Journal of Manufacturing Processes最新文献

Joining of large aspect ratio WC-based cemented carbide and Si₃N₄-based ceramic at 1650 °C was achieved via spark plasma sintering by using functionally graded material (FGM) as a bonding layer. The influence of FGM layer numbers on the bonding quality of the large aspect ratio WC-FGM-Si₃N₄ composite materials was assessed based on thermal expansion coefficient and residual stress data. The microstructure analysis of the interface between adjacent layers revealed the formation of a structure with grain bidirectional anchoring, which promoted the enhancement of the bonding strength. High-strength joining of the large aspect ratio WC-FGM-Si₃N₄ composite rods with the average shear strength of 210.5 MPa was successfully realised. Furthermore, the rod was ground into an end mill, and the obtained end mill connection strength fulfilled the stringent machining conditions for dry milling of nickel-based superalloys.

Recent advancements in Metal Additive Manufacturing (MAM) are transforming manufacturing. Most research and market adoption of MAM have focused on Powder Bed Fusion (PBF), with less attention given to Directed Energy Deposition (DED), Binder Jetting (BJ), and Metal Material Extrusion (MEX), which are only now reaching industrial readiness. This increased availability of MAM processes provides SMEs with a wider range of options, opening up new opportunities that were previously inaccessible. However, despite recent technological improvements that broaden potential applications, the suitability of these processes for industrial use by SMEs is not yet well understood. SMEs currently face difficulties in adopting MAM due to complexities and costs. Moreover, existing literature often overlooks the distinct characteristics and needs of SMEs, making it challenging for them to identify the most suitable MAM processes. This study addresses this gap by using a fuzzy logic approach to evaluate the technical characteristics of PBF, DED, BJ, and MEX, focusing on their compatibility with SME requirements. Each process is ranked based on criteria including costs, complexity, energy consumption, mechanical quality, geometrical quality, speed, and market demand. This evaluation is refined through logarithmic normalization and scaling, resulting in a comprehensive scoring system from 1 to 5. Based on these findings, an SME-focused evaluation matrix is proposed to guide SMEs in selecting the most appropriate MAM process for their specific contexts. This matrix promotes informed and effective adoption strategies, supported by practical examples illustrating the application of each MAM process in SME environments.

Trimming technology is crucial for fixed abrasive pads (FAP) to maintain peak performance and prolonging their lifespan. However, there is a lack of effective evaluation methods for trimming FAP, which leads to under-trimming and over-trimming of FAP. As a result, it is very important to recognize and evaluate the critical trimming stage of the FAP. Therefore, the discriminative method for trimming FAP based on interfacial friction is proposed to determine the optimal trimming stage of FAP. Firstly, according to lapping experiments and tribological experiments of the FAP, the mapping relationship between the characteristics of interfacial friction and the parameters related to the FAP surface has been established. Secondly, the specific indices were identified to evaluate the surface state of FAP by the SEM analysis and mapping relationship of the FAP. Finally, the trimming thresholds of FAP were determined by combining the receiver operating characteristic (ROC) curves and Youden index, and the threshold achieved an average accuracy of over 92 % in recognizing the trimming requirements of the FAP. This research provides the theoretical basis for the online trimming technology and intelligent processing of FAP.

Metal additive manufacturing (AM) revolutionizes design with complex structures and customizable material properties. However, high-energy heat source utilized in metal AM creates non-uniform temperature gradients, which usually leads to significant residual stresses and deformations. The present study proposes a line source based semi-analytical thermo-mechanical approach. The accuracy of the proposed line source based model is thoroughly examined by comparing with corresponding experiments in the literature and other numerical models. Besides, an automated framework for fast construction of a thermo-mechanical FE model for the metal AM process is introduced. A numerical tool AM-builder is developed to bridge the gap between AM process parameters and the FE model. The proposed automated framework is followed by utilization of the AM-builder and the line source based model. A purely numerical example is conducted to demonstrate the effectiveness of the proposed automated framework in rapidly constructing AM thermo-mechanical models for various geometries.

