International Journal of Advanced Manufacturing Technology最新文献

Abstract

Ultrasonic additive manufacturing (UAM) is an advanced joining technique that utilizes ultrasonic vibrations to bond layers of metal foil together. UAM offers several benefits over traditional manufacturing methods, including enhanced design flexibility and reduced material waste, and its potential applications in various industries such as aerospace, automotive, and biomedical engineering are being actively explored. The study employs a nanoindentation apparatus to investigate the effect of the UAM process on the local mechanical properties of the bonded interface, along with changes in microstructure, which were investigated using scanning electron microscopy and electron back-scattered diffraction. The results revealed a significant correlation between material hardness and local plasticity. EBSD has revealed that the grain size distribution of Al far from the interface contains 57% of the grains less than 3 µm in size, while at the interface this number rises to approximately 78%, indicating that the average grain size decreases as it approaches the interface. This result is consistent with nanoindentation results that demonstrated a gradual change in the hardness of Al foil far from the interface to close to the interface (the maximum penetration depth near the interface was 500 nm less than far from the interface). Both EBSD and nanoindentation disclose the effect of work hardening close to the interface, which is related to dislocation accumulation with a density of (8.6times {10}^{-10} {{text{cm}}}^{-2}) beneath the interface. The consistency of hardness and Young’s modulus with the pole figures and microscopic images demonstrated that plasticity flow and fine grain distribution would only occur in the vicinity of the interface in the softer metal region. Although the harder metal did not exhibit plasticity or recrystallization, the hardness, and Young’s modulus map indicated the formation of a layer of small grains close to the interface on the aluminum side owing to strain hardening and dynamic recrystallization.

This work addresses the problem of the development of a robotic system for the picking of parts cut by a CNC machine and the optimization of the sequencing of this picking process. An automated parts collection system is optimized to reduce the time required to perform the task of both picking and the subsequent classification by the type of part. The automated picking system, which is located at the end of a cutting machine, uses a robot equipped with an additional axis to expand its working space. Therefore, in this proposal, the industrial equipment necessary to automate this process is designed and the process to be optimized is computationally modeled. In particular, three discrete optimization algorithms are analyzed, with different evolution strategies and operators, but all of them are free of specific configuration parameters. The whole process is shown in this research, from the design of the procedure to the design of the tool, the algorithm selection, and elements validation. Finally, the first steps towards its industrial implementation are presented, and the hypothesis behind this project is validated.

This paper describes a novel end-to-end approach for automatic welding-robot programming based on a product-process-resource (PPR) model, for one-of-a-kind manufacturing systems. Traditionally, the information needed to program a welding robot is processed and transferred along the manufacturing organisation’s value chain by using several stand-alone digital systems which require extensive human input and high skill to operate. A PPR model is proposed through this research as a platform for storing and processing the necessary information along the value chain seamlessly. Unlike existing approaches which make use of complex algorithms to automatically identify the weldment seams, the approach suggested in this research makes use of information already digitalised by design engineers under the form of ISO 2553:2019 compliant weldment annotations. Hence, the PPR model contains the weldment annotations; it enables the automatic programming of welding robots and reduces human input down to a few minutes only. The applicability in manufacturing of the theoretical concept is demonstrated through technical implementations tested in the laboratory and on the value chain of an engineering-to-order (ETO) industrial partner involved in the metal fabrication industry. The experiments were conducted by creating several products using the proposed artefact. Experiments show that automatic programming of welding robots can be achieved using PPR models. The conducted experiments showed a reduction of about 80% in human input measured in terms of time, when using the proposed solution. The reduction of the human input can free up skilled labour resource which ETO SMEs can reallocate to other tasks.

