Thermal management remains a persistent challenge in extrusion-based additive manufacturing of amorphous polymers such as acrylonitrile butadiene styrene (ABS), where rapid cooling and steep thermal gradients often induce warpage, delamination, and print failure. This study introduces a low-cost, lamp-based infrared (IR) heating system integrated with real-time, non-contact temperature monitoring via an MLX90614 sensor to enhance dimensional stability in fused pellet modeling (FPM). Two experimental series were conducted: (i) flat plates printed under varying IR and bed heating conditions, and (ii) thin-walled structures fabricated using directional radiant heating while maintaining a constant bed temperature of 90 ^∘ C. Results indicate that spatial thermal uniformity—not merely average platform temperature—is critical for suppressing warpage in planar geometries. A bilateral IR configuration that maintained the deposition interface near 108 ^∘ C and the build surface at 90 ^∘ C produced fully adhered, distortion-free plates. For vertical walls, successful printing required a controlled axial thermal gradient: freshly deposited material remained within 90–95 ^∘ C to ensure interfacial continuity, while underlying layers stabilized between 63 ^∘ C and 75 ^∘ C to prevent heat accumulation and lateral buckling. The system enabled reliable thermal tracking and demonstrated feasibility for retrofit integration into open-chamber FPM platforms. These findings underscore the potential of targeted IR heating as a practical strategy to improve geometric fidelity and process reliability in industrial-scale polymer additive manufacturing.
Inspection activities on industrial assets, for instance in the aerospace or automotive industry, frequently pertain to the examination of the surface conditions of objects. Among the established techniques for surface assessment, photogrammetry has gained increasing relevance in manufacturing quality monitoring, due to its ability to provide high-fidelity 3D spatial data. However, during the photogrammetric reconstruction of a physical object, some regions may be reconstructed less accurately, for example due to an insufficient number of local photographic acquisitions; in such cases, additional photographs of these regions would be needed. This, however, requires the object to remain accessible and unchanged from the time of the initial acquisition; in practice, this condition is rarely met, as the object is often no longer accessible or is found in a different state. Therefore, photogrammetry is intrinsically tied to the acquisition moment, since it relies on images captured at a specific time and does not allow purpose-driven further inspection. This paper proposes a solution to this limitation of traditional photogrammetry, exploiting the concept of “3D scene”, defined as a high-fidelity digital representation of an object at a specific moment in time. Recent advances in radiance-field methods, particularly Gaussian Splatting (GS), enable the creation of fully navigable 3D frozen-in-time scenes, allowing for the extraction of digital images, i.e., renders, that can be integrated into the photogrammetric process to enhance reconstruction quality. The most significant advantage of GS-based scenes is that they are generated from exactly the same photographs used for the preliminary (and sometimes incomplete) photogrammetric reconstruction, without requiring any additional acquisitions. This combined use of photogrammetry and GS introduces the innovative paradigm of back-in-time inspection into manufacturing. This study addresses two main research questions: (i) How can the combined use of photogrammetry and GS be employed for inspection tasks? and (ii) To what extent does GS improve reconstruction completeness and defect detectability? A case study in the aerospace sector is proposed to demonstrate the effectiveness of this method.
