Search Results (889)

Steel and prestressed concrete traction poles can be fixed to reinforced concrete pile foundations using typical bolted connections. The stainless steel fastening screw is connected to the ordinary steel foundation pile reinforcement by friction welding under specific friction welding process parameters. From the perspective of the structural strength of the connection between the traction pole and the foundation pile, regarding the transfer of tensile and shear forces through a single anchor bolt, the yield strength of stainless steel bolts should be R_e,min ≥ 345 MPa for M30 anchors, R_e,min ≥ 310 MPa for M36 anchors and R_e,min ≥ 300 MPa for M42 anchors. This requirement is reliably met by martensitic stainless steels, while other stainless steels have yield strengths below the required minimum. What truly determines the foundation pile’s load capacity is not the satisfactory mechanical strength of the stainless steel (here, the parameters are met), but the quality of the friction-welded end connection between the reinforcement and the threaded bars. Incorrect selection of the type of prestressing steel in the analyzed connection can have enormous consequences for foundation pile manufacturers. Annual production of foundation piles amounts to thousands of units, and an incorrect decision made by the pile designer at the design stage can result in significant financial losses and a high risk to human life. This article presents the results of studies on friction-welded connections of M30, M36, and M42 threaded bars made of austenitic 1.4301 (AISI 304) and martensitic 1.4021 (AISI 420) stainless steel with B500SP reinforcement bars. The tests yielded negative results for 1.4021 (AISI 420) steel, despite its yield strength exceeding R_e ≥ 360 MPa. Full article

High-end manufacturing supply chains are increasingly exposed to disruption risks and environmental uncertainty, yet how Industrial Internet (II) empowerment builds supply chain resilience (SCR) and when such benefits are most pronounced remain unclear. Grounded in the resource-based view and ambidextrous innovation logic, this study investigates whether II empowerment—captured by connectivity capability (CC), integration capability (IC), and analytics capability (AC)—enhances SCR through supply chain collaborative innovation (SCCI), including supply chain breakthrough innovation (SCBI) and supply chain incremental innovation (SCII), and whether environmental uncertainty (EU) conditions these relationships. Survey data from 293 Chinese high-end manufacturing firms were analyzed using structural equation modeling and bootstrapped mediation tests, supplemented by moderated regression analysis. The results indicate that CC, IC, and AC all directly and positively affect SCR. CC and AC significantly promote SCBI, whereas the effect of IC on SCBI is not significant; meanwhile, CC, IC, and AC all significantly foster SCII. Both SCBI and SCII are positively associated with SCR. SCBI mediates the effects of CC and AC (but not IC) on SCR, while SCII mediates the effects of all three II dimensions. Furthermore, EU strengthens the impacts of CC, AC, SCBI, and SCII on SCR, whereas the IC × EU interaction is not significant. These findings clarify the innovation-based mechanisms and boundary conditions of II-enabled resilience and offer actionable implications for high-end manufacturers seeking resilient supply chains under uncertainty. Full article

Although AWGs are widely used in FBG interrogation systems, conventional interrogators are often bulky and hard to deploy, limiting their use in complex field environments. Here, we developed an FBG interrogator based on a photonic AWG chip, comprising a photonic chip module, an optoelectronic detection and processing module, and an output interface module. The AWG chip measures only 280 µm × 150 µm, while the entire interrogator measures just 160 mm × 100 mm × 80 mm, achieving system miniaturization. Wavelength interrogation tests show that the FBG interrogator achieves a wavelength accuracy of 9.87 pm and a high-speed sampling rate of up to 10 kHz, enabling high-precision, real-time FBG demodulation under rapidly varying temperatures. Furthermore, the interrogator was subjected to engineering validation, with dynamic FBG wavelength demodulation experiments conducted under high-temperature shocks in a turbo-engine, verifying its reliability under extreme conditions and demonstrating its potential for broader engineering applications. Full article

