International Journal of Advanced Manufacturing Technology最新文献

Abstract

The conventional repairing of CFRP (continuous fiber-reinforced polymer composites) includes complicated steps of patching, splicing, repairing, and post-curing. Intensive labor work needs to be conducted, and poor surface quality and weak interfacial adhesion are usually observed. This work mainly introduces an in situ online repairing method using AM (additive manufacturing) facilitated composite fabrication. With the advances of the robotic-assisted AM process, the surface roughness and accuracy during the repairing process can be evaluated online upon layer-by-layer process. In order to fulfill the efficient and on-site requirements for repairing damage in structural components, this study explores the method including in situ repairing, laser point clouds online collection, and repairing path planning based on multi-axial additive manufacturing of composites. A repair algorithm is proposed incorporating point clouds collection, measurement evaluation, and path planning. Furthermore, relevant mechanical measurements have been conducted, so as to assess the interface degree of recovery. A rapid online evaluation and surface conformal repairing method have been proposed to overcome the technical bottleneck of in situ automatic repairing of damaged composites. It expands the application of multi-axial robot-assisted CFRP AM.

Deriving inspiration from natural hierarchical superhydrophobic surfaces, multi-scale structures were manufactured on AA6082 surfaces via wire electrical discharge machining (WEDM), featuring microscale texture due to spark erosion, superimposed upon a wide-range simple and more complicated geometries of submillimeter profiles. The effect that the higher-order scale morphologies had on wettability was investigated. The dual-scale morphology elevated the hydrophobicity of the surfaces compared to single-scale or unmodified surfaces, reaching superhydrophobicity (151°) in the case of a certain triangular profile. Rectangular and triangular profiles facilitated the higher contact angles, while re-entrant geometries were able to totally prevent cavity wetting. A correlation of static contact angle with roughness parameters of the larger scale such as Ra, Rz, Rp, Rsk, and Rku for certain geometry configurations was identified. Peak hydrophobicity resulted at Ra = 70 μm, Rz = 240 μm, and Rp = 160 μm concerning simple geometries. Negative Rsk and Rku > 1.5 affected negatively contact angle of samples. All investigated tested types were found to reach higher hydrophobicity at moderate drop volumes (5 μl). The fabricated samples were anisotropic in at least two directions, showing decreased hydrophobicity in the front, parallel to the groove direction. When tested in multi-directional dynamic tilting up to 90°, the more complicated geometries were able to retain resistance to spreading. All samples demonstrated superliquiphilicity with lower surface tension liquids, making them strong candidate in applications such as oil/water separation. Finally, all samples tested sustained their hydrophobic character subsequent to a 3-month atmospheric exposure period.

The reasonable allocation of geometric errors of machine tools can improve their machining accuracy reliability (MAR). However, due to the complexity and high nonlinearity of limit state function (LSF) of MAR, the fitting accuracy is usually low when the traditional method is used to approximate LSF. To solve this problem, a doubly-weighted vector projection response surface (DWVPRS) method, which considers not only the approximation results of test sample points (TSPs) to LSF but the distances between TSPs and the most probable failure point (MPFP), is proposed. Using the reliability sensitivity analysis method, the key geometric errors were identified and optimized. Finally, taking a large gantry guideway grinding machine as an example to verifies the effectiveness and correctness of the DWVPRS method proposed in this paper, the results show that compared with the traditional methods, the DWVPRS method has the highest fitting accuracy to approximate LSF at the MPFP, and after the optimization of geometric accuracy, both the minimum and average reliability values of the grinding machine meet the design requirements.

This work aims at investigating the causes affecting the dimensional and geometrical accuracy of holes in metal binder jetting stainless steel parts. Parallelepiped samples with a through hole were produced using AISI 316L and 17-4PH powders, differing for diameter (3, 4, 5 mm), and position of the axes with respect to the building plane (6, 9, 12 mm distance). Dimensions and geometrical characteristics were measured at green and sintered state by a coordinate measuring machine, determining the dimensional change and the geometrical characteristics. As expected, the shrinkage of linear dimensions is anisotropic; moreover, change in volume and sintered density are significantly affected by the position in the printing chamber. Higher shrinkage was measured along building direction (Z) – 18.5 ÷ 19.5%, than in the building plane – 16.5 ÷ 17.5%, and slightly higher shrinkage – 0.5 ÷ 0.8% was measured along powder spreading direction (X) than binder injection direction (Y). A variation up to 3% in relative density of sintered parts depending on the position in the building plane was observed in 316L. The dimensional change of diameters generally confirmed the shrinkage predicted by the model previously developed—difference between real and expected dimensional changes lower than 3%, except for three geometries (4 ÷ 6%). The cylindricity form error of sintered parts was strongly underestimated by the prediction model (up to 0.15 mm), but underestimation was considerably reduced (generally lower than 0.05 mm) adding the cylindricity form error due to printing. Dimensional and geometrical accuracy of holes are strongly affected by shape distortion of the parallelepiped geometry, in turn due to layer shifting and inhomogeneous green density during printing, and to the effect of frictional forces with trays during sintering. Gravity load effect was also observed on the holes closest to the building plane. Future work will improve the reliability of the prediction model implementing the results of the present work.