Manufacturing through additive techniques, especially utilizing the fused filament fabrication (FFF) method, is a transformative technology revolutionizing design and manufacturing. FFF builds parts layer by layer using thermoplastic polymer and polymer composite filaments. However, weak interlayer connections often lead to low interlaminar resistance in printed structures. Recent studies suggest that post-deposition heat treatments can mitigate this issue by reducing internal thermal stresses and enhancing layer adhesion, thereby improving part properties. This research delves into the impact of annealing heat treatment on a composite of Polylactic Acid (PLA) reinforced with graphene. The annealing process was conducted at temperatures of 90, 100, and 120 °C for durations of 60, 120, and 240 min. The annealing response of the graphene-reinforced PLA composite is compared to that of natural PLA for reference, analyzing its effects on electrical, thermal, chemical, and mechanical properties. Chemical characterization was performed using X-ray diffraction (XRD), Raman spectroscopy, and Fourier-transform infrared spectroscopy (FTIR). Thermal properties were analyzed via thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Electrical properties were assessed by measuring resistance, resistivity, and conductivity. Mechanical properties were evaluated through tensile, flexural, notch sensitivity, impact tests, and hardness measurements. The results demonstrated that annealing of graphene-reinforced PLA and natural PLA did not alter their functional groups but increased their crystallinity. This treatment improved thermal stability, electrical conductivity, and mechanical properties, including tensile strength, flexural strength, and Shore D hardness. However, it reduced impact and notch sensitivity resistances. The increased crystallinity had a greater beneficial impact on some properties than the graphene reinforcement. These findings have potential applications in the automotive, aerospace, electronics, and medical sectors, given the increasing use of PLA in these industries.

Quantitative detection of welding spatter is not only critical for evaluating and optimizing welding energy consumption, but also significant for improving welding quality. Inspired by the welder's visual estimation of the amount of spatter, a novel welding spatter measurement method based on image segmentation is proposed. Considering the challenges of welding spatter such as small object, aggregation, and unclear boundary, four loss functions, namely, Focal loss, Dice loss, Boundary loss, and Count loss, are designed to achieve global optimization for complex welding spatter. Furthermore, for the different spatter characteristics with different optimization difficulties, a multi-loss dynamic fusion method with differential optimization capability is designed. Finally, a welding spatter segmentation dataset is established and a comprehensive ablation and comparison test of the proposed methodology is carried out. The results indicate that the proposed method has an average F1-score metric of 83.52 % and a mean intersection over union metric of 75.11 %. These findings suggest the potential for vision-based quantitative estimation of welding spatter.

Robotized wire-laser directed energy deposition (DED) is a highly anticipated technology with excellent material utilization and deposition efficiency for fabricating complex-structure metal parts. Nevertheless, the immaturity of the monitoring and control techniques for the deposition process stability are the main challenges in achieving automatic and repeatable manufacturing of metal parts. This study proposes a novel approach to monitor the process stability, i.e., the intersection point between the laser beam and the wire to the top layer distance (IPTD), in robotic wire-laser DED based on a designed multi-modal IPTD estimation model and coaxial visual sensing. The novelty is that directly extracting deposition height features from coaxial molten pool images is attempted based on deep learning. In particular, the designed multi-modal IPTD estimation model, consisting of a convolutional neural network (CNN) part and a fully connected network, combines the features of molten pool images and process parameters to estimate the IPTD state. Six model architectures and three image pixel sizes are used in the IPTD classification tasks to determine the CNN part architecture and the input image pixel size of the multi-modal deep learning model based on classification results. The regression performances of established single-modal and multi-modal IPTD estimation models are studied and discussed. Compared to other model architectures, the ResNet-18 model possesses the highest convergence rate during training and the best classification accuracy of 0.9975 on the testing dataset with the image pixel size of 180 × 105. The fitting accuracy and generalization performance of the multi-modal IPTD estimation model are marked superior to the single-modal IPTD estimation model. Validation experiments reveal the effectiveness of the proposed monitoring approach of process stability. This study will lay a solid foundation for the future control of process stability in robotic wire-laser DED.

