Thermophysical Phenomena in Metal Additive Manufacturing by Selective Laser Melting: Fundamentals, Modeling, Simulation and Experimentation

Among the many additive manufacturing (AM) processes for metallic materials, selective laser melting (SLM) is arguably the most versatile in terms of its potential to realize complex geometries along with tailored microstructure. However, the complexity of the SLM process, and the need for predictive relation of powder and process parameters to the part properties, demands further development of computational and experimental methods. This review addresses the fundamental physical phenomena of SLM, with a special emphasis on the associated thermal behavior. Simulation and experimental methods are discussed according to three primary categories. First, macroscopic approaches aim to answer questions at the component level and consider for example the determination of residual stresses or dimensional distortion effects prevalent in SLM. Second, mesoscopic approaches focus on the detection of defects such as excessive surface roughness, residual porosity or inclusions that occur at the mesoscopic length scale of individual powder particles. Third, microscopic approaches investigate the metallurgical microstructure evolution resulting from the high temperature gradients and extreme heating and cooling rates induced by the SLM process. Consideration of physical phenomena on all of these three length scales is mandatory to establish the understanding needed to realize high part quality in many applications, and to fully exploit the potential of SLM and related metal AM processes

A novel numerical framework for simulation of multiscale spatio-temporally non-linear systems in additive manufacturing processes.

New computationally efficient numerical techniques have been formulated for multi-scale analysis in order to bridge mesoscopic and macroscopic scales of thermal and mechanical responses of a material. These numerical techniques will reduce computational efforts required to simulate metal based Additive Manufacturing (AM) processes. Considering the availability of physics based constitutive models for response at mesoscopic scales, these techniques will help in the evaluation of the thermal response and mechanical properties during layer-by-layer processing in AM. Two classes of numerical techniques have been explored. The first class of numerical techniques has been developed for evaluating the periodic spatiotemporal thermal response involving multiple time and spatial scales at the continuum level. The second class of numerical techniques is targeted at modeling multi-scale multi-energy dissipative phenomena during the solid state Ultrasonic Consolidation process. This includes bridging the mesoscopic response of a crystal plasticity finite element framework at inter- and intragranular scales and a point at the macroscopic scale. This response has been used to develop an energy dissipative constitutive model for a multi-surface interface at the macroscopic scale. An adaptive dynamic meshing strategy as a part of first class of numerical techniques has been developed which reduces computational cost by efficient node element renumbering and assembly of stiffness matrices. This strategy has been able to reduce the computational cost for solving thermal simulation of Selective Laser Melting process by ~100 times. This method is not limited to SLM processes and can be extended to any other fusion based additive manufacturing process and more generally to any moving energy source finite element problem. Novel FEM based beam theories have been formulated which are more general in nature compared to traditional beam theories for solid deformation. These theories have been the first to simulate thermal problems similar to a solid beam analysis approach. These are more general in nature and are capable of simulating general cross-section beams with an ability to match results for complete three dimensional analysis. In addition to this, a traditional Cholesky decomposition algorithm has been modified to reduce the computational cost of solving simultaneous equations involved in FEM simulations. Solid state processes have been simulated with crystal plasticity based nonlinear finite element algorithms. This algorithm has been further sped up by introduction of an interfacial contact constitutive model formulation. This framework has been supported by a novel methodology to solve contact problems without additional computational overhead to incorporate constraint equations averting the usage of penalty springs

Additive Manufacturing Technologies and Applications

The present Special Issue proposes articles in the area of Additive Manufacturing with particular attention to the different employed technologies and the several possible applications. The main investigated technologies are the Selective Laser Sintering (SLS) and the Fused Deposition Modelling (FDM). These methodologies, combined with the Computer Aided Design (CAD), provide important advantages. Numerical, analytical and experimental knowledge and models are proposed to exploit the potential advantages given by 3D printing for the production of modern systems and structures in aerospace, mechanical, civil and biomedical engineering fields. The 11 selected papers propose different additive manufacturing methodologies and related applications and studies

Rapid prototyping of micro-optics for brightness restoration of diode lasers

Abstract unavailable please refer to PD

Modeling and Experimental Techniques to Demonstrate Nanomanipulation With Optical Tweezers

