International Journal of Advanced Manufacturing Technology最新文献

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.

The constant development of new materials, such as ceramics with high wear resistance, makes it necessary to adapt machining processes by creating new methods or improving existing ones. Moreover, grinding is widely used as a finishing process in workpieces since it provides excellent surface roughness and dimensional tolerances. Due to the high volume of cutting fluid used in conventional lubrication (flood), associated with harmful effects such as contamination and intoxication, alternative methods have become highly necessary. The minimum quantity lubrication (MQL) technique reduces fluid use but has low cooling capacity, in addition to barely removing chips, making it essential to seek improvements to make it competitive. In this sense, adding water to the MQL can improve the cooling capacity at the cost of reducing lubrication. At the same time, applying a wheel cleaning jet (WCJ) of compressed air assists in chip removal. Thus, this work aims to analyze the results of external cylindrical grinding of alumina (Al₂O₃) using a synthetic diamond grinding wheel, comparing the techniques of conventional MQL, diluted MQL, and MQL + WCJ against the flood method. Surface roughness, roundness deviation, diametrical wheel wear, G ratio, grinding power, tangential cutting force, grinding cost analyses, and CO₂ pollution emission were conducted for this. The results show that adding water to MQL, associated with WCJ, significantly improved roughness, wear, and grinding power. Furthermore, the analysis of costs and pollution showed that using MQL significantly reduced costs and CO₂ emissions, proving the socio-environmental advantage of the MQL method against the flood method.

Abstract

Laser metal deposition (LMD) is of the directed energy deposition (DED) process which is widely used for producing large-scale, dense, and functional parts in the field of additive manufacturing (AM). This research work investigates the microstructure and mechanical properties of PH 13–8 Mo martensitic stainless-steel parts produced via LMD. The workshop trials were conducted using an LMD system collaborated with a robotic arm to deposit single-track thin walls and horizontal blocks. The microstructural characteristics of the additively manufactured parts were analyzed using an optical microscope. The mechanical properties were evaluated through hardness measurements and uniaxial tensile tests. The influence of energy density and powder deposition density on the characteristic geometry of straight walls was also investigated. The microstructural analysis showed that the microstructure consisted of columnar dendrites that grew epitaxially from the substrate, with primary austenite cells containing intercellular ferrite and martensite laths that were roughly parallel with the retained austenite. When the energy density increased from 43 to 86 J/mm² (a doubling of energy density), there was an increase in secondary dendritic arm spacing (SDAS) by approximately 250% in the first layer and approximately 90% in the top layer. The difference in SDAS change between the first and top layers can be attributed to the difference in cooling rates experienced by each layer during the additive manufacturing process. Increasing powder deposition density from 0.5 to 1 g/min results in a decrease in porosity from 3% to less than 1% and an increase in strength from 800 to over 1000 MPa. The hardness of the deposits was found to range from 300 to 400 HV. This variation in hardness can be attributed to differences in microstructure resulting from changes in cooling rates at different heights.

The escalating significance of 3D printing in various industries is underscored by its ability to rapidly and cost-effectively produce distinctive parts. Among the 3D printing methods, fused filament fabrication (FFF) has emerged as a highly productive and cost-effective approach. While extensive efforts have been made to enhance the qualities of FFF products, challenges persist in material availability and quality compared to traditional methods. This study provides a meticulous overview of the FFF process, delving into various 3D printing processes, polymers, and polymer composites. Despite documented efforts to augment mechanical, thermal, and electrical properties, material constraints remain a focal point. Our analysis extends to various PLA/biocomposites, shedding light on achieved improvements and potential applications. Looking forward, the future trend in FFF technology suggests a paradigm shift towards enhanced material diversity and performance. Anticipated applications span beyond traditional use cases, encompassing sustainable manufacturing, medical devices, and eco-friendly construction materials. This comprehensive review not only consolidates the current state of FFF and PLA-biocomposites but also anticipates future trends and potential applications. This research enhances the current knowledge of additive manufacturing and sets a standard for assessing developments in FFF technology by comparing them to previous works.

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