Cylindricity error (Δ), being nonlinear and multi-modal in higher-dimensional space, is considered challenging to evaluate. This study proposes a novel approach to minimize the search space for a computationally intensive global search. For this purpose, a faster least-squares (LS) algorithm was used. The sensitivity of the optimal solution to random initial solutions to search space width (λ) around the LS solution is studied. The cylindricity error drastically decreased from the LS solution to Δconv at λconv (2
Robotic Compact Storage and Retrieval Systems (RCS/RS) represent a key category of high-density automated warehousing technologies that depend on efficient robot navigation and task execution. This study addresses the sequencing and scheduling problem for a single robot operating under a dedicated storage policy. An analytical travel-time formulation is developed to characterize robot movement along the three spatial axes of the storage grid. A novel Location-Based Reshuffling Policy (LBRP) is proposed to manage blocking-bin relocations through multi-directional temporary storage. This policy significantly reduces operation time. To further enhance task sequencing, a Genetic Algorithm (GA) is integrated to generate near-optimal execution sequences under stochastic task conditions. The proposed approach is evaluated across multiple randomized scenarios to assess its robustness and sensitivity to task variability. The results show that the combined LBRP+GA framework achieves the lowest operation times among all evaluated strategies. It outperforms the One-Path Reshuffling Strategy (OPRS) by 18
Lithium iron phosphate (LiFePO4) is widely regarded as a sustainable cathode chemistry. However, the environmental advantages are often offset by energy-intensive synthesis and solvent-based electrode manufacturing. This critical review examines emerging low-carbon manufacturing pathways for LFP cathodes, with a focus on solvent-free synthesis, low-temperature crystallisation, aqueous and dry electrode fabrication, and process intensification strategies. Beyond summarising recent advances, this review critically evaluates the thermodynamic, kinetic, and interfacial trade-offs associated with reduced thermal and solvent severity, highlighting key failure modes such as antisite defects, carbon-network degradation, and electrode integrity challenges. A process-centric framework is proposed to connect laboratory-scale concepts with industrially scalable production, identifying drying and calcination as dominant decarbonisation levers and outlining design principles for multifunctional precursors, binder engineering, and roll-to-roll compatibility. Finally, technological readiness levels (TRLs) and scale-up barriers are discussed to provide a roadmap toward manufacturable, low-emission LFP battery production.
This study evaluates the deflection behavior of an AA7005-T53 porthole-extruded aluminum mullion through combined material characterization, a component-level water-tank loading experiment, and explicit finite element simulation. Hardness testing, tensile testing, metallographic observation, and fracture morphology analysis were conducted to quantify local mechanical variations associated with the longitudinal weld seams formed during extrusion. The weld-seam region exhibited markedly lower hardness than the surrounding non-welded material (99.73 HV versus 135.36–137.31 HV, corresponding to a reduction of approximately 27
Rocket engine nozzles are typical tubular components with variable wall thickness and variable diameter, and the accurate prediction and precise control of wall-thickness distribution are of great significance to their service performance. Based on the necking–bulging integrated forming (NBIF) process, this study systematically investigates the forming process of complex variable-section nozzle components. From the perspective of process conditions, the loading path and friction coefficient are selected as representative key parameters affecting wall-thickness variation. A combined finite element simulation and experimental method is used to analyze the effects of loading path and friction coefficient on stress-state evolution and wall-thickness variation ratio distribution under soft-die support conditions. The results show that the normalized second principal deviatoric stress χ changes from positive to approximately zero and then to negative along the axial direction, corresponding to the necking thickening region, transition region, and bulging thinning region, respectively. Furthermore, the introduced accumulated stress-state index Iχ shows an obvious positive correlation with the final wall-thickness variation ratio, indicating that it can characterize the accumulated effect of stress-state history on wall-thickness transition. A reasonable loading path can improve the matching between necking-induced thickening and bulging-induced thinning, whereas excessive friction leads to material accumulation and forming instability. Under the optimal process parameters, the wall-thickness variation of the formed part is controllable, with a maximum thickening ratio of 26.3
The intellectualization of machine tools constitutes a primary driving force for the advancement of the manufacturing industry. To enhance the robustness and generalizability of machine tools operating in dynamic and unstructured machining environments, and to achieve deep integration between intelligent algorithms and physical execution, this paper proposes the concept of an Embodied Intelligence Machine Tool (EIMT). Unlike conventional intelligent machine tools (IMTs), which rely primarily on disembodied intelligence decoupled from physical execution feedback, the EIMT paradigm emphasizes a closed-loop interactive system encompassing embodied perception, cognition, decision-making, and execution. A cloud-edge-device collaborative architecture grounded in the dual-process theory of cognition is established to enable machine tools to autonomously perceive, understand, reason about, and interact with the physical world. Four pivotal enabling technologies are systematically analyzed: embodied cognitive models, embodied simulation, Sim-to-Real transfer, and privacy and trust. A D-shaped profile milling case study is presented to verify the feasibility and effectiveness of the proposed framework. The EIMT concept is poised to advance machine tool evolution into a new era characterized by heightened autonomy, enhanced adaptability, and seamless human-machine-environment collaboration.
