Концепция поддержки лазерного аддитивного производства на основе онтологического подхода

Additive manufacturing (AM) of metallic parts is widely utilized for industrial applications. However, quality issues of the printed parts, including part distortion and cracks caused by high temperature and fast cooling, result in high residual stress. This is a challenge that limits the industry acceptance of AM. To overcome this challenge, a numerical modeling method for predicting part distortion at the design stage plays an important role and enables design engineers to remove failures before printing as well as determine the optimal printing process parameters to minimize part deformation. This research proposes an inherent strain-based part deformation prediction method. To determine the inherent strain (IS) value, a micro-scale model for analyzing the temperature distribution is constructed. The IS value is calculated from the temperature gradient. Then, the IS value is used for determining the part deformation. The proposed methodology has been developed and evaluated using a 316L stainless steel cantilever beam in both simulations and experimental results.

Current trends in the industry go towards individualised parts in high volume production. Facing the high amount of different parts, efficient processes with a high flexibility are necessary. Within this context, laser-based additive manufacturing of metal parts become more important. These processes enable the production of individual geometries using a variety of metal materials. However, main challenges of this technology are rough surfaces, limited geometric precision and varying material properties. Therefore machining after the additive manufacturing process is a common approach for improving surface quality and geometric accuracy. However, the machining process affects the grain structure at the edges of the part. Using forming instead leads to a deformation of the grains but does not create new interfaces. Furthermore, the material properties can be improved due to work hardening and fibre flow. Within this work, a bulk metal forming process of an additively manufactured element of stainless steel 316L will be analysed. The aim of the investigation is a fundamental understanding of the forming behaviour and the material flow of an additively built metal part compared to the conventionally manufactured material. Therefore mechanical properties of both materials will be identified by upsetting tests. Additionally, fibre flow and Vickers hardness before and after the forming operation are analysed. It is detected that the forming behaviour of the additively manufactured material is strongly influenced by the layer-wise building process.

The presence of anomalies in components generated using laser-based Additive Manufacturing (AM) processes greatly affects mechanical properties, making anomaly detection and prevention significant for the improvement of AM. Melt pool thermal imaging of laser-based AM processes such as Direct Energy Deposition (DED) provide rich data from which anomalies can be detected. Although through feature-based learning methods (e.g., support vector machine and k-nearest neighbor) or end-to-end deep learning models (e.g., self organizing map) the anomalies can be identified, the sparsity of defective instances and lack of interpretable analysis, together with high dimensionality and velocity of melt pool images further increase the difficulty of anomaly detection. We propose implementing Neural Vector Quantized Variational Autoencoder (Neural-VAE) to analyze thermal images from a DED process to determine where and when anomalies occur, indicating a flaw in the generated component. A Vector Quantized VAE is designed to represent high-dimensional images as low-dimensional vectors. Then, hyperdimensional computing is introduced to leverage the low-dimensional representation of image data and determine defective and non-defective images. Our proposed methodology can detect anomalies with an accuracy, sensitivity, specificity, and f-score of 99.2%, 99.6%, 98.8%, and 99.2%, respectively, while successfully generating anomalous images.

Integrated Modelling and Simulation of the Additive Manufacturing and Heat Treatment Process Chain

Chapter 14 - Laser aided metal additive manufacturing and postprocessing: a comprehensive review

Smart-Alloying enables local modification of the chemical composition of metallic components within additive manufacturing processes. This new method represents an innovative process for localized grading of metallic materials in laser-based additive manufacturing. Smart-Alloying involves applying nanoparticulate alloying elements onto the surfaces of additive manufactured components in precise doses using suspensions. The method is shown here using laser powder bed fusion as an example. By coating the base material powder and exposing it to the laser source, localized modifications in chemical composition and altered properties can be achieved. Using a chromium-rich ferritic stainless steel, significant alterations in the fundamental microstructure are attained through localized re-alloying of carbon. Investigation utilizing focused ion beam-scanning electron microscope elucidates the evolution of increasing carbon content within the re-alloyed area. Next to the formation of chromium-rich carbides and ultrafine regions of needle-like martensite, retained austenite could be made visible. Transmission electron microscopy investigations proved these findings and revealed mainly α-ferritic microstructure with small γ-austenitic grains as well as the presence of chromium-rich carbides on the main grain boundaries. The completely renewed microstructure is then further modified using secondary exposures of the laser source. Under the influence of the subsequent melting process, there is an increasing distribution of the re-alloyed carbon, resulting in a completely new structure of a line-shaped duplex microstructure. The integration of secondary exposure introduces a completely novel aspect, offering the potential to merge localized grading via Smart-Alloying with targeted applications of laser parameters.