Fiber composite materials exhibit exceptional specific stiffness and strength compared to traditional engineering materials. Nevertheless, automating the handling of limp flexible materials like fabrics remains a challenging process, often relying on multi-stage manual operations for hand layups. In this study, carbon fabric properties were initially characterized through standard experiments to develop and calibrate a finite element (FE) model. The FE model was subsequently validated against real-world pick-and-place tests involving soft robotic grippers. The validation results demonstrated a high correlation between the FE model and experiments, achieving an average accuracy of 97.2% for fabric projected area and 84.6% for fabric vertices’ displacement. Additionally, the FE model was used to design, evaluate, and optimize alternative automation strategies. It was discovered that a convex surface improved fabric projection area and placement accuracy by 5.9% and 1.9%, respectively, compared to a concave surface with the same curvature radius. Larger concave surfaces contributed to increased projected area and placement accuracy as well. Longitudinal pick-and-place operations also enhanced the projection area and placement accuracy compared to transverse handling processes. Achieving successful fabric pick-and-place operations necessitates a comprehensive system’s approach, considering the interaction between grippers, fabric, and mold surface. The FE model developed in this study will be further employed by the current research team in designing innovative compliant grippers tailored to complex mold surface geometries and specific fabric material requirements. The presented FE model offers valuable insights and paves the way for rapid, efficient, cost-effective, and secure implementation of automation solutions for handling limp flexible materials.

The service performance of hot stamped ultra-high strength steel after die punching was evaluated in this paper through typical experiments, including tensile tests, bending tests, and hydrogen embrittlement tests, as well as characterization analysis. The tested samples were prepared through a specially designed tool by considering different die clearances, punch corner radii, and punch shapes. The results showed that the tensile properties are closely related with the selected punching parameters, while the bending properties and hydrogen embrittlement susceptibility are less sensitive to parameters. Furthermore, the laser cutting method was also involved to produce holes on hot stamped parts and compared with the punching samples. It was found that the laser cutting samples performed better in terms of tensile properties and hydrogen embrittlement susceptibility, which were due to the effects of annealing treatment during laser cutting that decreases the material hardness and brings compressive residual stress near the cutting surface, while the bending properties are insensitive to different drilling methods.

Abstract

The digital twin is driving the machine manufacturing and processing workshop to change in the direction of digital intelligence and service. Aiming at the application requirements of virtual simulation monitoring of typical CNC machine tools for the unified interaction and integration of processing and production process data, this paper proposes a development architecture of virtual simulation monitoring and processing process optimization system for CNC machine tools that integrate data, model, communication, and optimization. The data semantic format and data communication are normalized by designing the OPC UA information model of CNC machine tools, modular construction of a three-dimensional digital model, and interactive mapping technology of OPC UA server address space. Virtual simulation visualization and monitoring of CNC machine tools are realized by integrating synchronous simulation modeling, collision detection, and viewpoint control technologies. Building upon this foundation, the control process of the CNC machine tool machining cell is optimized using ECRS and lean production methods. The application focuses on a typical flexible manufacturing cell (FMC) in a machine tool processing and manufacturing workshop. The development of the virtual simulation visualization monitoring system for FMC addresses challenges such as heterogeneous data interaction, sharing, and integration difficulties across multiple heterogeneous equipment. The system successfully fulfills all required functions, and the optimization of the CNC machine tool machining unit’s control process has enhanced equipment utilization and productivity. This solution effectively supports the realization of intelligent manufacturing services, including standardized data-driven digital twins.

Friction lap welding was used to join aluminum with glass fiber–reinforced thermoset polymer (GFRP) using a thermoplastic interlayer. The effect of different welding parameters on joint strength and fracture surfaces was investigated, and the optimal welding parameters were determined using the Taguchi method. Results revealed that low heat generation led to weak mechanical interlocking between GFRP and the thermoplastic interlayer, while high heat generation caused degradation of the aluminum/thermoplastic polymer interface. The tool traverse speed was found to be the most influential parameter in terms of joint strength, followed by plunge depth and rotational speed. Moreover, thermal measurements were conducted during the welding process using thermocouples. An uneven thermal distribution was discovered across the overlap area due to dissimilar substrates. This issue was resolved by incorporating aluminum thermal insulation, resulting in improved heat distribution and a significant enhancement of 94% in joint strength. Scanning electron microscopy (SEM) was employed to identify joining mechanisms and examine the effect of welding parameters on joint microstructure. Furthermore, Fourier-transform infrared spectroscopy (FTIR) was used to investigate chemical bond formation at the aluminum/thermoplastic polymer interface. The results showed that the joining mechanisms involved mechanical interlocking between the thermoplastic interlayer and aluminum, as well as chemical bonding, penetration, and intertwining between the thermoplastic interlayer and the thermoset composite.

The superposition effect of various cutting mechanisms (CM) in the fine drilling process brings great challenges to the accurate characterization of the cutting stress field of the workpiece. To solve the above problem, the cutting stress characterization modeling and parameter identification for the fine drilling process with multiple cutting mechanisms is studied in this paper. Firstly, two cutting mechanisms (shear-slip and plough-slip) are distinguished according to the relative tool sharpness (RTS) which is determined by the cutting tool radius and cutting depth, and the fine characterization model for drilling stress of the workpiece is constructed by considering the two cutting mechanisms. Then, in order to overcome the problem that model parameters are difficult to be accurately determined, the sub-interval decomposition optimization method (SDOM) and the improved particle swarm optimization (PSO) are employed to identify parameters in the model. Finally, the proposed method is verified by comparing the single cutting mechanism model, the multiple cutting mechanisms model, and the actual characterization parameter model.