Advanced Manufacturing Processes Research Articles

Unveiling the mechanical and microstructural properties of SiC reinforced aluminum wires recycled from scraps by friction stir extrusion

Friction Stir Extrusion (FSE) has been proven as an effective solid-state process to directly recycle metal chips into high-quality wires/rods/tubes. However, the demand for Al-based composite wires with enhanced properties is substantial, and achieving a uniform distribution of the strengthening phase within the composite wire by establishing a robust metal/ceramic interface remains a significant challenge. In this study, AA6082 Aluminum alloy wires reinforced with 12.5 vol% SiC particles, exhibiting homogeneous particle distribution and superior mechanical properties, were successfully produced through the FSE technique. The investigation focused on examining the influence of processing parameters and the addition of SiC reinforcement on the microstructure and mechanical characteristics of the extruded wires. Scanning electron microscopy (SEM), Electron Backscatter Diffraction (EBSD), together with microhardness, bending, and tensile tests, were used to comprehensively analyze the resulting properties. The findings demonstrate a substantial enhancement in the wires' mechanical properties due to the SiC particles. Both tool rotation and tool force were varied during the experimental campaign. Notably, the reduction in porosity and grain refinement resulted in a 12.78 % increase in tensile strength and an 18.6 % improvement in microhardness compared to Al 6082 alloy extruded wires. Grain orientation analysis revealed a fully recrystallized microstructure with a weak texture. Furthermore, the study evaluated power consumption and surface roughness while assessing the feasibility of deposition, thereby highlighting the potential application of this technique in advanced additive manufacturing processes.

Quantification of delamination resistance data of FRP composites and its limits

Quantitative delamination resistance data of fibre-reinforced polymer-matrix (FRP) composites for quasi-static or cyclic fatigue loads are determined under different loading modes and load rates, respectively. Such data find use, e.g., in FRP materials’ development, materials’ selection, assessment of durability, or structural design. Round robins during test development yielded repeatability and reproducibility (coefficients of variation) of roughly 10 to 25%. This scatter has several different sources. Intrinsic material variability amounts to a few percent at best, at least for advanced manufacturing processes. This intrinsic scatter is essential for material comparisons and structural design. Measurement resolution specified in standardised test procedures yields a maximum of 4–5% variability. Most of the remaining scatter comes from other, extrinsic sources. Test operator actions, e.g., choice of test set-up, manual data acquisition or data analysis can yield extrinsic scatter. Damage mechanisms during delamination initiation and propagation act on the micro- and meso-scale, typically a few micrometer to a few hundred micrometer in size, with corresponding time-scales estimated to between a few tens of nanoseconds and a few microseconds. Defect size-scales are hence several orders of magnitudes lower than test specimen and structural scales, respectively. Predictive capability of models using such test data for structures are, therefore, limited. Major issues are up-scaling from straight beam-like specimens to larger shell-like structures, possibly with complex shape and varying thickness as well as from unidirectional fibre orientation to multidirectional lay-ups.

Advanced Manufacturing Processes

The advancement of engineering and the emergence of increasingly individualized and specific applications have heightened the need to develop advanced manufacturing processes capable of delivering all of the required characteristics [...]

A flexible and efficient calibration method for discrete element simulations of additive manufacturing in construction

Extrusion-based 3D concrete printing (E3DCP), as one of the most advanced Additive Manufacturing processes, is a hotspot in the construction industry. Using the Discrete Element Method (DEM), the E3DCP can be investigated with reduced prototyping- and trial costs. However, DEM parameter calibration is challenging in the accurate simulation of construction materials because they are hardly typical granular matters. In addition, the introduction of a novel extrusion-based process Near-Nozzle-Mixing calls for a more flexible, efficient, and reliable calibration procedure due to the useable wide variety of material models.In this study, DEM simulation parameters of mortar and its components (e.g. Poraver® and paste) were thoroughly calibrated based on the central composite design method. Notably, the specific shape indices of related substrates were identified in the study to achieve the precise calibration of Poraver® and paste. Moreover, the European standard-based Haegermann flow table test was applied in the calibration by quantifying mortar consistency. This work provides a holistic and efficient calibration approach for three typical and physically different construction materials, which can be universally explored in the DEM simulation of most materials in E3DCP.

