CIRP Journal of Manufacturing Science and Technology最新文献

In the realm of Digital Twins (DTs), industry experts have emphasised the pivotal concept of the Federation of Twins, envisioning seamless collaboration across sectors driven by shared semantics. In response to this challenge, the Cognitive Digital Twin (CDT) integrates the DT framework with formal semantics, specifically ontologies. This paper introduces a comprehensive five-step methodology for CDT development. Furthermore, it becomes possible to incorporate human expertise into the DT ecosystem by adopting an ontological approach. The CDT enhances DT services with advanced reasoning capabilities, leading to a profound semantic enrichment of the data. The presented methodology has been validated using a use case where the CDT is employed to detect malfunctions, significantly reducing manual intervention. This paper advocates for the adoption of CDTs, which represent a harmonious fusion of formal semantics and human expertise, enhancing system efficiency and operational performance.

This paper proposes a model to predict the tensile characteristics of metal fused filament fabricated (MFFF) components. The proposed model consists of mathematical, experimental, and finite element (FE) models. The mathematical model was constructed based on the composite laminate theory and was combined with experiments for basic layup of 0° and 90° raster angle to describe the behavior of MFFF parts. The FE model was built to simulate the behavior of MFFF parts in a virtual environment and its validity was verified using independent experiments for a more common layup of +45°/−45°.

Due to severe deformation, noise, and occlusion, the registration problem of non-rigid point sets in rolling formed metal workpieces poses challenges, and the demand for real-time data storage and registration during the rolling forming process makes this problem even more prominent. This paper proposes an enhanced nonrigid point set registration algorithm based on the Coherent Point Drift (CPD) framework, introducing novel methods to improve accuracy and efficiency. A refined local distance calculation method combining spatial distance has been proposed to improve matching accuracy. In contrast, an optimized shape context method introduces a new driving force criterion to expedite initial registration and reduce subsequent errors. Leveraging the Expectation-Maximization (EM) algorithm, the approach iteratively solves point correspondences, demonstrating robustness in handling complex scenarios like non-rigid deformation and noise. Experimental validation using real production datasets shows superior accuracy and efficiency over classical algorithms, showcasing a practical solution for non-rigid point set registration challenges in roll forming applications.

Lattice structures to fabricate bone implants can avoid stress-shielding effects and promote bone-in-growth. However, the performance of bones varies in different body parts, creating a barrier to manufacture an appropriate lattice structure for bone implant. Here, the formability, anisotropy, energy absorption abilities, stress distribution, and deformation mode of the laser powder bed fusion (LPBF) processed face-centered cubic (FCC), Octet, and Kelvin lattice structures were systematically compared through experiments and finite element analysis. The results show that the Kelvin lattice structure had the optimal comprehensive mechanical performance. This research has potential value for the design and manufacturing of specific bone implants.

Sintered NdFeB, owing to its outstanding magnetic properties, finds widespread applications in diverse fields. However, its susceptibility to corrosion limits its utility. To enhance its corrosion resistance, a rotating transverse magnetic field is incorporated into the electrical discharge machining milling (EDM-M) process. Comparative experiments are conducted on sintered NdFeB by EDM-M, fixed transverse magnetic field assisted EDM-M(FTMEDM-M), and rotating transverse magnetic field assisted EDM-M(RTMEDM-M). Results indicate that the RTMEDM-M process yields the least surface cracks, the least "caves", and the recast layer which is the most uniform and the most continuous. Its impedance value is the highest, self-corrosion potential is the largest, and self-corrosion current density is the lowest according to its electrochemical impedance spectroscopy (EIS). In addition, its mass loss per unit area is the least, with the latest and the weakest reaction of chemical corrosion of the workpiece surface.

