of multi-directional incremental forming observed in the contact area of the rolling head. Full article

Additive manufacturing has a strong potential to produce sound metal–polymer joints using controlled polymer deposition on a metallic substrate. In this way, this study aimed to explore the morphological and mechanical properties of metal–polymer joints produced through material-extrusion-based AM using a pin-based macro-mechanical interlocking mechanism. Joints were fabricated with polylactic acid deposited onto a heated aluminium alloy substrate to form the connection. The optimisation process was focused on improving the printing parameters and pin geometries to reduce voids and enhance joint integrity. The results indicate that optimised samples exhibit superior mechanical resistance, achieving a maximum load improvement with an overall strength increase of 368.97% compared to non-optimised joints. A combined pin geometry (50% cylindrical, 50% conical) was found to be the most effective. Morphological analysis confirmed uniform polymer deposition, ensuring reliable joint performance. These findings underscore the critical role of geometric optimisation in enhancing the strength and durability of metal–polymer joints in AM applications. Full article

This study investigates the feasibility and effectiveness of two non-destructive testing methods, active thermography and shearography, on 3D-printed thermoplastic (TP) composites reinforced with continuous carbon fiber. Artificial defects were introduced into the composite plate to benchmark the detection capabilities of these non-destructive testing techniques (NDT). Active thermography produced a thermogram that highlighted defects through variations in surface temperature. Although effective for identifying defects ranging from 3 to 10 mm in size at four different depths, specifically 1 mm, 1.25 mm, 1.5 mm, and 1.75 mm, through the thickness of a 2.8 mm plate, the method encountered some limitations. It faced challenges in detecting deeper defects and accurately determining their shapes. Shearography, which utilizes fringe pattern distortions to detect surface displacement anomalies, successfully identified near-surface defects within the same size range. However, it required more expertise for accurate interpretation and struggled with detecting smaller and deeper defects. The complementary strengths and limitations of these methods suggest that employing both could offer a more comprehensive solution for defect detection in 3D-printed TP composites. Full article

This study explores the flexural behavior of continuous fiber-reinforced composite sandwich structures built entirely using material extrusion additive manufacturing. The continuous fiber additive manufacturing system used in this study works sequentially, thus enabling the addition of fiber reinforcement just in the face sheets, where it is most effective. Three-point bending tests were carried out on sandwich panel specimens built using thermoplastic reinforced with continuous glass fiber to quantify the effect of fiber reinforcement and infill density in the flexural properties and failure mode. Sandwich structures containing continuous fiber reinforcement had higher flexural strength and rigidity than unreinforced sandwiches. On the other hand, an increase in the lattice core density did not improve the flexural strength and rigidity. The elastic modulus of fiber-reinforced 3D-printed sandwich panels exceeded the predictions of the analytical models; the equivalent homogeneous model had the best performance, with a 15% relative error. However, analytical models could not correctly predict the failure mode: wrinkle failure occurs at 75% and 30% of the critical load in fiber-reinforced sandwiches with low- and high-density cores, respectively. Furthermore, no model is currently available to predict interlayer debonding between the matrix and the thermoplastic coating of fiber layers. Divergences between analytical models and experimental results could be attributed to the simplifications in the models that do not consider defects inherent to additive manufacturing, such as air gaps and poor interlaminar bonding. Full article

This paper proposes a procedurefor multi-criteria calibration of a thermo-mechanical model for numerical simulation of welding in the space vacuum. A finite-element model of a steel plate is created. Experimental and computational data are obtained. An inverse problem is formulated for the vector identification of five calibration parameters from the heat-flow model. They are evaluated for adequacy with controlled accuracy according to four criteria. An optimization problem is solved using a two-step interactive procedure. The parameter space studying method (PSI) has been applied to the study of multidimensional regions by means of quasi-uniform sounding. A Pareto-optimal set is defined. It is used to determine reduced ranked Pareto subsets by μ-selection. Salukvadze optimum is also determined. Full article