Semi-supervised adaptive anti-noise meta-learning for few-shot industrial gearbox fault diagnosis

Abstract Real-time and accurate predictive maintenance of industrial equipment is fundamental for ensuring the safety and stability of advanced manufacturing processes. Current fault diagnosis methods based on data mining rely on a large number of labeled samples, and obtaining sufficient labeled data for diagnosing industrial equipment faults is challenging. Meta-learning can achieve the diagnosis of few-shot samples to a certain extent, but the effect is not ideal. Semi-supervision can effectively leverage a large number of unlabeled samples, which is of great practical significance for handling scenarios involving limited labeled samples. However, noise interference can occur when unlabeled samples appear that do not belong to known categories. Therefore, this study proposes adaptive semi-supervised meta-learning networks for noisy few-shot gearbox fault diagnosis. Firstly, a residual network with a Morlet Wavelet layer is used to extract signal features. Next, sample-level attention is defined to select unlabeled samples that are more similar to labeled sample prototypes, thereby reducing the influence of noisy samples. The adaptive metric is used to obtain the relational distance functions of labeled samples and unlabeled samples. Adaptive semi-supervised meta-learning networks uses unlabeled data to refine prototypes for better fault diagnosis. The effectiveness and anti-noise performance of the proposed method are verified by using two gearbox datasets with various few-shot noise scenarios.

Bridging length scales in hard materials with ultra-small angle X-ray scattering - a critical review.

Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments.

Evolution of sustainable wearables: integrating cutting-edge techniques for future textile innovation

Purpose Sustainable textiles have become imperative in mitigating the adverse environmental and social impacts of the textile industry. This paper aims to synthesize recent advancements and key considerations in sustainable textile development, emphasizing their role in promoting environmental stewardship, social responsibility and economic viability. Design/methodology/approach The literature search has been conducted by identifying and articulating the previous studies related to integrating the latest cutting-edge techniques with functional textiles. Findings Future-generation textiles (FGTs), which incorporate state-of-the-art developments in materials, technologies and functionalities, herald a paradigm-shifting period in the textile industry. FGTs mark a new era in this dynamic world by igniting conversations about their mechanisms, problems, progress to date and potential future applications. This investigation covers a wide range of topics, including wearable electronics, nanotechnology, 3D printing, recycling, machine learning and energy harvesting. Key components include sustainability, functionality, intelligent integration, advanced manufacturing processes and multifunctionality. The paper highlights the potential benefits of smart textiles, wearable technology, improved performance and sustainability through advances in customization and security. Originality/value It is an original review work. This paper will be helpful for manufacturers and researchers in the smart wearable textile sector in developing innovative techniques for multifunctional garments by integrating cutting-edge technology.

Selective laser sintering and spark plasma sintering of (Zr,Nb,Ta,Ti,W)C compositionally complex carbide ceramics

AbstractTwo advanced manufacturing processes, spark plasma sintering (SPS) and selective laser sintering (SLS), have been developed for synthesis of (Zr,Nb,Ta,Ti,W)C compositionally complex carbide (CCC) via reactive sintering of a powder mixture of constitute monocarbides. X‐ray diffraction analysis confirmed that the single‐phase CCC can be formed by both SPS and SLS. While a homogenous microstructure with uniform metal element distributions was developed during SPS, three‐layer microstructures with a thin TiC‐rich layer and two TaC‐rich layers along with a TiO2‐rich surface layer containing W nanoparticles were formed during SLS. In addition, cellular structures with W, Zr, and Ti element segregation and dislocations on cell boundaries were observed in the SLS‐CCC sample, indicating the effect of nonequilibrium conditions on microstructure formation during laser melting followed by rapid cooling and solidification process. Compared to the SPS‐CCC sample, the SLS‐CCC showed enhanced hardness and reduced thermal conductivity, which may be related to their unique cellular structures.