Amid the dual-driven trends of Industry 5.0 and smart manufacturing integration, as well as the global imperative for manufacturing sustainability to address resource constraints, carbon neutrality goals, and circular economy demands, human–robot collaborative (HRC) manufacturing has emerged as a core direction for reshaping manufacturing production modes while aligning with sustainable development principles. This paper comprehensively reviews HRC manufacturing systems, summarizing their technical framework, practical applications, and development trends with a focus on the synergistic realization of operational efficiency and sustainability. Addressing the rigidity of traditional automated lines, inefficiency of manual production, and the unsustainable drawbacks of high energy consumption and resource waste in conventional manufacturing, HRC integrates humans’ flexible decision-making and environmental adaptability with robots’ high-precision and continuous operation, not only improving production efficiency, quality, and safety but also optimizing resource allocation, reducing energy consumption, and minimizing production waste to bolster manufacturing sustainability. Its core technologies include task allocation, multimodal perception, augmented interaction (AR/VR/MR), digital twin-driven integration, adaptive motion control, and real-time decision-making, all of which can be tailored to support sustainable production scenarios such as energy-efficient process scheduling and circular material utilization. These technologies have been applied in automotive, aeronautical, astronautical, and shipping industries, boosting high-end equipment manufacturing innovation while advancing the sector’s sustainability performance. Finally, challenges and future directions of HRC are discussed, emphasizing its pivotal role in driving manufacturing toward a balanced development of efficiency, intelligence, flexibility, and sustainability. Full article

Joining technologies play a decisive role in the sustainability, circularity, and end-of-life performance of metal structures. Despite the increasing emphasis on low-impact manufacturing and Extended Producer Responsibility (EPR), the connection between joining methods and producers’ environmental obligations remains underexplored. This review provides a comprehensive assessment of conventional and emerging techniques, including fusion welding, solid-state welding, mechanical fastening, adhesive bonding, and hybrid and AM-assisted processes, examining how each technology influences material efficiency, durability, repairability, disassembly, and recyclability. Particular attention is devoted to the effects of joint characteristics on life-cycle impacts, waste generation, and the technical and economic feasibility of high-quality material recovery, using recent LCA evidence and industrial case studies from automotive, shipbuilding, aerospace, and consumer products. Building on this analysis, the review proposes qualitative checklists and semi-quantitative scoring schemes to compare joining options under EPR-relevant criteria and to identify best- and worst-case design scenarios. Finally, promising research directions are outlined, including reversible and debond-on-demand solutions, low-energy solid-state routes, joining strategies for multi-material yet recyclable structures, and the integration of digital twins and LCA-informed design tools, offering a roadmap for metal structures that align technical performance with EPR-driven end-of-life management. Full article

The rapid progress of perovskite solar cells (PSCs) has established them as a groundbreaking technology for sustainable energy. However, the sustainability of their lifecycle is still hindered by challenges related to material toxicity and end-of-life management. This review comprehensively assesses emerging recycling technologies, with a particular focus on their effectiveness in recovering perovskite compounds, transparent conductive oxides, and metallic contacts. Mechanical separation, solvent-based dissolution, thermal decomposition, and hybrid methods are compared in terms of recovery rates, purity levels, energy consumption, and scalability. Current challenges, such as the generation of secondary waste, the instability of recovered perovskites, and economic barriers, are critically analyzed alongside emerging solutions, including the use of non-toxic solvents, vacuum-assisted recovery, and the integration of closed-loop manufacturing. By evaluating lifecycle impacts and cost–benefit trade-offs, this work outlines pathways for transforming PSC waste into high-value secondary resources, thereby promoting both environmental sustainability and industrial competitiveness. Full article

Laser powder bed fusion (LPBF) is the best suited technology to manufacture temperature-resistant Ti-6Al-2Sn-4Zr-6Mo parts with complex geometrical features for high-end applications. Improving printing accuracy by reducing the layer thickness (t) generally requires repeating a tedious and time-consuming process optimization routine. To simplify this endeavour, the present work proposes three process equivalence criteria allowing to transfer optimized process conditions from one printing parameter set to another. This approach recommends keeping the volumetric laser energy density (VED) and hatching space-to-layer thickness ratio (h/t) constant, while adjusting the scanning speed (v) and hatching space (h) accordingly. To validate this approach, Ti6246 parts were printed with 50 µm and 25 µm layer thicknesses, while keeping VED = 100 J/mm³ and h/t = 3 constant for both cases. The printed samples were analyzed in terms of their density, microstructure and mechanical properties, as well as the geometric compliance of wall-, gap- and channel-containing artefacts. Highly dense samples exhibiting comparable microstructures and mechanical properties were obtained with both parameters sets investigated. However, they induced markedly differing geometric characteristics. Notably, using 25 µm layers allowed printing walls as thin as 0.2 mm as compared to 1.0 mm for 50 µm layers. Full article