The development of truly three-dimensional nanodevices is currently impeded by the absence of effective prototyping tools at the nanoscale. Optical trapping is well established for flexible three-dimensional manipulation of components at the microscale. However, it has so far not been demonstrated to confine nanoparticles, for long enough time to be useful in nanoassembly applications. Therefore, as part of this work we demonstrate new techniques that successfully extend optical trapping to nanoscale manipulation. In order to extend optical trapping to the nanoscale, we must overcome certain challenges. For the same incident beam power, the optical binding forces acting on a nanoparticle within an optical trap are very weak, in comparison with forces acting on microscale particles. Consequently, due to Brownian motion, the nanoparticle often exits the trap in a very short period of time. We improve the performance of optical traps at the nanoscale by using closed-loop control. Furthermore, we show through laboratory experiments that we are able to localize nanoparticles to the trap using control systems, for sufficient time to be useful in nanoassembly applications, conditions under which a static trap set to the same power as the controller is unable to confine a same-sized particle. Before controlled optical trapping can be demonstrated in the laboratory, key tools must first be developed. We implement Langevin dynamics simulations to model the interaction of nanoparticles with an optical trap. Physically accurate simulations provide a robust platform to test new methods to characterize and improve the performance of optical tweezers at the nanoscale, but depend on accurate trapping force models. Therefore, we have also developed two new laboratory-based force measurement techniques that overcome the drawbacks of conventional force measurements, which do not accurately account for the weak interaction of nanoparticles in an optical trap. Finally, we use numerical simulations to develop new control algorithms that demonstrate significantly enhanced trapping of nanoparticles and implement these techniques in the laboratory. The algorithms and characterization tools developed as part of this work will allow the development of optical trapping instruments that can confine nanoparticles for longer periods of time than is currently possible, for a given beam power. Furthermore, the low average power achieved by the controller makes this technique especially suitable to manipulate biological specimens, but is also generally beneficial to nanoscale prototyping applications. Therefore, capabilities developed as part of this work, and the technology that results from it may enable the prototyping of three-dimensional nanodevices, critically required in many applications

Process planning for robotic wire ARC additive manufacturing

Robotic Wire Arc Additive Manufacturing (WAAM) refers to a class of additive manufacturing processes that builds parts from 3D CAD models by joining materials layerupon- layer, as opposed to conventional subtractive manufacturing technologies. Over the past half century, a significant amount of work has been done to develop the capability to produce parts from weld deposits through the additive approach. However, a fully automated CAD-topart additive manufacturing (AM) system that incorporates an arc welding process has yet to be developed. The missing link is an automated process planning methodology that can generate robotic welding paths directly from CAD models based on various process models. The development of such a highly integrated process planning method for WAAM is the focus of this thesis

In-situ monitoring and intermittent controller for adaptive trajectory generation during laser directed energy deposition via powder feeding