Additive Manufacturing (AM) or 3D printing defines a family of technologies for making objects of almost any shape from virtual 3D model data through additive processes in which successive layers of material are laid down. Laser‐based AM processes like laser‐sintering, selective laser‐melting or stereolithography, differ in the materials that can be used as well as the smallest features that can be achieved in the final parts. Driven by the trend of miniaturization, common high‐resolution AM technologies like stereolithography reach their limitations, when the typical layer thickness in the range of 50 to 150 μm does not suffice to meet application demands. This is where the innovative technique of 3D laser lithography provides a significant advantage. The process of two‐photon polymerization enables the fabrication of high‐resolution 3D micro‐objects with heights from a few hundred nanometers up to several millimeters and layer thicknesses well below 1 μm.

The ever-growing interest in additive manufacturing (AM) is evidenced by its extensive utilisation to manufacture a broad spectrum of products across a range of industries such as defence, medical, aerospace, automotive, and electronics. Today, most laser-based AM is carried out by employing continuous-wave (CW) and long-pulsed lasers. The CW and long-pulsed lasers have the downside in that the thermal energy imparted by the laser diffuses around the irradiated spot and often leads to the creation of heat-affected zones (HAZs). Heat-affected zones may degrade the material strength by producing micro-cracks, porous structures and residual stresses. To address these issues, currently, attempts are being made to employ ultrafast laser sources, such as femtosecond (fs) lasers, in AM processes. Femtosecond lasers with pulse durations in the order of 10−15 s limit the destructive laser–material interaction and, thus, minimise the probability of the HAZs. This review summarises the current advancements in the field of femtosecond laser-based AM of metals and alloys. It also reports on the comparison of CW laser, nanosecond (ns)/picosecond (ps) lasers with fs laser-based AM in the context of heat-affected zones, substrate damage, microstructural changes and thermomechanical properties. To shed light on the principal mechanisms ruling the manufacturing processes, numerical predictions are discussed and compared with the experimental results. To the best of the authors’ knowledge, this review is the first of its kind to encompass the current status, challenges and opportunities of employing fs lasers in additive manufacturing.

It is no secret that the laser was the driver for additive manufacturing (AM) of 3D objects since such objects were first demonstrated in the mid-1980s. A myriad of techniques utilizing the directed energy of lasers were invented. Lasers are used to selectively sinter or fuse incremental layers in powder-beds, melt streaming powder following a programmed path, and polymerize photopolymers in a liquid vat layer-by-layer. The laser is an energy source of choice for repair of damaged components, for manufacture of new or replacement parts, and for rapid prototyping of concept designs. Lasers enable microstructure gradients and heterogeneous structures designed to exhibit unique properties and behavior. Laserbased additive manufacturing has been successful in producing relatively simple near net-shape metallic parts saving material and cost, but requiring finish-machining and in repair and refurbishment of worn components. It has been routinely used to produce polymer parts. These capabilities have been widely recognized as evidenced by the explosion in interest in AM technology, nationally. These successes are, however, tempered by challenges facing practitioners such as process and part qualification and verification, which are needed to bring AM as a true manufacturing technology. The ONR manufacturing science program, in collaboration with other agencies, invested in basic R&D in AM since its beginnings. It continues to invest, currently focusing on developing cyber-enabled manufacturing systems for AM. It is believed that such computation, communication and control approaches will help in validating AM and moving it to the factory floor along side CNC machines. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Recent advances in additive manufacturing (AM) have attracted significant industrial interest. Initially, AM was mainly associated with the fabrication of prototypes, but the AM advances together with the broadening range of available materials, especially for producing metallic parts, have broaden the application areas and now the technology can be used for manufacturing functional parts, too. Especially, the AM technologies enable the creation of complex and topologically optimised geometries with internal cavities that were impossible to produce with traditional manufacturing processes. However, the tight geometrical tolerances along with the strict surface integrity requirements in aerospace, biomedical and automotive industries are not achievable in most cases with standalone AM technologies. Therefore, AM parts need extensive post-processing to ensure that their surface and dimensional requirements together with their respective mechanical properties are met. In this context, it is not surprising that the integration of AM with post-processing technologies into single and multi set-up processing solutions, commonly referred to as hybrid AM, has emerged as a very attractive proposition for industry while attracting a significant R&D interest. This paper reviews the current research and technology advances associated with the hybrid AM solutions. The special focus is on hybrid AM solutions that combine the capabilities of laser-based AM for processing powders with the necessary post-process technologies for producing metal parts with required accuracy, surface integrity and material properties. Commercially available hybrid AM systems that integrate laser-based AM with post-processing technologies are also reviewed together with their key application areas. Finally, the main challenges and open issues in broadening the industrial use of hybrid AM solutions are discussed.