This paper presents constitutive equations that describe the hot flow behaviour of Virgin (VG) X20 and rejuvenated heat-treated creep exhaust (CE) X20 steels. The study provides a foundation for determining the effect of rejuvenation heat treatment on CE steels by making comparisons to the VG steel. Hot compression tests were conducted in the temperature range of 900 °C to 1050 °C, at strain rates of 0.1–10 s⁻¹ to a total strain of 0.6, and stress–strain curves were obtained. The flow stress curves of both steels exhibited dynamic recovery (DRV) characteristics as the main softening mechanism. Constitutive constants of steady-state stresses were determined. The stress exponents, n, were 6.62 (VG) and 5.58 (CE), and the apparent activation energy values were 380.36 kJmol⁻¹(VG) and 435.70 kJmol⁻¹ (CE). Analysis of the activation energies showed that VG steel had better workability properties than CE steel and was easier to deform at high temperatures. Constitutive equations for predicting the flow stress in the two steels were established. This were verified by statistical tools: Pearson’s correlation coefficient (R) and Absolute Average Relative Error (AARE). The results showed R-values were, 0.98 (VG) and 0.99 (CE), and the AARE values for VG were 4.17% and 9.01% for CE. The statistical parameters indicated a good correlation between the experimental and predicted values. The constitutive equations therefore adequately described the flow stress behaviour of both steels and can therefore efficiently analyse industrial metal forming schedules.

Abstract

Supermartensitic stainless steel (SMSS) UNS S41426 is an extra-low carbon steel with 12–13%Cr-5%Ni-2%Mo (%wt.) and microadditions of Ti and V. This material offers an interesting combination of mechanical and corrosion resistance. Although the weldability was improved in relation to conventional martensitic steels, due to the drastic reduction of carbon content, post-weld heat treatments are still necessary to decrease the hardness of the heat affected zone (HAZ). The UNS S41426 is used to manufacture mandrels for chemical products or gas injection in the well in the oil and gas off-shore production. Those mandrels are constructed with forged parts and hot rolled seamless pipes joined by welding. The microstructure, hardness, toughness, and sensitization of simulated HAZ of SMSS UNS S41426 forged and hot rolled were investigated. The effect of single tempering at 650 °C for 5 min and at 620 °C for 1 h, as well as double tempering (670 °C/2 h + 600 °C/2 h), was analyzed. The short duration tempering treatments did not change considerably the microstructure, but provoked an undesirable decrease of toughness. The single tempering for 1 h and the double tempering promoted more important microstructural changes, accompanied by the decrease of hardness and the increase of the degree of sensitization.

Laser beam welding (LBW) has been widely employed to acquire defect-free joints between aluminum alloys for a wide range of applications, especially within the automotive industry. The current study aims to examine the effect of laser power on the final microstructure, as well as the mechanical properties of laser beam welded AA5754 and AA6063 aluminum alloys. Lap joints of the abovementioned alloys were performed using a 1030-nm Yb:YAG LBW process with a laser power of 3000 W and 3500 W. The microstructure of base metals (BM), heat-affected zone (HAZ), and fusion zone (FZ) was investigated by means of visible light microscopy (VLM) under non-polarized and polarized light, as well as of scanning electron microscopy (SEM) in conjunction with energy-dispersive spectroscopy (EDS), while the crystal structure was evaluated by X-ray diffraction (XRD). The mechanical properties of welded samples were investigated through Vickers microhardness and tensile shear tests. Furthermore, the fracture surfaces were observed under a stereoscope and a SEM. The metallographic examination revealed the presence of small defects, such as pores, with a diameter ranging from 20 to 50 μm, and microcracks, whose length ranged from 300 to 400 μm. Reducing the laser power was observed to affect the weld geometry, and more specifically the penetration, that was found at 900 μm for the samples welded with 3500 W and 333 μm for those welded with 3000 W. It was also noticed that reducing the laser power resulted in decreased width of the HAZ; the samples welded with 3000 W had a HAZ width of approximately 400–500 μm, while the samples welded with 3500 W had a HAZ width of 500 μm. Finally, applying higher laser power was observed to improve the mechanical properties of welded samples, resulting in higher relative ductility and fewer microhardness fluctuations within the FZ. The specimens welded with 3500 W presented increased tensile shear force and displacement of 3.8 kN in comparison to 3.4 kN of the joints welded with 3000 W.

