Laser Solid Freeform Fabrication Research Articles

Towards additive manufacturing of compressor impellers: 3D modeling of multilayer laser solid freeform fabrication of nickel alloy 625 powder mixed with nano-CeO2 on AISI 4140

Gas turbine blades, turbine shafts and centrifugal compressor impellers are often damaged by erosion and/or corrosion. By laser cladding technique, a coating layer can be deposited on the base material in order to rebuild, repair and improve anti-erosion or anti-corrosion properties of the sensitive machine parts. In this paper, a three-dimensional finite element modeling of the laser solid freeform fabrication (LSFF) process for nickel alloy 625 powder mixed with nano-CeO2 on AISI 4140 steel is extensively studied. Using Comsol Multiphysics software and the finite element method (FEM), the heat transfer equation, moving mesh equation and stress tensor are numerically solved. The dynamic geometry of the molten zone is studied by a 3D moving mesh based on Arbitrary Lagrangian–Eulerian (ALE) module. Clad shape, temperature distribution and stress fields are obtained. The effects of preheating as well as addition of nano-CeO2 are investigated. Dependence of the clad height on the scanning velocity of the laser is also studied.

Three dimensional finite element modeling of laser solid freeform fabrication of turbine blades

This paper presents a three-dimensional simulation of laser solid freeform fabrication (LSFF) process for NiCoCrAlY powder with 2% nano-CeO2 on IN-738, using COMSOL MULTIPHYSICS and MATLAB. The heat transfer and mass transfer equations are solved by finite element method (FEM). Using this simulation the clad height, temperature distribution and stress fields can be obtained. For each layer, the clad height is predicted and the absorption coefficient for each layer is considered separately.

An experimentally derived hybrid intelligent tool for analysing and optimising the clad height and melt-pool depth in laser solid freeform fabrication process

In this investigation, a comparative experimental study is conducted to obtain an efficient hybrid intelligent framework for analysing and optimising the operating parameters of the laser solid freeform fabrication (LSFF) process. Here, the experimental studies are conducted in two different stages. In the first stage, different concepts of machine learning systems are taken into account to find a simple yet accurate intelligent model for identifying the LSFF process. To do so, multi-layered neural network with different types of analytical, gradient-based and heuristic learning strategies, i.e. extreme learning machine, back-propagation and steepest descend gradient-based learning and Nelder–Mead simplex heuristic, respectively, are adopted and applied to the LSFF process. In the second stage, different types of swarm- and evolutionary-based metaheuristics, i.e. differential evolutionary algorithm, particle swarm optimisation, the great salmon run, firefly algorithm, bee algorithm, are used to simultaneously find the optimal values of melt-pool depth and clad height during the LSFF process. The statistical results of the simulation indicate that the conducted experiments can result in a fast, robust and accurate hybrid intelligent system which can easily cope with the nonlinearities and uncertainties of the resulting optimisation problem.

Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map

The purpose of current investigation is to develop a robust intelligent framework to achieve efficient and reliable operating process parameters for laser solid freeform fabrication (LSFF) process as a recent and ongoing topic of investigation. Firstly, based on mutable smart bee algorithm (MSBA) and fuzzy inference system (FIS) two models are developed to identify the clad hight (deposited layer thickness) and the melt pool depth as functions of scanning speed, laser power and mass powder. Using the obtained model, the well-known multiobjective evolutionary algorithm called non-dominated sorting genetic algorithm (NSGA-II) is used for multi-criterion optimization of LSFF process. According to the available reported information and also the author's experiments, it is observed that the obtained Pareto front is not justifiable since it fails to cover the entire Pareto hyper-volume due to the lack of intensified exploration. To tackle this deficiency, authors execute a post optimization process through utilizing a competitive unsupervised machine learning approach known as self-organizing map (SOM) with cubic spatial topology. Achieved results indicate that this grid based network is capable of enhancing the intensification of Pareto solutions since its synaptic weights successfully imitate the characteristics of non-dominated solutions (optimal values of mass powder, laser power and scanning speed). For extracting the corresponding objective functions of these non-dominated synaptic weights, MSBA---FIS is used again to map the operating parameters to objective functions space. After the termination of abovementioned procedures, a valuable archive, containing a set of non-dominated solutions, is obtained which lets the authors to make a deliberate engineering trade-off. Simulation experiments reveal that the proposed intelligent framework is highly capable to cope with complex engineering systems. Besides, it is observed that MSBA is more efficient in evolving the structure of hierarchical fuzzy inference system in comparison with classic hierarchical GA-FIS model. This rises from the simple structure of MSBA that turns it into a fast and robust algorithm for handling constraint distributed systems (i.e. hierarchical FIS in current investigation). The obtained results also indicate that the introduced intelligent framework is applicable for optimal design of complex engineering systems where there exists no analytical formulation that describes the phenomenon as well as information of optimal operating parameters.

