Additive manufacturing is a promising tool that has proved its value in various applications. Among its technologies, the fused filament fabrication 3D printing technique stands out with its potential to serve a wide variety of applications, ranging from simple educational purposes to industrial and medical applications. However, as many materials and composites can be utilized for this technique, the processability of these materials can be a limiting factor for producing products with the required quality and properties. Over the past few years, many researchers have attempted to better understand the melt extrusion process during 3D printing. Moreover, other research groups have focused on optimizing the process by adjusting the process parameters. These attempts were conducted using different methods, including proposing analytical models, establishing numerical models, or experimental techniques. This review highlights the most relevant work from recent years on fused filament fabrication 3D printing and discusses the future perspectives of this 3D printing technology.
The recovery of signals with finite-valued components from few linear measurements is a problem with widespread applications and interesting mathematical characteristics. In the compressed sensing framework, tailored methods have been recently proposed to deal with the case of finite-valued sparse signals. In this work, we focus on binary sparse signals and we propose a novel formulation, based on polynomial optimization. This approach is analyzed and compared to the state-of-the-art binary compressed sensing methods.
In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.
Laser Wire-Feed Metal Additive Manufacturing (LWAM) is a process that utilizes a laser to heat and melt a metallic alloy wire, which is then precisely positioned on a substrate, or previous layer, to build a three-dimensional metal part. LWAM technology offers several advantages, such as high speed, cost effectiveness, precision control, and the ability to create complex geometries with near-net shape features and improved metallurgical properties. However, the technology is still in its early stages of development, and its integration into the industry is ongoing. To provide a comprehensive understanding of the LWAM technology, this review article emphasizes the importance of key aspects of LWAM, including parametric modeling, monitoring systems, control algorithms, and path-planning approaches. The study aims to identify potential gaps in the existing literature and highlight future research opportunities in the field of LWAM, with the goal of advancing its industrial application.
Improving the vibration isolation for the seat of small vehicles under low excitation frequencies is important for providing good comfort for the driver and passengers. Thus, in this study, a compact, low-dynamic, and high-static stiffness vibration isolation system has been designed. A theoretical analysis of the proposed quasi-zero stiffness (QZS) isolator system for vehicle seats is presented. The isolator consists of two oblique springs and a vertical spring to support the load and to achieve quasi-zero stiffness at the equilibrium position. To support any additional load above the supported weight, a sleeve air spring is used. Furthermore, the two oblique springs are equipped with a horizontal adjustment mechanism that is aimed to reach higher frequencies with the existed stroke when a heavy load is applied. The proposed system can be fitted for small vehicles, especially for B-segment and C-segment cars. Finally, the simulation results reveal that the proposed system has a large isolation frequency range compared to that of the linear isolator.
Laser Wire Additive Manufacturing (LWAM) is a flexible and fast manufacturing method used to produce variants of high metal geometric complexity. In this work, a physics-based model of the bead geometry including process parameters and material properties was developed for the LWAM process of large-scale products. The developed model aimed to include critical process parameters, material properties and thermal history to describe the relationship between the layer height with different process inputs (i.e., the power, the standoff distance, the temperature, the wire-feed rate, and the travel speed). Then, a Model Predictive Controller (MPC) was designed to keep the layer height trajectory constant taking into consideration the constraints faced in the LWAM technology. Experimental validation results were performed to check the accuracy of the proposed model and the results revealed that the developed model matches the experimental data. Finally, the designed MPC controller was able to track a predefined layer height reference signal by controlling the temperature input of the system.
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