Thermal error is one of the key factors affecting the motion accuracy of the linear motor driven feed axis (LMFDA), so it is necessary to model and predict it. The complexity of the underlying physics makes mechanistic models hard to build and solve, and their predictions often deviate from real-world measurements. Empirical models require high-quality data in training, and their training process lacks physical interpretability. To address these challenges, we propose a physics-informed neural network (PINN)-based thermal error modeling method that integrates governing partial differential equations into a deep learning framework. We conduct a mechanistic analysis of LMFDA’s operation, derive the governing temperature-field PDEs under coupled electromagnetic-thermal-flow conditions, and build a PINN model tailored to LMFDA. During the neural network training process, prior physical information such as PDE, initial condition (IC), and boundary condition (BC) are embedded to regularize learning, thereby achieving good physical interpretability. We validate the proposed method on a fully direct-drive three-axis machine tool. Experimental results demonstrate that by combining mechanistic analysis with data-driven learning, the proposed method is well-suited for thermal error problems characterized by limited training data but well-defined physical mechanisms.
This study introduces a multi-point adaptive magnetorheological polishing technique employing an enhanced Halbach array for the ultra-precision finishing of Ti-6Al-4 V turbine blades, addressing the challenges associated with machining complex freeform surfaces. An improved Halbach magnetic configuration was devised to concentrate the magnetic field within the polishing zone, achieving a peak strength of 1.78 T. A mechanism-based material removal model, integrating Preston theory with magnetic-force-induced abrasive interactions, was developed to predict material removal distribution on intricate curved surfaces. A shape-adaptive optimization strategy was proposed to determine the optimal interaction angle, ensuring uniform magnetic pressure and stable polishing conditions. The experimental results indicate significant enhancements in surface quality, reducing the surface roughness from approximately 90–100 nm to 5–9 nm with high uniformity. The magnetic field simulation closely aligned with the experimental measurements. This method offers an efficient and scalable solution for finishing complex freeform components, particularly lightweight titanium alloys, in aerospace applications.
The modern industrial automation needs intelligent coordination of Robotic Distribution Systems (RDS) to reduce downtime and maximize the use of resources. This paper suggests an AI-based predictive scheduling model to combine a hybrid model of Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and ensemble-based downtime predictions. The framework is a hybrid which incorporates predictive intelligence and adaptive scheduling to reach dynamic optimization in robotic task allocation. The down time events are predicted with an impressive accuracy of 98.30
Ball burnishing is an efficient and innovative process for improving surface integrity through controlled plastic deformation. This study proposes the simultaneous modeling and multi-objective optimization of surface quality, surface hardness, and productivity during the ball burnishing of high-strength AA2024-T3 aluminium alloy. The effects of burnishing force, workpiece rotational speed, and feed rate were investigated. Quadratic regression models for surface roughness improvement rate and surface hardness were developed using Response Surface Methodology (RSM), while production time was independently determined using an analytical model. Statistical analysis showed that feed rate and burnishing force predominantly govern roughness improvement, whereas surface hardness is mainly influenced by the applied force and rotational speed. Significant interactions between process parameters were also identified. The developed RSM models were experimentally validated and, together with the analytical production-time model, integrated into a multi-objective optimization framework to identify optimal trade-off solutions. The results showed that surface quality improvement generally evolves in opposition to surface hardness and productivity. Three representative solutions were selected: (i) a productivity- and hardening-oriented solution, achieving a 60.99
Titanium alloys are widely deployed in aerospace, biomedical and marine applications owing to their high strength-to-weight ratio, corrosion resistance and biocompatibility. Recent progress in additive manufacturing (AM) has enabled the production of near-net-shape Ti-based components; however, fusion-based AM processes impose steep thermal gradients and rapid cooling rates that generate coarse columnar prior-β grains, strong build-direction textures and pronounced mechanical anisotropy, restricting industrial uptake. Ceramic-particle inoculation has emerged as a compelling route to overcome these limitations by providing heterogeneous nucleation sites during solidification. This review consolidates and critically appraises the peer-reviewed literature on ceramic inoculation of Ti alloys processed by directed energy deposition (DED-Arc, DED-LB) and powder bed fusion (PBF-LB, PBF-EB). The inoculant families examined include borides (TiB, LaB₆, boron nitride nanotubes), nitrides (TiN, ZrN), carbides (B₄C, SiC) and oxides (Y₂O₃, TiO₂), with selected non-ceramic systems (B, Si, TiAlNb) added for comparison. When the inoculant loading is optimised, prior-β grain refinement to the sub-millimetre scale is consistently reported, whereas over-loading commonly increases porosity, anisotropy or embrittlement. In contrast to earlier broader nano-inoculant surveys spanning multiple base metals, this review focuses specifically on ceramic inoculants for Ti-alloy AM, offering a mechanistic and process-oriented framework to guide future alloy and process design.