Optimal design of laser solid freeform fabrication system and real-time prediction of melt pool geometry using intelligent evolutionary algorithms

With the rapid growth of laser applications and the introduction of high efficiency lasers (e.g. fiber lasers), laser material processing has gained increasing importance in a variety of industries. Among the applications of laser technology, laser cladding has received significant attention due to its high potential for material processing such as metallic coating, high value component repair, prototyping, and even low-volume manufacturing. In this paper, two optimization methods have been applied to obtain optimal operating parameters of Laser Solid Freeform Fabrication Process (LSFF) as a real world engineering problem. First, Particle Swarm Optimization (PSO) algorithm was implemented for real-time prediction of melt pool geometry. Then, a hybrid evolutionary algorithm called Self-organizing Pareto based Evolutionary Algorithm (SOPEA) was proposed to find the optimal process parameters. For further assurance on the performance of the proposed optimization technique, it was compared to some well-known vector optimization algorithms such as Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA 2). Thereafter, it was applied for simultaneous optimization of clad height and melt pool depth in LSFF process. Since there is no exact mathematical model for the clad height (deposited layer thickness) and the melt pool depth, the authors developed two Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to estimate these two process parameters. Optimization procedure being done, the archived non-dominated solutions were surveyed to find the appropriate ranges of process parameters with acceptable dilutions. Finally, the selected optimal ranges were used to find a case with the minimum rapid prototyping time. The results indicate the acceptable potential of evolutionary strategies for controlling and optimization of LSFF process as a complicated engineering problem.

Identifying the behaviour of laser solid freeform fabrication system using aggregated neural network and the great salmon run optimisation algorithm

Bio inspiration is a branch of artificial simulation science that shows pervasive contributions to variety of engineering fields such as automated pattern recognition, systematic fault detection, machine learning and applied optimisation. In this paper, a new bio-inspired optimisation algorithm which is the simulation of 'the great salmon run' (TGSR) is developed. Thereafter, it has been used to predict the efficient structure of an aggregated artificial neural network (AANN) to identify the behaviour of laser solid freeform fabrication (LSFF) system. Our experiments show that the combination of AANN and an appropriate supervised method is best suit for modelling cited engineering process. To prove the superiority of TGSR in both robustness and quality, it has been compared with most of the state-of-the-art optimisation techniques such as fast simulated annealing (FSA), parallel migrating genetic algorithm (PMGA), differential evolutionary with parent centric crossover (DEPCX), unified particle swarm optimisation (UPSO), shuffle frog leaping algorithm (SFLA), artificial bee colony (ABC), firefly algorithm (FA) and cuckoo search (CS). The obtained results confirm the acceptable potential of the proposed method to be applied on complex engineering systems.

Surface finish in laser solid freeform fabrication of an AISI 303L stainless steel thin wall

Laser solid freeform fabrication is a flexible manufacturing technique that can be used to produce a near net shape component by consolidation of successive deposited layers of additive materials. This study investigates the effects of the main process parameters, the laser power and scanning speed, on the surface finish of AISI 303L thin walls through reducing the thickness of the deposited layers. To study the effects of the variations of the operating parameters, the process is simulated. The numerical results showed that by increasing the scanning speed the laser power must be adjusted in order to maintain the melt pool maximum temperature within a pre-defined value. In this way, the surface finish of the fabricated walls considerably improves due to the decreased thickness of each individual layer without significantly disturbing the melt pool and the main physical characteristics of the build-ups. This was also verified through experimentally fabrication of the walls and their microstructural examination.

Metal Embedded Optical Fiber Sensors: Laser-Based Layered Manufacturing Procedures

This paper describes laser-based layered manufacturing processes for embedding optical fiber Bragg gratings (FBGs) in metal structures to develop cutting tools with embedded sensors. FBG is a type of optical fiber that is used for the measurement of parameters manifesting as the changes of strain or temperature. The unique features of FBGs have encouraged their widespread use in structural measurements, failure diagnostics, thermal measurements, pressure monitoring, etc. Considering the unique features of FBGs, embedding of the sensors in metal parts for in-situ load monitoring is a cutting-edge research topic with a variety of applications in machining tools, aerospace, and automotive industries. The metal embedding process is a challenging task, as the thermal decay of UV-written gratings can start at a temperature of ∼200 °C and accelerates at higher temperatures. The embedding process described in this paper consists of low temperature laser microdeposition of on-fiber silver thin films followed by nickel electroplating in a steel part. A microscale laser-based direct write (DW) method, called laser-assisted maskless microdeposition (LAMM), is employed to deposit silver thin films on optical fibers. To attain thin films with optimum quality, a characterization scheme is designed to study the geometrical, mechanical, and microstructural properties of the thin films in terms of the LAMM process parameters. To realize the application of embedded FBG sensors in machining tools, the electroplating process is followed by the deposition of a layer of tungsten carbide-cobalt (WC-Co) by using laser solid freeform fabrication (LSFF). An optomechanical model is also developed to predict the optical response of the embedded FBGs. The performance of the embedded sensor is evaluated in a thermal cycle. The results show that the sensor attains its linear behavior after embedding. Microscopic analysis of the tool with the embedded sensor clearly exhibits the integrity of the deposited layers without cracks, porosity, and delamination.

