Cirp Annals-Manufacturing Technology最新文献

The safety, health, and well-being of human workers are crucial for socially sustainable production systems, especially in Industry 5.0. Occupational exoskeletons, particularly back-support devices, are increasingly being adopted to reduce musculoskeletal disorders and human fatigue. To reduce costs and weight, optimized exoskeleton design is being explored. A 3D-printed cycloidal reduction stage for the actuation unit is proposed, focusing on an interaction torque observer and an impedance-based controller for human-robot interaction. The device's dynamic characterization and control are analyzed to evaluate its applicability to a sensorless back-support occupational exoskeleton.

Enhancing autonomy and applicability of robotic systems across diverse scenarios, requires efficient environment perception. Conventional vision systems are highly performing but limited to simple tasks, while AI based ones require extensive data collection, processing and training. This paper presents a framework leveraging generative AI and Neural Networks to implement a dynamically updateable perception system. A multimodal conditional Generative Adversarial Network generates large image datasets which are automatically annotated by a Large Multimodal Model. A Convolutional Neural Network performs further dataset augmentation. A case study on the inspection of aircraft fuel tanks is used to showcase the potential of the approach.

Creasing and folding are fundamental steps in many manufacturing processes of multi-material paperboard packaging. The complex structure of these materials, which comprise layers of cellulose fibres, aluminium, and polyethylene, coupled with the growing complexity of packaging designs, make these process operations essential to ensure the required structural integrity for packaging as well as their functionality in daily life. This paper introduces an approach for modelling damage in paper-based sandwich materials by integrating fibre-based and cohesive numerical modelling techniques. The results prove the effectiveness of the proposed methodology, opening new possibilities for process design and optimization in packaging manufacturing.

This paper proposes a novel microforming limit curve (MFLC) prediction model that accounts for surface roughening and suggests appropriate surface roughness indices for use with the model. Parallel calculations of the Parmar, Mellor, and Chakrabarty (PMC) model and Marciniak–Kuczynski (M-K) model define the switching point of the dominant phenomenon. The potential for high-precision forming limit prediction for micro-precision presses is demonstrated based on comparisons with experimental values for pure aluminium foil. The proposed model demonstrates MFLC predictions with fine precisions by applying the maximum valley depth as a surface roughness index for pure aluminium foil.

Electrochemical jet machining (EJM) on concave surfaces or cavities is challenging because the jet and film flow fail to form. This work presents an in-situ electrolytic gas and plasma assistance approach to enable EJM under electrolyte. A structured nozzle cathode induces pressurized and insulating gas around electrolyte at the gap, generating a constrained jet and film flow. This serves to allow a precise and effective submerged EJM (SEJM) routine. Compared to EJM in air, SEJM shows more concentrated current distribution owing to a thinner film flow by the gas assistance, leading to a 65 % improvement in surface finish and a 16 % reduction of machining overcut.

This study presents a novel machine learning approach for predicting the anisotropic parameters of the Yld2000–2d non-quadratic yield function using a hole expansion test. Heterogeneous stress-strain fields during the test substitute for multiple experiments required in the conventional parameter identification approach. An artificial neural network model for the parameter prediction is developed using a virtually generated training dataset composed of strains from hole expansion simulations, performed using randomly selected anisotropic parameters. The developed model predicts the Yld2000–2d parameters for AA6022-T4 based on the measured strain field from a hole expansion experiment, and the parameter results are evaluated by comparing anisotropy in uniaxial tension tests, the yield locus, and thinning variation in hole expansion test.

This study addresses challenges in design and fabrication of thermally auxetic structures with zero thermal expansion (ZTE) using multi-material laser powder bed fusion. Planar 316L-CuCr1Zr lattices with re-entrant and triangular unit cells were designed, manufactured and tested. Introducing beam curvature as a new design parameter effectively reduces the coefficient of thermal expansion (CTE) compared to standard designs with straight struts. Curved beams act like non-linear springs and allow accommodating internal strains in the lattice. Despite the slight thermal expansion differences of CuCr1Zr and 316L, a curved-beam lattice is identified that mimics Invar's CTE up to 200 °C.

This paper presents a new analytical model for prediction of cutting forces in binary materials such as metal matrix composites (MMC) based on the cutting power. The model considers the effect of matrix shearing, reinforcement particles fracture and debonding as well as frictional contact between the tool and the particles to predict cutting forces required to form free surfaces. Linear orthogonal cutting on Al2124-SiC MMC with different cutting speed and depth of cuts were performed. The developed model shows a better performance compared with other available models in the literature to predict cutting forces while the experimental results reveal shearing and fracture as the main chip formation mechanism.

The large acquisition times needed for high-quality XCT measurements remain a stumbling block for high-throughput inspection tasks. This paper therefore presents a deep learning reconstruction algorithm to improve the quality of fast, few-view XCT measurements. The proposed method is validated on both simulated and experimental XCT measurements of additively manufactured cranio-maxillofacial implants. The validation demonstrates a drastic reduction in noise and streaking artifacts associated with few-view acquisitions. Therefore, the potential to maintain high reconstruction quality while reducing acquisition times by more than one order of magnitude is confirmed.

Lattice structures offer advantages in load-bearing applications in terms of structural efficiency and strength-to-weight ratio. Previous structure optimization methods were mainly based on discretized structures without incorporating manufacturing capabilities, thus ad-hoc treatments or redesigns were required to enable fabrication. This paper presents a ‘rotation vector’ based method that parameterizes lattice structures and directly generates curved printing paths, which is particularly suitable for multi-axis additive manufacturing using fiber-reinforced filaments. The proposed method simultaneously considers structure-stress alignment and layer-wise fabrication to achieve optimized design with enhanced strength-to-weight ratio, which is parametrically adaptable to different printing configurations and infill densities.