Complete inspection of workpiece surface integrity invariably involves a form of destructive testing to enable the assessment of microstructural defects such as machining-induced white layers and near-surface plastic deformation. The incumbent offline and destructive microscopy inspection process is incompatible with both a digital and sustainable manufacturing vision of zero waste, as such, a non-destructive technique which utilises a novel X-ray diffraction surface integrity inspection method (XRD-SIIM) has been developed. This approach has been designed to complement traditional machinability-type assessments of tool life and machined surface topography, establishing a new process flow for validation. In this paper, for the first time, non-destructive on-machine validation of workpiece microstructural surface integrity is demonstrated, via a comparative investigation into the effect of insert grade, cutting speed and coolant delivery method on the depth of the imparted plastic deformation depth. It is shown that XRD-SIIM allows repeatable, non-destructive determination of deformed layers within a typical machining centre enclosure, with comparable findings to the incumbent cross-sectional microscopy approach. The generation of surface integrity digital fingerprints of a machining operation facilitates rapid comparison between testing variables, with a transition to an objective quantifiable assessment rather than one which open to subjectivity. In turn, XRD-SIIM expedites the development and benchmarking of new operations, tooling, materials, or coolant.

This paper presents an integrated approach for joint decision-making in reliable product design and resilient supply chain network design within a two-echelon capacitated network. Our methodology simultaneously addresses two crucial decisions faced by manufacturing industry by integrating the design processes of modular products and their supply chain networks, allowing for multiple production facilities and product variants. Through redundancy allocation, we optimize product reliability within production facilities while considering component sourcing from multiple suppliers. Our approach employs three resilience strategies—multi-sourcing, supplier fortification, and backup supplier contracting—to enhance supply chain resilience against disruptions. A case study solved using a genetic algorithm demonstrates the effectiveness of different resilience strategy combinations in achieving various levels of production resilience. This research offers insights into integrated decision-making for enhancing product reliability and supply chain resilience, thereby providing valuable guidance for industry practitioners. Furthermore, the sensitivity analysis highlights the framework’s capability to minimize total costs by prioritizing resilient designs and strategically investing in resilience strategies as costs of production shortage increase. This analysis underscores the interconnected nature of product and supply chain network design decisions in mitigating disruptions and enhancing production resilience.

The dynamics of thin-walled parts are highly affected by the clamping conditions. Clamping stiffness is a function of c lamping force and surface roughness profiles of the clamp and part. Since the surface profiles cannot be altered, estimating clamping stiffness as a function of the clamping force is essential to simulate vibrations of the machined thin-walled parts. This paper presents the modeling of clamping stiffness as a function of the applied clamping force and material properties. Surface profile parameters are estimated from the identified contact stiffnesses evaluated using the Finite Element (FE) model. The contact stiffnesses are either predicted directly from the proposed mechanics model of the part or estimated from the Fractal surface parameters. It is shown that an average clamping stiffness can be predicted from the Fractal surface parameters, or directly and more accurately from the static model of the clamped part.

High-speed milling (HSM) of Ti-6Al-4V is subjected to complex thermo-mechanical loads, leading to alteration in metallurgical conditions within the cutting deformation zones, adversely impacting the mechanical performances of manufactured products. Hence, inclusive insight into microstructural alterations within the Adiabatic Shear Band (ASB) and the milled surface becomes essential for better service performance. This study first developed a Finite Element (FE) milling model to simulate the machining process of Ti-6Al-4V. The proposed FE model is validated through experimental results regarding cutting forces (CFs), cutting temperature (CT), and chip geometry, where the absolute relative error between simulations and experiments was less than 15 %. Secondly, Zenner-Holloman (Z-H) and Hall-Petch (H-P) equations were incorporated into a user-defined subroutine to simulate dynamic recrystallization (DRX) for grain size and microhardness prediction. Simulation results revealed that the grains became finer in the ASB than on the milled surface. In particular, when the cutting speed and feed rate were increased to 350 m/min and 0.30 mm/tooth, the grain size in the ASB decreased from 14 to 0.68 and 0.44 µm, while in the topmost milled surface, it reduced to 7.06 and 6.75 µm, respectively. Conversely, microhardness exhibited an inverse correlation with grain size and increased with cutting speed and feed rate. Furthermore, the impact of increased plastic strain and temperature on the grains during chip segmentation was also examined. Finally, the proposed FE model validity was established by comparing simulated results with experimental data using advanced characterization techniques. This research significantly contributes to a comprehensive understanding of microstructural evolution and its implications for the mechanical performance of machined titanium components.