On the microstructure of a thin wall formed under thermal and stress fields induced in laser solid freeform fabrication process

In laser solid freeform fabrication (LSFF), the final microstructures and consequently the physical properties of the successive deposited layers of additive materials are determined through melting, solidification and solid state transformations caused by a moving laser beam. In this paper, temperature distribution and stress field induced during the multilayer LSFF process and their correlation with the local microstructure formation are studied throughout the fabrication of a four-layer thin wall of SS304L. For this purpose, parallel to the experimental investigation, a coupled 3D time-dependent numerical model is employed to simulate the process. The numerical and experimental results show that stress concentrations formed at the end points of the wall are the locations prone to potential delaminations and crack formations. Different types of microstructures, such as dendritic with and without secondary arm spacings, are observed at various locations within the same layer due to different cooling rates.

Three dimensional finite element modeling of laser solid freeform fabrication of cobalt alloy stellite 21 with 1.5% nano[formula omitted] on the low carbon steel 1015

Our purpose is to model the multilayer laser solid freeform fabrication (LSFF) process for the material properties of low carbon steel 1015 for workpiece and cobalt alloy stellite 21 with 1.5 wt.% nano CeO2 as the powder particles. In this paper, transient heat transfer and mass transfer equations in laser solid freeform fabrication process are solved by Finite Element Method (FEM). In this approach, the geometry of the deposited material, temperature and thermal stress fields across the process area are predicted. For each layer the clad height is computed and computations are done by the use of MATLAB and COMSOL software.

On the delamination and crack formation in a thin wall fabricated using laser solid freeform fabrication process: An experimental–numerical investigation

Parts fabricated using laser solid freeform fabrication (LSFF) are subject to thermal stresses due to the layer-by-layer material deposition and the temperature distribution characteristic throughout the process domain. The thermal stress patterns and intensity contribute significantly to potential delamination and crack formation. In this paper, the temperature distribution and stress field induced during the multilayer LSFF process, and their correlation with delamination and crack formation are studied. This is performed by a numerical and experimental investigation in the fabrication of a thin wall of 304L stainless steel. For time-dependent predictions on the locations of maximum temperatures and thermal stresses and their patterns, a three-dimensional (3D) transient finite element model is employed to simulate the process, including the geometry of the deposited materials as well as coupled temperature and stress distributions across the process domain. The experimental results are used to verify the numerical results as well as to investigate the correlation between the numerical results and micro-crack formations across the fabricated parts. The experiments are conducted with the same process parameters used in the numerical analyses using a 1 kW Nd:YAG pulsed laser. The trend of numerical and experimental results reveals that by preheating the substrate prior to the fabrication process, it is possible to substantially reduce the micro-cracks formed across the part. To demonstrate the feasibility of preheating on the reduction of micro-cracks, several simulations and experiments are performed in which a crack-free result is obtained when the substrate is preheated to 800 K. For this case, 22% reduction in thermal stresses is obtained throughout the process domain.

A 3D dynamic numerical approach for temperature and thermal stress distributions in multilayer laser solid freeform fabrication process

This paper presents a 3D transient numerical approach for modeling the multilayer laser solid freeform fabrication (LSFF) process. Using this modeling approach, the geometry of the deposited material as well as temperature and thermal stress fields across the process domain can be predicted in a dynamic fashion. In the proposed method, coupled thermal and stress domains are numerically obtained assuming a decoupled interaction between the laser beam and powder stream. To predict the time-dependent geometry of the deposited material, once the melt pool boundary is obtained, the process domain is discretized in a cross-sectional fashion based on the powder feed rate, elapsed time, and intersection of the melt pool and powder stream area on the workpiece. Layers of additive material are then added onto the non-planar domain. Main process parameters affected by a multilayer deposition due to the formation of non-planar surfaces, such as powder catchment, are incorporated into the modeling approach to enhance the accuracy of the results. To demonstrate the proposed algorithm, fabrication of a four-layer thin wall of AISI 304 L stainless steel on a workpiece with the same material is modeled. The geometry of the wall, temperature, and stress fields across the modeling domain are dynamically predicted throughout the process. The model is used to investigate the effect of preheating and clamping the workpiece to the positioning table. Results show that preheating improves the process by reducing the thermal stresses as well as the settling time for the formation of a steady-state melt pool in the first layer. In addition, clamping the workpiece can also decrease thermal stresses at its critical locations (i.e. deposition region). In terms of geometrical aspects, the results show that the temperature and the thickness of the deposited layers increase at the end-points of layers 2–4. The reliability and the accuracy of the model are experimentally verified.

