J. Manuf. Mater. Process., Volume 8, Issue 5 (October 2024) – 42 articles

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

This study examines the dry milling process of a rare-earth-based magnesium alloy, emphasizing the optimization of the milling parameters and their impact on the surface quality, cutting forces, and the rate of material removal. The objective is to improve our comprehension of the milling behavior of the Mg-Gd-Y-Er alloy. The Taguchi technique is adopted to formulate the experimental design. This study methodically investigates the influence of heat treatment (T4 and T6) on milling performance, and the effects of speed, feed rate, and depth of cut. The output variables considered for this investigation are the surface roughness (Ra, Rz, Sa, and Sz), material removal rate (MRR), and cutting force. To optimize the milling parameters and achieve superior outcomes, the multi-objective optimization technique TOPSIS is used. At a feed rate of 150 mm/min, a spindle speed of 1500 rpm, and a depth of cut of 1 mm, the T4-treated sample exhibits a minimum surface roughness value of 0.0305 µm. The highest resultant force values of 96.4416 N and 176.1070 N for 200 °C and 225 °C T6-treated alloys are obtained by combining process parameters such as a spindle speed of 1500 rpm, a feed rate of 50 mm/min, and a depth of cut of 1.5 mm. Furthermore, the maximum closeness coefficient value is achieved by combining a spindle speed of 1000 to 1500 rpm, a feed rate of 150 mm/min, and a depth of cut of 0.5 mm to 1 mm. The closeness coefficient value is significantly influenced by the most significant process parameters, as indicated by the ANOVA results. Full article

Laser wire-feed metal additive manufacturing (LWAM) is an innovative technology that shows many advantages compared with traditional manufacturing approaches. Despite these advantages, its industrial adoption is limited by complex parameter management and inconsistent process quality. To address these issues and improve geometric accuracy, this study explores how process parameters influence bead geometry. We conducted a parameter study varying laser power, wire feed rate, traverse speed, and welding angle. Using a full factorial design with a central composite design methodology, we assessed bead height and width. This allowed us to develop a model to estimate ideal process parameters. The findings offer a detailed analysis of parameter interactions and their effects on bead geometry, aiming to enhance geometric accuracy and process stability in LWAM. Moreover, we have evaluated the proposed process parameters from our developed model, which showed a significant enhancement to the overall quality. This was validated via printing a single layer and multi-layer structures. The quality of the final predicted sample using the proposed method was improved by 40% compared to the best sample produced for the Design of Experiment trials. Full article

Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, small-batch, and high-precision parts processing requirements. To solve this problem, this paper proposes a machining quality prediction method based on the machining error transfer network and the grey neural network. Initially, by constructing a processing error transfer network, the error transfer law in part processing is described, and the PageRank algorithm and the influence degree of the nodes are used to determine the critical quality features. Additionally, the problem of low prediction accuracy due to small sample data and multiple coupling relationships is solved using the grey neural network algorithm, and a high accuracy prediction of critical quality features is achieved. Finally, the effectiveness and reliability of the method are verified by the case of medium-speed marine diesel engine fuselage processing. The results indicate that this method not only effectively identifies critical quality features in the machining process of complex parts, but it also maintains a high predictive accuracy for these features, even with small samples and limited data. Full article

Die-sink electric discharge machining (EDM) is essential for shaping complex geometries in hard-to-machine materials. This study aimed to optimize key input parameters, such as the discharge current, gap voltage, pulse-on time, and pulse-off time, to enhance the EDM performance by maximizing the material removal rate while minimizing the surface roughness, residual stress, microhardness, and recast layer thickness. AISI 316L stainless steel was chosen due to its industrial relevance and machining challenges, while a Ti-6Al-4V-SiCp composite electrode was selected for its thermal resistance and low wear. Using Taguchi’s L₂₇ orthogonal array, this study minimized the trial numbers, with analysis of the variance-quantifying parameter contributions. The results showed a maximum material removal rate of 0.405 g/min and minimal values for the surface roughness (1.95 µm), residual stress (1063.74 MPa), microhardness (244.8 Hv), and recast layer thickness (0.47 µm). A second-order model, developed through a response surface methodology, and a feed-forward artificial neural network enhanced the prediction accuracy. Multi-response optimization using desirability function analysis yielded an optimal set of conditions: discharge current of 5.78 amperes, gap voltage of 90 volts, pulse-on time of 100 microseconds, and pulse-off time of 15 microseconds. This setup achieved a material removal rate of 0.13 g/min, with reduced surface roughness (2.46 µm), residual stress (1518.46 MPa), microhardness (259.01 Hv), and recast layer thickness (0.87 µm). Scanning electron microscopy further analyzed the surface morphology and recast layer characteristics, providing insights into the material behavior under EDM. These findings enhance the understanding and optimization of the EDM processes for challenging materials, offering valuable guidance for future research and industrial use. Full article