The thermal field inside industrial Czochralski (CZ) silicon crystal growth systems strongly influences the crystal quality, oxygen incorporation, interface stability, intrinsic point-defect behavior, and furnace energy efficiency. A fully coupled three-dimensional thermal-fluid model was developed to investigate the influence of a furnace’s thermal-field design on industrial CZ silicon crystal growth under realistic operating conditions. The numerical framework simultaneously considers conductive, convective, and radiative heat transfer, as well as melt convection, turbulence transport, argon gas flow, and interface heat transfer. A comprehensive simulation database consisting of 2588 operating conditions was generated by systematically varying seven major thermal-field design parameters related to the heater geometry, heater position, insulation configuration, thermal shielding, and heater power redistribution. Four characteristic thermal outputs were extracted for thermal-field evaluation and machine-learning (ML) prediction: the average crucible temperature, total heater power consumption, average V/G ratio at the crystal–melt interface, and the radial nonuniformity of V/G , where V is the crystal pulling rate, and G is the axial thermal gradient near the solid–liquid interface. Multiple ML algorithms were evaluated for predicting the complex nonlinear thermal behavior of the CZ system: linear regression (LR), artificial neural network (ANN), support vector machine (SVM), random forest (RF), gradient boosting (GB), and stochastic gradient descent (SGD) models. The GB model consistently achieved the highest prediction accuracy, particularly for interface-sensitive thermal parameters. Furthermore, explainable artificial intelligence analysis based on SHapley Additive exPlanations (SHAP) was used to identify the dominant thermal-control mechanisms that govern the furnace’s thermal behavior and interface stability. The results show that the dominant parameters were the main-heater length, main-heater vertical position, thermal gap between the melt free surface and thermal shield, and heater-power ratio. These parameters controlled the global furnace thermal behavior and the localized interface thermal uniformity. Smaller thermal gaps increased the asymmetry of radiative cooling near the crystal shoulder region and the radial V/G nonuniformity, whereas larger thermal gaps led to smoother thermal distributions at the interface. The total power consumption of the furnace varied significantly from 52.6 to 76.9 kW for different thermal-field configurations, which indicates that there is considerable potential for industrial energy optimization through redesign of the thermal field. The proposed COMSOL–ML–SHAP framework provides both highly accurate thermal-field prediction capability and physically interpretable insight into the dominant thermal mechanisms that govern the crystal quality, energy consumption, and defect-related growth behavior in such growth systems.