This study investigates the effects of incorporating glass microspheres (GMSs) as fillers in carbon fabric–epoxy composites (CFECs) on their degradation behavior under environmental conditions such as moisture and ultraviolet rays. The GMS-filled composites were subjected to accelerated ageing and evaluated using dynamic mechanical analysis (DMA), the Charpy impact test, and inter-laminar shear strength (ILSS) tests. The results indicate that the addition of GMS fillers significantly improves the stiffness and viscoelastic behavior of the composites. However, the impact strength of the composites decreases with the addition of GMS fillers and accelerated ageing. The ILSS results demonstrate that the addition of GMS fillers improved the interfacial bonding between the carbon–epoxy matrix and fillers. This study provides insights into the mechanical properties of GMS-filled carbon–epoxy composites. Full article

Throughout recent years, the implementation of nanoparticles into the microstructure of additively manufactured (AM) parts has gained great attention in the material science community. The dispersion strengthening (DS) effect achieved leads to a substantial improvement in the mechanical properties of the alloy used. In this work, an ex situ approach of powder conditioning prior to the AM process as per a newly developed fluidized bed reactor (FBR) was applied to a titanium-enriched variant of the NiCu-based Alloy 400. Powders were investigated before and after FBR exposure, and it was found that the conditioning led to a significant increase in the TiN formation along grain boundaries. Manufactured to parts via laser-based powder bed fusion of metals (PBF-LB/M), the ex situ FBR approach not only revealed a superior microstructure compared to unconditioned parts but also with respect to a recently introduced in situ approach based on a gas atomization reaction synthesis (GARS). A substantially higher number of nanoparticles formed along cell walls and enabled an effective suppression of dislocation movement, resulting in excellent tensile, creep, and fatigue properties, even at elevated temperatures up to 750 °C. Such outstanding properties have never been documented for AM-processed Alloy 400, which is why the demonstrated FBR ex situ conditioning marks a promising modification route for future alloy systems. Full article

Thermal simulation is essential in wire-arc-directed energy deposition (W-DED) to accurately estimate temperature distributions, impacting residual stress and distortion in components. Proper calibration of simulation models minimizes inaccuracies caused by varying material properties, machine settings, and environmental conditions. The lack of standardized calibration methods further complicates thermal predictions. This paper introduces a novel calibration method integrating both machine learning, as the high-fidelity (HF) model, and response surface modeling, as the low-fidelity (LF) model, within a multi-fidelity (MF) framework. The approach utilizes Bayesian optimization to effectively explore the search space for optimal solutions. A two-tiered model employs the LF model to identify feasible regions, followed by the HF model to refine calibration parameters, such as thermal efficiency (η), convection coefficient (h), and emissivity (ε), which are difficult to determine experimentally. A three-factor Box–Behnken design (BBD) is applied to explore the design space, requiring only thirteen parameter configurations, conserving resources and enabling robust model training. The efficacy of this MF model is demonstrated in multi-layer W-DED calibration, showing strong alignment between experimental and simulated temperatures, with a mean absolute error (MAE) of 7.47 °C. This method offers a replicable framework for broader additive manufacturing processes. Full article

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing. Full article

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications. Full article

This study looks at how welding intensity, speed, voltage, and stick-out affect the structural and mechanical characteristics of metal inert gas (MIG) welding on SUS 201 stainless steel and C20 steel. The Taguchi method is used to optimize the study’s experiment findings. The results show that the welding current has a more significant effect on the tensile test than the welding voltage, stick-out, and welding speed. Welding voltage has the lowest influence. In addition to the base metals’ ferrite, pearlite, and austenite phases, the weld bead area contains martensite and bainite microstructures. The optimal parameters for the ultimate tensile strength (UTS), yield strength, and elongation values are a 110 amp welding current, 15 V of voltage, a 500 mm.min⁻¹ welding speed, and a 10 mm stick-out. The confirmed UTS, yield strength, and elongation values are 452.78 MPa, 374.65 MPa, and 38.55%, respectively, comparable with the expected value derived using the Taguchi method. In the flexural test, the welding current is the most critical element affecting flexural strength. A welding current of 110 amp, an arc voltage of 15 V, a welding speed of 500 mm.min⁻¹, and a stick-out of 12 mm are the ideal values for flexural strength. The flexural strength, confirmed at 1756.78 MPa, is more than that of the other samples. The study’s conclusions can offer more details regarding the dissimilar welding industry. Full article