Understanding shock wave behavior in supersonic flow environments is critical for optimizing the aerodynamic performance of turbomachinery components. This study introduces a novel transonic linear cascade design, focusing on advanced blade manufacturing and experimental validation. Blades were 3D-printed using Inconel 625, enabling tight control over the geometry and surface quality, which were verified through extensive dimensional accuracy assessments and surface finish quality checks using coordinate measuring machines (CMMs). Numerical simulations were performed using Ansys CFX with an implicit pressure-based solver and high-order numerical schemes to accurately model the shock wave phenomena. To validate the simulations, experimental tests were conducted using Schlieren visualization, ensuring high fidelity in capturing the shock wave dynamics. A custom-designed test rig was commissioned to replicate the specific requirements of the cascade, enabling stable and repeatable testing conditions. Experiments were conducted at three different inlet pressures (0.7-bar, 0.8-bar, and 0.9-bar gauges) at a constant temperature of 21 °C. Results indicated that the shock wave intensity and position are highly sensitive to the inlet pressure, with higher pressures producing more intense and extensive shock waves. While the numerical simulations aligned broadly with the experimental observations, discrepancies at finer flow scales suggest the need for the further refinement of the computational models to capture detailed flow phenomena accurately. Full article

Modern mechanical engineering faces high competition in global markets, which requires manufacturers of process equipment to significantly reduce production costs while ensuring high product quality and maximum productivity. Metalworking occupies a significant part of industrial production and consumes a significant share of the world’s energy and natural resources. Improving the technology of manufacturing parts with an emphasis on more efficient use of metalworking machines is necessary to maintain the competitiveness of the domestic machine tool industry. Hybrid metalworking systems based on the principles of multi-purpose integration eliminate the disadvantages of monotechnologies and increase efficiency by reducing time losses and intermediate operations. The purpose of this work is to develop and implement a hybrid machine tool system and an appropriate combined technology for manufacturing machine parts. Theory and methods. Studies of the possible structural composition and layout of hybrid equipment at integration of mechanical and surface-thermal processes were carried out, taking into account the basic provisions of structural synthesis and componentization of metalworking systems. Theoretical studies were carried out using the basic provisions of system analysis, geometric theory of surface formation, design of metalworking machines, methods of finite elements, and mathematical and computer modeling. The mathematical modeling of thermal fields and structural-phase transformations during HEH HFC was carried out in ANSYS (version 19.1) and SYSWELD (version 2010) software packages using numerical methods of solving differential equations of unsteady heat conduction (Fourier equation), carbon diffusion (2nd Fick’s law) and elastic–plastic behavior of the material. The verification of the modeling results was carried out using in situ experiments employing the following: optical and scanning microscopy; and mechanical and X-ray methods of residual stress determination. Formtracer SV-C4500 profilograph profilometer was used in the study for simultaneous measurement of shape deviations and surface roughness. Surface topography was assessed using a Walter UHL VMM 150 V instrumental microscope. The microhardness of the hardened surface layer of the parts was evaluated on a Wolpert Group 402MVD. Results and discussion. The original methodology of structural and kinematic analysis for pre-design studies of hybrid metalworking equipment is presented. Methodological recommendations for the modernization of multi-purpose metal-cutting machine tool are developed, the implementation of which will make it possible to implement high-energy heating with high-frequency currents (HEH HFC) on a standard machine tool system and provide the formation of knowledge-intensive technological equipment with extended functionality. The innovative moment of this work is the development of hybrid metalworking equipment with numerical control and writing a unique postprocessor to it, which allows to realize all functional possibilities of this machine system and the technology of combined processing as a whole. Special tooling and tools providing all the necessary requirements for the process of surface hardening of HEH HFC were designed and manufactured. The conducted complex of works and approbation of the technology of integrated processing in real conditions in comparison with traditional methods of construction of technological process of parts manufacturing allowed to obtain the following results: increase in the productivity of processing by 1.9 times; exclusion of possibility of scrap occurrence at finishing grinding; reduction in auxiliary and preparatory-tasking time; and reduction in inter-operational parts backlogs. Full article