Directed Energy Deposition (DED), as a core branch of additive manufacturing, encompasses two typical processes: laser directed energy deposition (LDED) and wire and arc additive manufacturing (WAAM), which are widely used in manufacturing aerospace engine blades and core components of high-end equipment. In recent years, with the increasing adoption of deep learning (DL) technologies, the research focus in DED has gradually shifted from traditional “process parameter optimization” to “AI-driven process optimization” and “online real-time monitoring”. Given the complex and distinct influence mechanisms of key parameters (such as laser power/arc current, scanning/travel speed) on melt pool behavior and forming quality in the two processes, the introduction of artificial intelligence to address both common and specific issues has become particularly necessary. This review systematically summarizes the application of DL techniques in both types of DED processes. It begins by outlining DL frameworks, such as artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning (RL), and their compatibility with DED data. Subsequently, it compares the application scenarios, monitoring accuracy, and applicability of AI in DED process monitoring across multiple dimensions, including process parameters, optical, thermal fields, acoustic signals, and multi-sensor fusion. The review further explores the potential and value of DL in closed-loop parameter adjustment and reinforcement learning control. Finally, it addresses current bottlenecks such as data quality and model interpretability, and outlines future research directions, aiming to provide theoretical and engineering references for the intelligent upgrade and quality improvement of both DED processes. Full article

Estimation of remaining useful life (RUL) of roller bearings is a prevalent problem for predictive maintenance in manufacturing. However, roller bearings are subject to a variety of factors during their operation. As a result, we deal with a slow nonlinear degradation process, which is long-range dependent, self-similar and has non-Gaussian characteristics. Proper data pre-processing enables us to use Pareto’s probability density function (PDF), Generalized Pareto motion (GPm) and its fractional-order extension (fGPm) as the degradation predictive model. Estimation of the Hurst exponent shows that this model has a long-range correlation and self-similarity. Through the analysis of the uncertainty of the end point of the bearing’s RUL and the prediction process, not only did it verify the high adaptability of fGPm in simulating complex degradation processes but also the criteria for judging self-similarity, and LRD characteristics were established. The case study mainly proves the validity of the theory, providing an effective analytical tool for a deeper understanding of the degradation mechanism. Full article

Optimizing end mill geometry is critical for improving performance and reducing costs in the high-volume manufacturing of tools, dies and molds. This study demonstrates a successful optimization framework for solid end mills machining 1.2379 cold-work tool steel, integrating Finite Element Analysis (FEA), Artificial Neural Networks (ANN), and Genetic Algorithms (GA). The optimized tool geometry, derived from four key design parameters, delivered substantial performance gains over an industrial reference (parent) tool. Our ANN-GA model achieved a remarkable predictive accuracy (R = 0.75–0.98) over the RSM model (R = 0.17–0.63) and identified an optimal design that reduced the resultant cutting force by approximately 11% (to 142.8 N) and improved surface roughness by 21% (to 0.1637 µm) compared to experimental baselines. Crucially, the new geometry halved the tool breakage rate from 50% to ~25%. Parameter analysis revealed the width of the land as the most influential geometric factor. This work provides a validated, high-performance tool design and a powerful modeling framework for advancing machining efficiency in tool, mold and die manufacturing. Full article

Cobalt-based superalloy Co06A exhibits excellent high-temperature performance and is widely used in the repair and additive manufacturing of critical hot-end components via direct laser deposition (DLD). However, improper energy–mass input during direct laser deposition often leads to defects such as porosity, cracks, and poor surface quality, which seriously affect the performance of formed parts. In this study, a systematic experimental investigation based on an orthogonal design was carried out to examine the effects of laser power, scan speed, and powder feed rate on the dilution rate, surface roughness, and powder capture efficiency of a one-layer single Co06A track. Range analysis and multiple linear regression were employed to quantify the influence of each parameter. The results showed that the powder feed rate was the dominant factor affecting both η and Sa, while the laser power had the most significant impact on PE. Through multi-objective optimization, a balanced parameter set (u = 6.66 mm/s, f = 20.81 g/min, P = 2543 W) was recommended, which achieved a dilution rate of about 11.95%, a surface roughness of 4.64 um, and a powder capture efficiency of 79.6%. Through testing, it was found that the energy/mass input ratio was approximately 8. This work demonstrated that matching energy–mass input and adopting a constrained optimization strategy could effectively improve the forming quality and manufacturing efficiency of Co06A in the first-layer manufacturing process, providing a promising prospect in guidance for engineering applications. Full article