Laser Directed Energy Deposition (LDED) is one of the advanced manufacturing technologies for building near-net-shaped engineering components in a layer-by-layer fashion using high-power lasers as an energy source. LDED using powder feeding (LDED-PF) is widely used due to its higher dimensional accuracy and ability to build fine features. The quality and performance of LDED-PF-built components are dependent on several factors such as process parameters, process conditions, feedstock properties, system configuration, tool-path generation, etc. Among the above, trajectory control is one of the emerging and active areas of research. Generally, trajectories are developed offline for printing the parts. However, some of the major challenges involved in conventional trajectory development for LDED-PF are the propensity for collision between the deposition head/ nozzle and the part being built and challenges in building components with variable overhang. The major goal of this work is the development of adaptive trajectory control of the LDED-PF process using online and offline techniques to build high-quality components. The work involves the offline trajectory development to build complex-shaped components with variable overhang angles by considering collision between the nozzle head and the part; adaptive layer thickness for higher dimensional accuracy. In addition, the work is extended to the development of online and intermittent trajectory control using a combination of in-situ surface quality monitoring and machine learning technique. Offline trajectory planning is performed for two complex-shaped geometries such as a hemispherical dome and a bent pipe. Offline adaptive trajectory planning for hemispherical dome involves the development of an algorithm including the deposition parameters with variable overhang and collision checking, while the trajectory planning for building bent pipe structures includes the deployment of adaptive slicing in addition to the collision check and overhang angle deposition. To manufacture the dome, the tilt angle is used to avoid the collision between the nozzle and previously built material with a condition that the tilt angle cannot exceed the maximum allowable overhang angle. The algorithm verifies the tilt angle suitable to build the dome and the angle is transferred from the tilt angle to the tilt angle of the rotary table. In order to build the bent pipe geometry, the variation in scanning speed is used to realize the adaptive slicing, which aids in having point-to-point variable layer height thereby permitting non-parallel deposition. In addition, changing the tool orientation during the deposition permits the manufacturing of support-free bent pipe parts as observed for dome structures. LDED-PF of the hemispherical dome and bent pipe was performed using the developed algorithms and the built geometries have good dimensional stability and density. In the case of online trajectory planning, a novel in-situ monitoring software platform was developed for the online surface anomaly detection of LDED-PF parts using machine learning techniques. The above starts with the development of a novel method to calibrate the laser line scanner with respect to the robotic end-effector with sub 0.5 mm accuracy. Subsequently, 2D surface profiles obtained from the LDED-PF built part surface using the laser scanner are stitched together to create an accurate 3D point cloud representation. Further, the point cloud data is processed, and defect detection is carried out using unsupervised learning and supervised (deep) learning techniques. Further, the developed defect detection software platform was used to create an online adaptive toolpath trajectory control platform to correct the dimensional inaccuracies in-situ. It uses a laser line scanner to scan the part after the deposition of the definite number of layers followed by the detection of concave, convex, and flat surfaces using deep learning. Further, the developed adaptive trajectory planning algorithm is deployed by using three different strategies to control material deposition on concave, convex, and flat surfaces. The material deposition is controlled by using adaptive scanning speed, and a combination of laser on-off and scanning speed. Subsequently, the built geometries are subjected to geometric, microstructure, and mechanical characterizations. The study offers an integrated and complete methodology for developing high-quality components using LDED-PF with a minimal dimensional deviation from the original CAD model

Applicability of a Picosecond Laser for Micro-Polishing of Metallic Surfaces

An increasing number of recent technological advancements is linked to the adoptions of ultra-short pulsed picosecond (ps) lasers in various material processing applications. The superior capability of this laser is associated with precise control of laser-material interaction resulted from extremely short interaction time. In this context, the present study explored the applicability of a ps laser in laser micro-polishing (LµP) of Inconel 718 (IN718) and AISI H13 tool steel. The melting regime ‒ a mandatory phase for LµP ‒ was determined experimentally by the variation of focal offset to attain desired laser fluence. The finite element formulation of heat transfer equation and its solution were also estimated in order to develop a theoretical foundation for the heat transfer mechanism in ps laser-material interaction. The initial one dimensional (1D) line polishing experiments were performed on ground IN718 and H13 tool steel samples with the parameters related to the melting regime of corresponding material. The knowledge of this initial experimental investigation was later utilized to prepare the surface topography by micromilling with a specific step-over and scallop height, followed by LµP experiments with the same set of aforementioned parameters. The performance of LµP was evaluated by average surface roughness (Ra) spectrum at different spatial wavelength intervals along the laser path trajectory. Additionally, statistical measures, such as power spectral density (PSD) function, transfer function (TF) and material ratio (MR) curve were analyzed in order to establish the process parameters resulting the best possible surface quality. From the analysis of this experimental investigation, surface quality improvement up to 78.5% and 75.7% were reported for the spatial wavelength interval of 50‒100 µm for IN718 and H13 tool steel respectively. As a next step, two dimensional (2D) areal polishing of micromilled IN718 and H13 tool steel were performed, where surface quality improvement up to 69.32% and 77.28% were observed for the spatial wavelength interval of 50‒100 µm for IN718 and H13 tool steel, respectively. Overall, ps LµP was found to be an effective way of enhancing desired surface quality as demonstrated by the reduction of surface asperities as well as their volumetric uniform redistributions