Fabrication of smart cutting tools with embedded optical fiber sensors using combined laser solid freeform fabrication and moulding techniques

To realize the concept of smart tools, embedding of fiber optic sensors in the metallic structure of a cutting tool with combined laser solid freeform fabrication (LSFF) and moulding is presented in this paper. Metallic parts with embedded optical fiber sensors are capable of monitoring physical parameters like force and temperature. These sensors are advantageous relative to other conventional electric and electromagnetic sensors due to their light weight, immunity to external electromagnetic fields, small size, long-term durability, and long-range linearity. In the present work, the optical fibers (e.g., fiber Bragg grating sensor, single-mode fiber optics) are moulded under tensile forces within a mild steel casing filled by Sn–Pb to fabricate a protective layer around them. Afterwards, LSFF is utilized to deposit tungsten carbide reinforced in cobalt (WC–Co) on the surface of the mild steel component. The performance results, in which the sensor exposed to a light bandwidth, show that the maximum light power loss after embedding is about 21% implying that the fiber is not damaged during the embedding process. Also, the sensor output has a linear characteristic under compression loadings indicating that the debonding of the fiber from the protective layer is not probable. The produced samples are examined by scanning electron microscopy and X-ray diffraction to assess the physical properties of the tool. Microstructural images reveal no cracks and porosity around the fiber indicating a good bonding between the fiber and the surrounding media. Material characterizations of the manufactured tool are also discussed.

Prediction of laser solid freeform fabrication using neuro-fuzzy method

In this paper, a new application of a neuro-fuzzy method (ANFIS) to laser solid freeform fabrication (LSFF) is presented. The laser solid freeform fabrication process is a complex manufacturing technique that cannot be modeled analytically due to non-linear behaviours of the physical phenomena involved in the process. A neuro-fuzzy model is proposed to predict the clad height (coating thickness) as a function of laser pulse energy, laser pulse frequency, and traverse speed in a dynamic fashion. Four membership functions are assigned to be associated with each input of the model architecture. Experiments are performed to collect data for the training of the proposed network, and a set of unseen experimental data are also considered for the verification of the identified model. The effects of the assigned inputs on the clad height are discussed. The comparison between the experimental data and the model output shows promising results. The model can predict the process with an absolute error as low as 0.07%.

Three-dimensional numerical approach for geometrical prediction of multilayer laser solid freeform fabrication process

This article presents the development of a three-dimensional numerical method for predicting transient geometrical and thermal characteristics of multilayer laser solid freeform fabrication as a function of process parameters and material properties. In the proposed method, the thermal domain is numerically obtained, assuming the interaction between the laser beam and powder stream is to be decoupled. Once the melt pool boundary is obtained, the physical domain is discretized in a cross-sectional direction. Based on the powder feed rate, elapsed time, and intersection of the melt pool and powder stream area substrate, layers of additive material are then added onto the nonplanar domain. A standard object is fit to each added layer to facilitate the numerical analysis of successive layers. Variations in physical parameters due to formation of nonplanar surfaces are incorporated into the model to increase the accuracy and reliability of the simulated results. The developed model was used to predict the geometrical and thermal properties of a four-layer thin wall of AISI 4340 steel. The results show that the temperature and the thickness of the deposited layers sensibly increase at the end point of layers 2, 3, and 4. Also, the powder catchment efficiency for the first layer is significantly lower than those of successive layers. The experimental results demonstrate the validity of the developed numerical methodology.

Clad height control in laser solid freeform fabrication using a feedforward PID controller

In this paper, a feedforward proportional-integral-derivative (PID) controller is developed to effectively control the clad height in laser solid freeform fabrication (LSFF). The scanning velocity is selected as the input control variable and the clad height is chosen as the output. A novel knowledge-based Hammerstein model, including a linear dynamic and a nonlinear memoryless block, is developed, and its parameters are identified offline using experimental data. The architecture of the controller consists of a PID and a feedforward module, which is the inverse of the identified model. The advantage of adding a feedforward path to the PID controller is evaluated experimentally, in which the results show a lower overshoot and faster response times. Also, the performance of the controller is verified in the presence of geometrical disturbances, as well as in the fabrication of a nonplanar part.