Additive manufacturing (AM) methods have been gaining momentum because they provide vast design and fabrication possibilities, increasing the accessibility of state-of-the-art hardware through recent developments in user-friendly computer-aided drawing/engineering/manufacturing (CAD/CAE/CAM) tools. However, in comparison to the conventional manufacturing methods, AM processes have some disadvantages, including the machining precision and fabrication process times. The first issue has been mostly resolved through the recent advances in manufacturing hardware, sensors, and controller systems. However, the latter has been widely investigated by researchers with different toolpath planning perspectives. As a contribution to these investigations, the present study proposes a toolpath planning method for AM, which aims to provide highly continuous yet distance-optimized solutions. The approach is based on the utilization of the signed distance field (SDF), clustering, and minimization of toolpath distances among cluster centroids. The method was tested on various geometries with simple closed curves to complex geometries with holes, which provides effective toolpaths, e.g., with relative distance reduction percentages up to 16.5% in comparison to conventional rectilinear infill patterns. Full article

The friction/adhesion between the tool and chip is generally large in metal cutting, and it causes many problems such as high cutting energy/rough surface finish. To suppress this, cutting fluid and tool coating are used in practice, but they are high in energy/cost and environmentally unfriendly. Therefore, this paper investigates the extraordinarily high-speed cutting (EHS cutting) mechanics of mainly soft and highly heat-conductive materials and proposes their application to solve the friction/adhesion problem in an environmentally friendly manner. In order to clarify the EHS cutting mechanics, a simple analytical model is constructed and experiments are conducted with measurement of the cutting temperature and forces. As a result, the following points are clarified/found: (1) heat softening at the secondary plastic deformation zone rather than the primary plastic deformation zone, (2) friction coefficient drop to 0.170 in EHS cutting, and (3) gradually increasing trend of cutting temperature in EHS cutting. Finally, EHS cutting is applied to dry cutting of aluminum alloys with a non-coated carbide tool and compared to conventional wet cutting with a DLC-coated carbide tool, and it is shown that a coating/coolant can be omitted in this region to achieve environmentally friendly cutting. Full article

Selective laser sintering (SLS) is a bed fusion additive manufacturing technology that facilitates rapid, versatile, intricate, and cost-effective prototype production across various applications. It supports a wide array of thermoplastics, such as polyamides, ABS, polycarbonates, and nylons. However, manufacturing plastic components using SLS poses significant challenges due to issues like low strength, dimensional inaccuracies, and rough surface finishes. The operational principle of SLS involves utilizing a high-power-density laser to fuse polymer or metallic powder surfaces. This paper presents a comprehensive analysis of the SLS process, emphasizing the impact of different processing variables on material properties and the quality of fabricated parts. Additionally, the study explores the application of machine learning (ML) techniques—supervised, unsupervised, and reinforcement learning—in optimizing processes, detecting defects, and ensuring quality control within SLS. The review addresses key challenges associated with integrating ML in SLS, including data availability, model interpretability, and leveraging domain knowledge. It underscores the potential benefits of coupling ML with in situ monitoring systems and closed-loop control strategies to enable real-time adjustments and defect mitigation during manufacturing. Finally, the review outlines future research directions, advocating for collaborative efforts among researchers, industry professionals, and domain experts to unlock ML’s full potential in SLS. This review provides valuable insights and guidance for researchers in regard to 3D printing, highlighting advanced techniques and charting the course for future investigations. Full article

This experimental study was performed to assess whether applying a metal primer containing 10-MDP multiple times affected the repair shear bonding ability of base metal alloys to resin composites. Ten base metal alloys were randomly assigned to each group in the manner described, following multiple applications of a metal primer (Clearfil Ceramic Primer Plus), namely one to five applications, and no primer application as a negative control. On the specimens’ prepared surfaces, the resin composite was pushed into the mold and then light-activated for 40 s. The bonded samples were kept for 24 h at 37 °C in distilled water in an incubator. The shear bond strength was determined using a universal testing device. A stereomicroscope was used to determine the debonded surface. The one-way ANOVA and Tukey’s test were implemented to statistically analyze. The lowest shear bond strength was found in group 6 (6.14 ± 1.12 MPa), demonstrating a significant difference (p = 0.000) when compared to groups 1 to 5. The shear bond strength of group 3 was highest at 21.49 ± 1.33 MPa; there was no significant difference between group 3 and groups 4 and 5 (20.21 ± 2.08 MPa and 20.98 ± 2.69 MPa, respectively) (p = 0.773, p = 1.000, respectively). All fractured specimens in groups 1, 2, and 6 were identified as adhesive failure. Groups 3 and 4 exhibited the highest percentage of mixed failures. To achieve the repair shear bonding ability of base metal alloys to resin composites, the sandblasted base metal alloys should be coated with three applications of a metal primer before applying the adhesive agent. Full article