The two-dimensional GTAW arc model is insufficient to fully explain the effects of the external longitudinal magnetic field on the arc. Therefore, a three-dimensional GTAW arc model was established to elucidate the recirculation flow and negative pressure arc characteristic of magnetic-controlled arc. The external magnetic field controls the surface temperature, pressure, and current density distribution of the workpiece by controlling the flow of arc plasma. When the magnetic flux density is 0.04T, the surface temperature, pressure, and current density of the workpiece exhibit a bimodal distribution. Under the negative pressure of the GTAW arc, the arc plasma on both sides flowed counterclockwise around the center in the spiral upward direction and then counterclockwise downward from 0.9 mm below the tungsten electrode. By controlling the welding current and magnetic flux density, the occurrence of negative surface pressure on the workpiece can be effectively controlled.

Metallic glass-reinforced metal matrix composites (MMCs) are in the focus of attention of many research groups due to the outstanding properties provided by a combination of ductile crystalline matrix and high-strength glassy phase. To date, many fabrication techniques have been used to form such composites. Most of them are based on pressure-assisted sintering of glassy and crystalline components. However, the selection of the heating temperature and holding time is challenging due to the low thermal stability of the metallic glasses (MGs). In this study, a solid-state magnetic pulse welding (MPW) technique was used for manufacturing laminated Ti-based composites with Zr-based MG reinforcement. The structure of the interfaces between Ti and MG layers was studied using light microscopy (LM), scanning electron microscopy (SEM), and synchrotron X-ray diffraction (SXRD). The experimental study was supplemented with smoothed-particle hydrodynamics (SPH) numerical simulations. The Ti-MG-Ti composite obtained by MPW possessed high quality of joint and had no macroscopic defects such as cracks or lack of fusion. The formation of a firm joint was provided by the plastic flow of titanium. Deformation processes in the titanium plates developed mainly in the interfacial zones, while the MG ribbons subjected to deformation by shear mechanism through the entire thickness. Due to the short-term thermal impact and high cooling rates, MPW retained a disordered structure of MG, despite local melting occurring at the interfaces and in shear bands. Tensile tests of composites containing 5 vol. % and 13 vol. % of MG phase showed that their strength follows the rule of mixtures.

A simple and inactive structure is able to transform into a complex and active one via four-dimensional (4D) printing. Controlling bending deformation, activation time, and temperature is crucial in 4D printing. This study aimed to comprehensively evaluate and analyze the effect of different process parameters on the bending deformation of polylactic acid (PLA) shape-morphing produced by material extrusion additive manufacturing. These parameters included layup, layer thickness, printing speed, nozzle temperature, nozzle diameter, and bed temperature. Since the bending deformation is significantly affected by the specimen wall, this study has focused, for the first time, on the simultaneous influence of process parameters and presence of a wall on the deformation. Furthermore, the study examined the influence of printing parameters on activation time and activation temperature. The results indicated that increasing the pre-strain stored in the parts led to a decrease in activation time and activation temperature. Subsequently, the Taguchi design of experiment method was used to optimize the most influential parameters on the bending deformation. The difference between the optimal predicted and the experimental deformations was less than 2%. Layer thickness, layup, nozzle temperature, and printing speed were recognized as the most effective parameters for controlling deformation, respectively.

Abstract

This work presents a novel adaptable framework for multi-objective optimization (MOO) in metal additive manufacturing (AM). The framework offers significant advantages by departing from the traditional design of experiments (DoE) and embracing surrogate-based optimization techniques for enhanced efficiency. It accommodates a wide range of process variables such as laser power, scan speed, hatch distance, and optimization objectives like porosity and surface roughness (SR), leveraging Bayesian optimization for continuous improvement. High-fidelity surrogate models are ensured through the implementation of space-filling design and Gaussian process regression. Sensitivity analysis (SA) is employed to quantify the influence of input parameters, while an evolutionary algorithm drives the MOO process. The efficacy of the framework is demonstrated by applying it to optimize SR and porosity in a case study, achieving a significant reduction in SR and porosity levels using data from existing literature. The Gaussian process model achieves a commendable cross-validation R2 score of 0.79, indicating a strong correlation between the predicted and actual values with minimal relative mean errors. Furthermore, the SA highlights the dominant role of hatch spacing in SR prediction and the balanced contribution of laser speed and power on porosity control. This adaptable framework offers significant potential to surpass existing optimization approaches by enabling a more comprehensive optimization, contributing to notable advancements in AM technology.