Solid oxide fuel cells (SOFCs) are electrochemical conversion devices which directly convert chemical energy of the fuel into electrical energy with high efficiency. SOFC comprises of an anode, electrolyte and cathode layer that are fabricated individually upon each other, usually one at a time. Additive manufacturing (AM) is a new fabrication technique that utilizes a layer by layer fabrication technique, which can potentially be suitable for use in SOFC, since it is also building different materials in a layer-wise manner. Furthermore, the use of laser-based AM techniques can eliminate the issue of long sintering hours by directly densifying the materials using laser as the heat source. In addition, SOFCs possessing newer designs can be fabricated with ease using this technique, gradient-structure electrodes can also be created either by design or varying the laser parameter and material composition. Therefore, this thesis aims to develop a process as well as study the feasibility for fabricating SOFC using laser-based AM techniques. First, a step-by-step process was conducted to identify significant factors affecting the building of Nickel metal support structures using Selective Laser Melting (SLM) process. Laser power, scanning speed and hatch spacing were found to be the dominant factors affecting the printability of the parts. Next, a porous structure was designed and printed which acted as a metal support for SOFC. A functional cell was further built upon the metal support and the electrochemical performance was investigated. Although a low open circuit voltage (OCV) of 0.53 V was obtained with a maximum power density of 11.0 mW/cm2 from the cell, it demonstrated potential of this technique as a new fabrication process. In order to realize the fabrication of the entire SOFC by laser-based AM, the influence of laser on SOFC materials were conducted. The effect of two different laser sources, Nd:YAG fibre laser and CO2 laser was studied on NiO-YSZ anodic material, which showed that CO2 xvi laser was better absorbed by the oxide material. In addition, dendrite features were formed on the surface, which were influenced by the rapid solidification process and could be modified by adjusting laser processing parameters. Although the laser-modified SOFC did not perform as well as the conventional one, it was found that significant improvement was made on the charge transfer kinetics. Lastly, the influence of laser on YSZ electrolyte surface showed that the polarization resistance of the cell was reduced due to better oxygen adsorption/dissociation process. These experiments demonstrated that laser-based AM is a viable fabrication tool for SOFC.

Residual stress analysis of additive manufacturing of metallic parts using ultrasonic waves: State of the art review

A conceptual architecture of the cloud knowledge portal for laser-based additive manufacturing (AM) is proposed. Its information resources and program components are specified, their features and approaches to development are described. The architecture is founded on the two-level ontological technique for creation of databases and a knowledge base (KB) for laser-based AM (LBAM), as well as on the principle of separating information (data and knowledge) from the tools that process it. The coupling of these components is brought to the level of ontologies. All ontologies are explicitly distinguished from databases and KBs. The information resources of the knowledge portal are a set of interrelated ontologies, reference books, a KB for the LBAM mode settings, a structured case database, and a database of mathematical models describing thermodynamic processes that accompany laser directed energy deposition (LDED) technology. The program components of the portal are ontology-driven editors for the formation and maintenance of all databases and the KB, and a hybrid intelligent decision support system. The latter is based on the methods of knowledge engineering, case-based reasoning, as well as on the use of traditional mathematical modeling of the processes that accompany LDED.

Laser-based additive manufacturing (LBAM) is a group of advanced manufacturing processes used to produce metal components and functionally graded products. Production in LBAM is either limited to the formation of thin or thick coatings on a substrate by laser metal deposition or the production of a fully functional metallic product by selective laser melting. In every case, LBAM fabricated components require optimization for the process parameters to avoid defects, such as porosity, crack holes, thermal deformation, and mechanical strength. As a key link in the laser additive manufacturing (LAM) process, laser scanning path planning is an effective strategy for balancing the temperature field of the formed part, avoiding stress concentration, and preventing deformation and cracking. Efficient, accurate, and reasonable planning of the laser scanning path is of great significance for improving the processing efficiency of the process data, prolonging the life of the laser scanning system, and improving the forming quality of the specimen. Through many studies, it was found that the scanning pattern of the lasers has a significant impact on the mechanical properties and deformations caused by a thermal mismatch during the process. Therefore, it is essential to have in-depth knowledge about path planning in LBAM. Our review mainly focuses on the influence of scanning patterns on deformation, temperature, and mechanical properties in LBAM. Finally, our paper discusses the current study limitations and some future studies in LAM technology.

Solid-state additive manufacturing of tantalum using high-pressure cold gas-dynamic spray