For non-destructive testing (NDT) with X-ray computed tomography (XCT), the complex morphological segmentation of homogeneous defects is impossible for traditional threshold-based segmentation algorithm, due to their same absorbances. A variety of defect segmentation using convolutional neural networks (CNNs) has been suitable for such problems, and its accuracy is determined by image quality and comprehensiveness of training set. To achieve a high-performance measurement for quantitative defect evaluation, a modified U-Net-based segmentation model of XCT was proposed suitable for defects with various image quality resulting from different experimental efficiency. The dataset consisting of 24 subsets was acquired from the condition-varying measurements of aluminum alloy samples produced by laser 3D metal printing additive manufacturing. The structure and distribution of segmented pore and crack defects were accurately visualized by our model. Compared with traditional and main CNNs-based segmentation methods, our model exhibited a better recognition accuracy for pores and cracks, the Mean Intersection over Union and Pixel accuracy metrics reaching to 84.83 % and 98.87 % respectively. Practically, the option of efficiency or accuracy priority was quantitatively determined to carry out such a high-performance defect-related NDT. The proposed XCT-based segmentation mode has a great potential in fields of engineering, material science, biomedicine.

In Wire-Arc Additive Manufacturing (WAAM), ensuring the quality and integrity of components is of key importance and performing anomaly detection during production is an efficient strategy for preventing defects and maintaining an acceptable quality with lower costs compared to the development of complex supervised learning techniques. This paper presents an approach based on semi-supervised learning for real-time anomaly detection in the production of Inconel 718 components via the Pulsed Transfer WAAM process. The workflow is based on the use of training data obtained by employing established process parameters, similar to what is done in Welding Procedure Qualification Records (WPQRs), to develop a semi-supervised anomaly detection application. The proposed approach involves depositing material using the already established process parameters and collecting the corresponding welding data in terms of welding current and voltage sensor signals. The collected data are then employed to train an unsupervised algorithm by using solely good deposition data to learn hidden complex patterns of normality and hence detect anomalies based on deviations from these patterns. With this aim, global and local features are extracted from the welding current and voltage sensor signals in both time and time-frequency domains via Wavelet Transform technique and a deep learning approach based on a residual convolutional autoencoder. To showcase the effectiveness of the proposed approach, a case study involving wire arc additive manufacturing of Inconel 718 components was conducted. The proposed algorithm was tested and achieved an F-score of 0.895, representing a 30 % improvement over state-of-the-art methods that rely on time domain features for anomaly detection. This research work contributes to the improvement of anomaly detection methodologies, offering substantial advantages over alternative techniques for quality control and reliability of additive manufacturing processes such as WAAM.

GH536 as a typical solid solution strengthened Ni-based superalloy, has been widely used in the preparation of laminated cooling structure. Diffusion bonding, which is highly promising as a joining process in laminated cooling structure, generally requires high strength and high precision. However, the high strength joint fabricated by the conventional hot pressure diffusion bonding (HPDB) often leads to severe deformation of base materials. Here, we developed a new diffusion bonding strategy to overcome this issue in GH536 via pulsed current diffusion bonding (PCDB) and subsequent heat treatment. At low temperature and short time (900 °C/30 min), a joint with a high shear strength of 535 MPa and a low deformation rate of 0.42 % was obtained using the PCDB, and these were 1.60 times and 0.28 times that of the conventional HPDB joint, respectively. Meanwhile, the low bonding temperature and short bonding time hindered the formation and coarsening of carbides, allowing them to be eliminated in a short time heat treatment. The unbonded zones healing, carbide dissolution and recrystallization during heat treatment significantly improved the joint shear strength, and the joint shear strength after heat treatment reached 561 MPa.