Friction Stir Channeling in Heat Sink Applications: Innovative Manufacturing Approaches and Performance Evaluation

The fabrication of compact heat exchangers with precisely designed micro- and mini-channels is crucial for enhancing the efficiency of thermal management systems. Friction stir channeling (FSC) emerges as a cost-effective advanced manufacturing process to create complex integral channels, offering channel shape and size flexibility. This review article highlights the pivotal role of processing parameters in channel formation and maintaining their integrity, necessitating a comprehensive understanding of material flow dynamics. A rigorous assessment has been conducted on the channel under mechanical stresses, including tension, bending, and fatigue. The paper emphasizes the potential of FSC to revolutionize heat sink applications by exploring the fundamental concepts, governing parameters, ongoing enhancements in tool design, microstructural and mechanical properties, and heat transfer performance.

Thermal optimization of buoyancy driven radiative engine-oil based viscous hybrid nanofluid flow observing the micro-rotations in an inclined permeable enclosure

Thermal enhancement of engine oil plays an important role in a variety of industrial applications including heat exchangers, automotive cooling systems, renewable energy systems, biomedical devices, and advanced manufacturing processes. Considering the high demand in industry, present investigation explores thermal optimization of engine oil contained in a porous inclined cavity involving dispersion of hybrid nanoparticles along with inclusion of micropolar fluid flow phenomena. Numerical analysis is performed on the micropolar nanofluid containing iron oxide and molybdenum disulfide while considering the effect of thermal radiation, buoyancy force, viscous dissipation, magnetic field’s strength, porosity, and vortex viscosity. Numerical solutions of the dimensionless governing equations are provided through utilization of FVM. The coupling of velocity and pressure terms was achieved through implementation of SIMPLE algorithm. The results are illustrated through streamlines, contours, velocity, temperature profiles and heat flux. The outcomes revealed that an augment in Hartmann number and thermal radiation parameter results in higher heat flux rate. The incrementation in Hartmann number disrupted the flow resulting a reduction in the size of major vortices and similar trend is noted in case of temperature profile for the said parameters. The outcomes complement the continuing efforts to improve the thermal management of various applications and offer insightful information for optimizing engine oil compositions.

A reality-augmented adaptive physics informed machine learning method for efficient heat transfer prediction in laser melting

Physics informed neural network (PINN) method is proposed to alleviate problems in many science and engineering scenarios when data-collection is difficult, or traditional numerical calculations are lack of convenience because more time-consuming numerical calculations are required whenever one or more parameters of the process is changed. However, for advanced manufacturing processes like laser melting (LM), a basis of laser metal additive manufacturing, involve many complex physical phenomena (e.g. melting, convection, solidification, vaporization and interface evolution), the full equations of which are too complex to be solved by current PINN. This paper proposes a reality-augmented adaptive physics informed machine learning (RAA-PIML) method to meet the objectives of precise and fast predictive LM. It applies adaptive function, integrating heat transfer law and boundary condition equations to loss function, which has fast convergence and strong physics-based constraints. The reality-augmented data are acquired by few experiments and an accurate 3D heat transfer model covering turbulence. The conducted experimental results demonstrate that the developed solution exhibits higher efficiency than simulation, and superior performance than state-of-the-art methods, which shows outstanding potential in industry applications.

ANALYSIS OF THE APPLICATION OF MODERN HERMETIC TECHNIQUES OF POLYMER MATERIALS IN HYDRAULIC MACHINES

The application of modern hermetic techniques in polymer materials for hydraulic machines is a significant advance in engineering design and manufacturing. This analysis examines the implications and benefits of using these techniques in hydraulic systems. By using polymeric materials and hermetic sealing solutions with special properties, hydraulic machines can achieve better efficiency, durability and durability. The selection of suitable polymer materials such as polytetrafluoroethylene (PTFE) and the use of advanced manufacturing processes such as injection molding enable the production of precision seals and components with increased wear and corrosion resistance. In addition, the lightweight structure of polymer materials helps reduce weight and cost while maintaining performance standards. Customization options provided by hermetic methods enable optimization of material usage and creation of custom solutions for various hydraulic applications. In addition, the environmental impact is reduced by using recyclable and biodegradable polymers. However, issues such as material compatibility and long-term performance under extreme conditions require extensive testing and verification. Overall, the application of modern hermetic techniques to polymeric materials provides a promising way to improve hydraulic machine performance, reduce costs, and promote environmental sustainability in engineering applications. The integration of modern hermetic technology into polymer materials has revolutionized the design and functionality of hydraulic machines. Keywords: hermetic techniques, hydration system, polymer materials, hydraulic machine, durability.