One of the primary applications of industrial robots is in various manufacturing processes, such as milling, grinding, and additive manufacturing. To achieve the desired precision in tool trajectory performance when machining specific parts, it is necessary to calibrate the tool centre point (TCP) of the robot for each manufacturing process. The development of industrial robot tool trajectories is a multipurpose task. It encompasses issues related to robot geometry, path interpolation type, and trajectory waypoints approximation. The primary objective of this study is to establish a camera-based methodology for evaluating trajectory-following accuracy in industrial robots. The present paper proposes the use of a high-speed motion camera system for non-contact tracking of TCP trajectories. By capturing the robot’s end-effector motion in real-time and under actual trajectory tracking conditions, this technique enables a clearer understanding of how trajectory execution accuracy varies with velocity, trajectory geometry, trajectory interpolation, and robot kinematics. Provided analysis of two industrial robot types opened interesting findings related to the dependencies between the implementation of first- and second-degree interpolations. To illustrate this point, the implementation of second-degree interpolation ensures a more consistent velocity in the trajectory. This contrasts with first-degree interpolation, which is more challenging to achieve and is susceptible to variations in curvature. Conversely, the utilization of first-degree interpolation facilitates enhanced performance accuracy for smaller curvatures. The results of the experimental research confirm the initial hypothesis regarding the influence of interpolation mode and pave the way for future uses of this information for machine learning algorithms. Full article

As high-end equipment manufacturing advances, demand for improved surface performance in critical components has increased. Laser cladding is an advanced surface strengthening technique that affords effective surface modification. During the laser cladding process, obtaining a fine grain microstructure usually helps to enhance the microhardness, wear resistance, and corrosion resistance of the cladding layer. However, conventional laser cladding often yields coarse columnar grains that limit further performance improvements, so process optimization to achieve grain refinement is necessary. In this study, oscillating laser cladding was combined with a pulsed-wave (PW) laser mode to deposit a fine-grained Al₂CrFe₂Ni₄Ti_1.5 high-entropy alloy cladding on Q550 steel substrates. Compared with continuous-wave (CW) laser cladding, the PW mode produced markedly refined grains and concomitant improvements in microhardness, wear resistance, and corrosion resistance. Specifically, the microhardness of the PW cladding layer reached approximately 673.34 HV_0.5, the wear volume was approximately 0.06 mm³, the wear rate was approximately 0.21 × 10⁻⁴ mm³/N·m, and the corrosion current density decreased to approximately 1.212 × 10⁻⁵ A·cm⁻². This work presents a novel approach for producing high-performance, wear-resistant, and corrosion-resistant high-entropy alloy cladding layers, and offers both theoretical insight and potential engineering applications. Full article

As product complexity and customization levels continue to rise in high-end manufacturing, optimizing and controlling multistage manufacturing processes (MMPs) presents growing challenges. However, existing MMP research has largely focused on optimizing relatively fixed process routes, while limited attention has been paid to the route selection problem itself, particularly the global selection of process routes under real-world conditions where MMPs stages are mutually coupled and characterized by uncertainty. Therefore, the present study focuses on the fundamental challenge of process route decision-making for complex products within MMPs. A hybrid decision model is developed that incorporates expert knowledge and explicitly quantifies uncertainty arising from decision inconsistency and linguistic ambiguity. The proposed model consists of three main components: expert weighting, criterion weighting, and comprehensive ranking of process schemes. Expert and criterion weights are derived using the Enhanced Analytic Hierarchy Process (EAHP) to address inconsistency in expert judgments, while the ranking of alternatives is performed using a novel Combined Compromise Solution (CoCoSo) rule within an Interval Type-2 Fuzzy Sets (IT2FS) linguistic environment. Furthermore, the effectiveness of the proposed framework is validated through a case study on the multistage manufacturing process of compact aerospace heat exchangers. The results demonstrate that the proposed approach provides effective decision support for selecting robust process schemes during the initial planning phase of MMPs. Full article

Additive manufacturing has huge development potential in the aerospace field. The hot-end components of aeroengines work in harsh environments, facing high temperatures and a demand for long service life. In this paper, high-cycle fatigue (HCF) tests of Ti6Al4V alloy at 400 °C by selective laser melting (SLM) under different stress ratios (−1, 0.1, 0.3, 0.5, and 0.8) were carried out, and the fracture surfaces were observed. The results show that the defects existing on the surface or subsurface are prone to become the origin of fatigue cracks. There is a large dispersion of the high-cycle fatigue life of the samples, especially at a low stress ratio. With the increase in the stress ratio, the fatigue failure area on the fracture surface gradually decreases, and the fracture surface gradually presents a mixed pattern of tensile endurance fracture and fatigue failure. Considering the influence of creep damage due to mean stress, models were established, respectively, for the fatigue behavior and time-related rupture behavior to predict fatigue life and conduct an assessment. Then, the two models were combined and the composite models were proposed using the linear damage law. Finally, the single fatigue model and rupture models, as well as the composite models, were evaluated, respectively, and compared with the actual fatigue life, and the best model was obtained for the high-cycle fatigue prediction of SLM Ti6Al4V at 400 °C. Full article