The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments. Full article

A complementary characterisation of the Al-alloy EN AW-5454 was carried out, intended for obtaining the laser hybrid welding parameters of subassemblies in the automotive industry. The investigation included a microstructural examination and the determination of the alloy’s properties using several analytical methods (HV5 hardness measurement, tensile test, Charpy impact toughness, fracture mechanics analysis). Samples were prepared in the longitudinal and transverse directions of a cold-rolled sheet of EN AW-5454 with thicknesses of 3.5 mm and 4 mm. The measured hardness on the thinner sheet was 5% higher than on the thicker sheet. The tensile and yield strength were nominal, while the elongations were smaller by 2.2–3.2% for the longitudinal samples and by 2.7–13.7% for the transverse samples. The smaller deviations from the nominal values are for the thinner sheet metal. A precise topographical analysis showed the brittle fractures of the samples. The Charpy impact toughness results on the thicker plate showed a 20% greater work needed to break it in the longitudinal direction than in the transverse direction. With the thinner sheet metal, 40% greater work was needed. SEM (scanning electron microscope) analysis has shown that the intermetallic Al₆(Mn,Fe) particles in the longitudinal samples were mostly intact, with evidence of tough areas on the upper part of the fracture, indicating a better toughness than the specimens in the transverse direction. More crushed intermetallic particles were observed at the fractures of the transverse samples, and their distribution appeared to be more oriented in the direction of rolling. Fracture mechanics SENB (single edge notch bending) tests and their analysis showed that the resistance of the material to crack propagation in the longitudinal sample was about 50% greater than that in the transverse sample. SEM analysis of the fractures showed that the state of the intermetallic particles in the fracture mechanics testing and the fracture mechanism differed from the one in the Charpy fractures. Full article

This paper aims to elucidate the state-of-the-art, prevailing priorities, and the focus of the industry, and identify both limitations and potential gaps regarding industrial robots and collaborative robots (cobots). Additionally, it outlines the advantages and disadvantages of cobots compared to traditional industrial robots. Furthermore, three novel factors are introduced in this survey as metrics to evaluate the efficiency and performance of industrial robots and cobots. To achieve these purposes, a statistical analysis and review of commercial articulated industrial robots and cobots are conducted based on their documented specifications, such as maximum payload, weight, reach, repeatability, average maximum angular speed, and degrees of freedom (DOF). Additionally, the statistical distributions of the efficiency factors are investigated to develop a systematic method for robot selection. Finally, specifications exhibiting strong correlations are compared in pairs using regressions to find out trends and relations between them, within each company and across them all. The investigation of the distribution of specifications demonstrates that the focus of the industry and robot makers is mostly on articulated industrial robots and cobots with higher reach, lower payload capacity, lower weight, better repeatability, lower angular speed, and six degrees of freedom. The regressions reveal that the weight of robots increases exponentially as the reach increases, primarily due to the added weight and torque resulting from the extended reach. They also indicate that the angular speed of robots linearly decreases with increasing reach, as robot manufacturers intentionally reduce the angular speed through reductive gearboxes to compensate for the additional torque required as the reach extends. The trends obtained from the regressions explain the reasons behind these interrelationships, the design purpose of robot makers, and the limitations of industrial robots and cobots. Additionally, they help industries predict the dependent specifications of articulated robots based on the specifications they require. Moreover, an accompanying program has been developed and uploaded on to GitHub, taking the required specifications and returning a list of proper and efficient robots sourced from different companies according to the aforementioned selection method. Full article

Avoiding downtime is one of the major concerns of manufacturing industries. In the era of connected industry, acquiring data has become cheaper than ever; however, turning that data into actionable insights for operators is not always straightforward. In this work, we present a manufacturing scenario involving a circular blade rubber cutting machine, where the goal is to minimize downtime. Historical cutting data are available, and the aim is to provide the machine operators with an intuitive tool that helps them reduce this downtime. This work demonstrates how, in an Industry 4.0 environment, data can be leveraged to minimize downtime. To achieve this, different survival model approaches are compared, a Health Index (HI) is developed, and the model deployment is analysed, highlighting the importance of understanding the model as a dynamic system in which the operator plays a key role. Full article