Developments and prospects of additive manufacturing for thermoelectric materials and technologies

In view of the increased demand for electricity and the associated environmental and financial concerns, there is an urgent need to develop technological solutions that can improve the efficiency of engineering systems and processes. Thermoelectric (TE) technologies, with their capability of direct conversion of thermal energy into electrical energy, are promising technologies for green power generation through using them as energy harvesting devices for waste heat recovery in industrial processes and power generation systems.To date, TE technologies are still not commercialized on a large scale due to various economic and technical obstacles. The majority of previous research on TE technologies concentrated on improving the TE properties, such as electronic transport and figure-of-merit, while limited attempts were made to identify the best material processing techniques or reduce the cost of manufacturing. Conventional Manufacturing (CM) of TE materials and devices is multi-stage, complex, labour-intensive, time-consuming, and has high energy requirements. Thus, manufacturing challenges are considered key contributors toward limited industrial adoption of TE technologies.The rapid advent of advanced Additive Manufacturing (AM) processes, in recent years, caused dramatic changes in engineering design thinking and created opportunities to solve manufacturing challenges. With its significant capabilities, AM can be the route to address the shortcomings of CM of the thermoelectric technologies. In this regard, this paper presents an in-depth review of the literature studies on using AM technologies, such as selective laser melting, fused deposition modelling, direct ink writing, stereo lithography, etc., for manufacturing TE materials and devices. The benefits and challenges of each AM technology are discussed to identify their merits and the required future research. This paper demonstrates the role of AM in advancing green materials and technologies for solving some of the outstanding energy and environmental issues.

Effects of homogenization and deep cryogenic treatments on microstructure and mechanical property of D2 tool steel fabricated by laser direct energy deposition

The integration of laser direct energy deposition (L-DED) for manufacturing high-hardness D2 tool steel components presents a promising avenue for addressing the increasing complexity of product geometries demanded by modern industry. However, the inherent variability in microstructure and mechanical properties induced by L-DED across the build height necessitates the employment of post-processing techniques to achieve material homogeneity. This study explores the synergistic application of homogenization and deep cryogenic treatment (DCT) as a novel post-processing strategy aimed at mitigating microstructural inhomogeneities and enhancing the mechanical properties of L-DED-fabricated D2 tool steel. Initial analysis of the as-built samples revealed a microstructure characterized by a fine network of eutectic carbides enveloping an FCC matrix, which imposed constraints on martensite transformation during DCT. Homogenization was found to effectively alleviate these constraints, promoting compositional and microstructural uniformity and enabling a more consistent transformation to martensite across the samples upon subsequent DCT. The strategic combination of homogenization and DCT resulted in achieving uniformly distributed high hardness levels exceeding 750 HV throughout the entire sample, regardless of their initial positions within the build. This study demonstrates the potential of combining homogenization with DCT as an effective approach to enhance the structural integrity and mechanical performance of L-DED-fabricated D2 tool steel, offering valuable insights for the development of advanced manufacturing processes for alloyed materials.