Manual assembly workstations still devote a relevant share of their cycle time to non-value-added picking-and-placing activities, which may also expose operators to awkward postures when bulky parts are stored in rear locations. This paper studies whether collaborative robots (cobots) can support large-part picking in Human–Robot Collaboration (HRC) assembly systems while jointly improving productivity, ergonomics, and economic feasibility. A structured offline decision-support framework is proposed. The method combines storage-location- and part-attribute-dependent estimation of Picking-and-Placing Time (PPT), a Mixed-Integer Linear Programming (MILP) task allocation model for two operators and one cobot, a time-weighted Rapid Entire Body Assessment (REBA) indicator, and an iso-payback economic analysis. The framework is applied to a 26-task assembly case study. Compared with the human-only baseline, the makespan-oriented solution reduces the cycle time from 553 s to 493 s (about 11
Aluminum alloys are extensively used in new energy vehicles and aerospace components owing to their high specific strength and low density. However, adhesive wear remains the primary failure mode of polycrystalline diamond (PCD) tools during the dry turning of these alloys. This study systematically investigates the influence of tool rake angle on adhesive wear evolution using PCD tools with rake angles of 3°, 6°, 9°, 12°, and 15°. Wear morphologies in the critical region near the cutting edge on both rake and flank faces were characterized using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Cutting forces and temperatures were measured to support the analysis of wear mechanisms. The results show that the three-component cutting forces decreased with increasing rake angle, with the principal cutting force reduced by 22.94
Additive manufacturing has recently emerged as a viable approach for producing prototype injection moulds for low-volume manufacturing. PolyJet-printed photopolymer inserts offer rapid lead times and cost efficiency, but their limited thermal resistance, low thermal conductivity, and tendency to creep are fundamental constraints on mould lifetime and process stability. In this study, the suitability of the MED615RGD biocompatible photopolymer for injection mould tooling was investigated and its performance was compared to the widely used VeroWhite photopolymer resin. Comprehensive thermal and mechanical characterization was conducted, including dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC), heat deflection temperature (HDT) and Shore D hardness testing, creep analysis, and 3D optical scanning. These tests help to evaluate the fundamental thermal and mechanical properties of the mould, which govern its durability. After the material tests, mould inserts were printed from VeroWhite and MED615RGD. The inserts were subsequently built into a custom-designed, experimental injection mould, equipped with strain gauges and an embedded thermocouple, which enables real-time monitoring of deformation and thermal loads during the injection moulding tests. The experimental results showed that MED615RGD exhibits a higher glass transition temperature (Tg = 78.3 °C) and improved stiffness compared to VeroWhite (Tg = 70.5 °C), while maintaining similar hardness. During injection moulding, both materials suffered from progressive heat accumulation due to low conductivity, resulting in dimensional deviations and eventual edge failure. Tool degradation began in the 40–50 cycle range, consistent with previously reported PolyJet-based tooling lifetimes. The findings confirm that MED615RGD is suitable for rapid tooling applications where short manufacturing cycles, dimensional stability, and design iteration speed are the primary objectives. In this study, performance limitations, degradation mechanisms and practical process window recommendations are discussed for future industrial use of photopolymer rapid tooling.
Wire-feed laser additive manufacturing (WLAM) offers significant potential for producing bimetallic structures (BMs) by combining the unique properties of dissimilar alloys. This study investigates the microstructural evolution, mechanical properties, and environmental performance of a BM composed of Inconel 625 (IN625) and Stainless Steel 316 (SS316) fabricated using a WLAM system. Monolithic IN625, monolithic SS316, and a 50/50 IN625-SS316 BM were characterized using optical microscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and mechanical testing. Microstructural analysis revealed a smooth transition zone with good metallurgical bonding, exhibiting epitaxial grain growth in the form of columnar dendrites and equiaxed grains with effective elemental mixing and no sharp discontinuities. XRD confirmed the stability of face-centered cubic (FCC) phases throughout the bimetallic structure, with no detectable peaks corresponding to brittle secondary phases. Mechanical testing showed that the BM exhibited intermediate properties, with a UTS of 506 MPa and an average hardness of 145 HV, corresponding to 27.5
Powder feedstock is the primary input to metal additive manufacturing processes such as laser powder bed fusion. Its characteristics are crucial for producing high-quality components. Realization of the waste reduction and cost benefits that this technology offers requires reclaiming, processing, and reusing powder feedstock. While these benefits can be significant, there are challenges related to the industrial implementation of powder reuse, including the variability in powder quality that can propagate through to the produced material. This variability can be both negative and positive, but ultimately leads to uncertainty for both users and regulators. This means material and process qualifications are difficult to achieve when powder feedstock is being repeatedly reused. This study reports the effects of 175 Ti-6Al-4V ELI powder reuse cycles, characterized at 23, 104, and 175 reuses, in a certified medical device production facility, whereby powder was topped up with virgin powder from the same production lot after each build cycle. To the best of our knowledge, this is the longest reuse campaign reported in the literature. During this campaign, flowability improved with Hall flow rate decreasing by 5.4