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS. Full article

Investment casting is a widely utilized casting technique that offers superior dimensional accuracy and surface quality. In this method, the wax patterns are employed in the layer-by-layer formation of a shell mold. As is customary, the patterns were created through the injection of molten or semi-solid wax into the die. The quality of the final casting is affected by the quality of the wax pattern. Furthermore, the filling of the die with wax can be associated with die-filling challenges, such as the formation of weld lines and misruns. In this study, the injection filling of the fluidity probe die with RG20, S1235, and S1135 pattern waxes was simulated using ProCast software. The thermal properties of the waxes, including thermal conductivity, heat capacity, and density across a wide temperature range, were determined with the assistance of a laser flash analyzer, a differential scanning calorimeter, and a dynamic mechanical analyzer. A favorable comparison of the acquired properties with those reported in the literature was observed. The Carreau model, which corresponds to non-Newtonian flow, was employed, and the parameters in the Carreau viscosity equation were determined as functions of temperature. Utilizing the thermal data associated with the wax patterns and the simulation outcomes, the interfacial heat transfer coefficients between the wax and the die were ascertained, yielding a value of 275–475 W/m²K. A strong correlation was observed between the experimental and simulated filling percentages of the fluidity probe across a wide range of injection temperatures and pressures. The analysis of the simulated temperature, fraction solid, viscosity, and shear rate in the wax pattern revealed that viscosity is a crucial factor influencing the wax fluidity. It was demonstrated that waxes with an initial high viscosity exhibit a low shear rate, which subsequently increases the viscosity, thereby hindering the wax flow. Full article

This study investigates the impact of incorporating TiC and TiB₂ inoculants on the microstructure and corrosion performance of an arc-directed energy-deposited PH 13-8Mo martensitic stainless steel. The microstructural characterizations revealed partial dissolution of the incorporated ceramic-based nanoparticles, resulting in the formation of in situ TiC phase in the TiC-inoculated sample, while TiC and chromium-enriched M₃B₂ phases were formed in the TiB₂-inoculated sample. Further investigations into the electrochemical response of the fabricated samples confirmed that the applied inoculation strategy slightly enhanced the corrosion resistance of the alloy, offering a valuable advantage for in-service performance for applications in harsher environments. The slight improvement in the corrosion resistance of the inoculated samples was found to be attributed to the formation of a higher fraction of low-angle grain boundaries and enhanced retained austenite content in the microstructure. However, it is essential to note that the formation of chromium-enriched M₃B₂ phases in the TiB₂-inoculated sample led to a slight deterioration in its corrosion resistance compared to the TiC-inoculated counterpart. Full article

The use of an electron beam (EB) as a heating source in EB-based powder bed fusion (PBF-EB) has several limitations, such as reduced powder recyclability, short machine service intervals, difficulties with heating large areas and the limited processability of charge-sensitive powders. Near-infrared (NIR) heating was recently introduced as a feasible replacement and/or complement to EB heating in PBF-EB. This work further investigates the feasibility of using NIR to eliminate the need for a build platform as well as to enable easier repairing of parts in PBF-EB. NIR-assisted Ti-6Al-4V builds were successfully carried out by starting from a loose powder bed without using a build platform. The results do not only confirm that it is possible to eliminate the build platform by the aid of NIR, but also that it can be beneficial for the process cleanliness and improve the surface quality of built parts. Furthermore, a 430 stainless-steel (SS) component could be repaired by positioning it in a loose 316L SS powder bed using a fully NIR-heated PBF-EB process. Full article

This paper describes the principles of obtaining wear-resistant coatings based on titanium diboride that are deposited on the cutting tool for use in the machining of chromium–nickel alloys. The spark plasma sintering of samples from the TiB₂/Ti powder composition was studied, and the influence of sintering modes on the characteristics of the ceramic targets was analyzed. The regularities of the magnetron sputtering of sintered targets were revealed. The dependences of the physical and mechanical properties of coatings formed on hard alloy substrates on deposition conditions were established. The wear resistance of carbide samples with TiB₂-based coatings under friction-sliding conditions and coated carbide ball-end mills in milling Inconel 718 chromium–nickel alloy that is widely used in the industry was assessed. Full article