Tunable metamaterials with carrier-induced effective permittivity for active control of electromagnetic fields in semiconductor manufacturing device

Precise control of electromagnetic fields is critical in many advanced manufacturing processes, such as those used in the semiconductor industry, where device performance relies on precision and uniformity. Here, we introduce a solution to control electromagnetic fields via permittivity modulation without the limitations of resonance-based approaches, through a patterned semiconductor enabling permittivity tuning via carrier-density modulation. This carrier-responsive metamaterial (CRM) exhibits frequency-independent performance over a broad frequency spectrum and significant permittivity tunability through controlled semiconductor conductivity. Furthermore, the conductivity response and the tuning range can be easily modulated through the variation of semiconductor materials and geometrical parameters. We present an intuitive model that explains the relationship between the CRM’s structure and properties, including its effective permittivity and loss tangent. Supported by comprehensive simulations and experimental validations, our findings show that the effective permittivity can be increased by over 3.5 times with low dielectric loss across a wide frequency range. As an application, we explore the CRM’s potential in plasma control, revealing its ability to influence plasma density nearly 30% by modulating its effective permittivity, exhibiting CRM’s versatile functionality and potential impact across diverse technological domains.

Advanced FEM Insights into Pressure-Assisted Warm Single-Point Incremental Forming of Ti-6Al-4V Titanium Alloy Sheet Metal

This study employs the finite element (FE) method to analyze the Incremental Sheet Forming (ISF) process of Ti-6Al-4V titanium alloy. The numerical modeling of pressure-assisted warm forming of Ti-6Al-4V sheets with combined oil-heating and friction stir rotation-assisted heating of the workpiece is presented in this article. The thermo-mechanical FE-based numerical model took into account the characteristics of the mechanical properties of the sheet along with the temperature. The experimental conditions were replicated in FEM simulations conducted in Abaqus/Explicit, which incorporated boundary conditions and evaluated various mesh sizes for enhanced accuracy and efficiency. The simulation outcomes were compared with actual experimental results to validate the FE-based model’s predictive capacity. The maximum temperature of the tool measured using infrared camera was approximately 326 °C. Different mesh sizes were considered. The results of FEM modeling were experimentally validated based on axial forming force and thickness distribution measured using the ARGUS optical measuring system for non-contact acquisition of deformations. The greatest agreement between FEM results and the experimental result of the axial component of forming force was obtained for finite elements with a size of 1 mm. The maximum values of the axial component of forming force determined experimentally and numerically differ by approximately 8%. The variations of the forming force components and thickness distribution predicted by FEM are in good agreement with experimental measurements. The numerical model overestimated the wall thickness with an error of approximately 5%. By focusing on the heating techniques applied to Ti-6Al-4V titanium alloy sheet, this comparative analysis underlines the adaptability and precision of numerical analysis applied in modeling advanced manufacturing processes.

Beyond 99.5% Geometrical Fill Factor in Perovskite Solar Minimodules with Advanced Laser Structuring

AbstractPerovskite solar cells, known for high efficiency and compatibility with various photovoltaic (PV) applications, have garnered significant attention from academia and industry. Scaling up these cells conventionally involves fabricating modules with series‐connected cells using a monolithic interconnection based on the P1‐P2‐P3 scheme, a common approach for thin‐film PV modules. The Geometrical Fill Factor (GFF), representing the ratio between active area and aperture area, typically ranges from 90% to 95%. This study introduces an advanced laser manufacturing process to minimize interconnection area by reducing scribe width and minimizing distances between them, achieving an interconnection width of 45 µm with a GFF of 99.1%. Additionally, a discontinuous P2 design further reduces the dead area to an average of 19.5 µm, resulting in a record GFF of 99.6%. Using this interconnection in a highly efficient p‐i‐n stack, the study demonstrates the feasibility of the discontinuous P2 by fabricating 2.6 cm2 minimodules with an aperture area efficiency of 20.7%. The research highlights how proper design can minimize intrinsic losses during the scaling process from cell to module to a negligible level. Experimental studies, coupled with cell‐to‐module loss simulations and electroluminescence mapping for layer deposition uniformity, provide insights into the potential of the new P2 design.