In metal additive manufacturing, reusing collected powder from previous builds is a standard practice driven by the substantial cost of metal powder. This approach not only reduces material expenses but also contributes to sustainability by minimizing waste. Despite its benefits, powder reuse introduces challenges related to maintaining the structural integrity of the components, making it a critical area of ongoing research and innovation. The reuse process can significantly alter powder characteristics, including flowability, size distribution, and chemical composition, subsequently affecting the microstructures and mechanical properties of the final components. Achieving repeatable and consistent printing outcomes requires powder particles to maintain specific and consistent physical and chemical properties. Variations in powder characteristics can lead to inconsistencies in the microstructural features of printed components and the formation of process-induced defects, compromising the quality and reliability of the final products. Thus, optimizing the powder recovery and reuse methodology is essential to ensure that cost reduction and sustainability benefits do not compromise product quality and reliability. This study investigated the impact of powder reuse and particle size distribution on the microstructural and mechanical properties of Ti-6Al-4V specimens fabricated using a laser beam directed energy deposition technique. Detailed evaluations were conducted on reused powders with two different size distributions, which were compared with their virgin counterparts. Microstructural features and process-induced defects were examined using scanning electron microscopy and X-ray computed tomography. The findings reveal significant alterations in the elemental composition of reused powder, with distinct trends observed for small and large particles. Additionally, powder reuse substantially influenced the formation of process-induced defects and, consequently, the fatigue performance of the components. Full article

Heat storage is an emerging field of research, and, therefore, new materials with enhanced properties are being developed. Examples of phase change materials that provide high heat storage are inorganic salts and salt mixtures. They are commonly used for industrial applications due to their high operational temperature and latent heat. These parameters can be modified by combining different types of salts. This paper presents the experimental study of the impact of the composition of binary salts on their thermophysical properties. Unlike the literature data, this article provides a detailed analysis of the phase change process in both directions: solid–liquid and liquid–solid. The results indicate that the highest latent heat was observed for a 70% NaNO₃ content in the NaNO₃–KNO₃ mixture. Therefore, when this salt is used for heat storage, the most favorable choice is a 70:30 ratio, which provides the highest heat storage density and the lowest phase transition temperature. In the case of the NaNO₃–NaNO₂ mixture, the highest value of latent heat occurs for a ratio of 80:20, resulting in phase transition temperatures of 267.0 °C for the solid–liquid transition, and 253.5 °C for the liquid–solid transition. For heat storage applications, it is recommended to use pure NaNO₂ salt instead of the NaNO₃–NaNO₂ mixture. Full article

In large-scale machine tool applications, the presence of low structural natural frequencies limits the cutting capabilities of the machine. The machine tool joints interact with the structural mode shapes, hence, the feed drive system characteristics can significantly influence the resultant dynamics at the cutting point. This paper investigates the effect of guideway non-linear friction and feed drive motion control parameters on chatter stability predictions. Field experimentation on seven machines reveals substantial differences between in-motion and idle dynamics, leading to errors in traditional process stability predictions. By using a one-degree-of-freedom model that incorporates non-linear friction and controller forces together with motion commands, the effect of axis motion on machine tool dynamics is analyzed. Later, the feed and force non-linearities are studied in a large-scale machine tool using traditional and alternative dynamic characterization techniques. The findings demonstrate that both feed and force non-linearities influence the frequency response functions at the cutting points, ultimately affecting the accuracy of process stability predictions. Proper selection of feed drive control parameters reduces the cutting point compliance, improving machine tool productivity by up to 50%. Full article

The purpose of this research was to create a predictive model for a medium-speed wire electrical discharge machine (WEDM) utilizing an artificial neural network (ANN). Medium-speed WEDM experiments were developed based on the I-optimal mixture design for machining, the Inconel 718 superalloy. During the experiment, the input parameters were the spark ontime, spark offtime, wire feed, and current, with the material removal rate (MRR) and surface roughness (Ra) selected as performance indicators. The ANN model was trained on experimental data and built using a feed-forward backpropagation neural network with a (4-8-2) structure and the Bayesian regularization (BR) learning approach. The model correctly predicted the relationship between the medium-speed WEDM’s primary process parameters and machining performance. An integrated ANN model and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) were used to determine the ideal parameters for the MRR and Ra, resulting in a set of Pareto-optimal solutions. The confirmation experiment revealed that the mean prediction error between the experimental and ideal solutions had a maximum error percentage of 1% for the MRR and 2% for the Ra, which are within acceptable ranges. This showed that the best process–parameter combinations were better for the MRR and Ra. Full article