Optimization of the extrusion rate for different layer thicknesses to achieve controlled mechanical properties in MEX 3D printing of low-alloy steel

Composite extrusion modeling is an advanced material extrusion additive manufacturing process that requires fine-tuning of printing parameters for printing injection molding feedstocks. In order to achieve comparable high-quality parts at different layer thicknesses, the parameters must be optimized for the respective layer thicknesses. In this work, the printing parameters for AISI 8740 low-alloy steel injection molding material are optimized for four different layer thicknesses. The extrusion multiplier values for each layer thickness are tuned, allowing fine adjustment of the printer's screw rotation speed at every layer thickness, resulting in all final parts' maximum densities (≥ 98 %) with high accuracies. Scanning electron microscope images, surface roughness measurements, and printing precision tests proved the good and comparable high-quality of all optimized printed parts. Young's modulus, tensile and yield strengths also raised by ∼24 %, ∼16 % and ∼15 %, respectively, compared to the non-optimized printed sintered part. The achieved tensile strength range of (∼888–947) MPa and yield strength range of (∼574–586) MPa of all optimized printed sintered parts were also higher than those provided by the feedstock manufacturer. This promising approach significantly reduces printing time and manufacturing costs without compromising the printed final parts' quality and mechanical properties. The adjusting screw rotation speed methodology allows broader applicability across diverse material extrusion processes.

Microstructural evolution and tensile properties of direct energy deposited Ti-4Al-5Co-0.25Si alloy during heat treatment

Additive manufacturing (AM) stands as an advanced manufacturing process, successfully utilized for the fabrication of high-value titanium components. However, these components have yet to find widespread use in industrial applications due to their inadequate performance when compared to conventionally processed parts. Consequently, there is a pressing demand to develop new alloys and microstructural control strategies to overcome this limitation. Here, we have designed a cost-effective Ti-4Al-5Co-0.25Si alloy for use in laser-based direct energy deposition additive manufacturing. The selection of solute elements has resulted in both solute strengthening and grain refinement. Additionally, the implementation of a heat-treatment schedule has allowed for control over the phase constituents and their stability. The as-fabricated alloy exhibits a prior-β grain size of ∼50 μm, α plate thickness of ∼0.6 μm, and tensile strength of 1145 MPa. Subsequent heat treatment induces the formation of subgrains, discontinuous grain boundary α and metastable β phases, and ultrafine martensitic features ranging from 40 to 100 nm. Moreover, this heat treatment improved the tensile strength of the alloy to 1500 MPa while maintaining an elongation of ∼5%. Importantly, the proposed hierarchical structured alloy offers an economical alternative, outperforming commercially available titanium alloys. This study presents a new direction for further research into alloy and microstructural design, aiming at the development of low-cost, high-performance titanium alloys suitable for additive manufacturing.

Experimental and numerical investigation of the damage state of Ti-6Al-4V alloy sheet in the tensile test, hydraulic bulging, and hydroforming processes

There has not been any damage prediction using Johnson-Cook’s (JC) hardening and damage model in the hydraulic bulging (HB) and hydroforming (HF), which are the advanced manufacturing processes, of the Ti-6Al-4V (Ti64) alloy. In the presented study, the damage behavior of the Ti64 alloy sheet in the HB and HF processes was investigated both experimentally and numerically for the first time to address the existing research gap. In this context, firstly, tensile tests (TT) were carried out on samples with different stress triaxiality values at three different tensile speeds, and the fracture morphologies of the samples were examined to evaluate whether it was appropriate to use the JC hardening and damage model. Since the fracture surfaces generally exhibit a ductile fracture morphology and are affected by stress triaxiality and strain rate, it was determined that it would be appropriate to use the JC hardening model and damage criterion to predict the damage of the Ti64 alloy in finite element analysis (FEA). Then, JC model parameters were determined by fitting the stress-strain curve obtained from the FEA and experimental tensile tests. In the HB experiments, bulging height and thickness thinning were predicted by FEA with an accuracy of 97% and 96.85%, respectively. In the HF experiments, the experimental burst pressure, die inlet radius, and base radius were predicted correctly at a rate of 92.5%, 95.5%, and 97.8%, respectively. Also, the thickness of the sample showed good agreement with the FEA results. The fracture zones in each process exhibited good agreement with the experimental results. Thus, it has been demonstrated that the JC damage criterion can be successfully applied in FEA if the Ti64 titanium alloy is damaged in various processes.