Browsing by Author "Malekipour, Ehsan"

Now showing 1 - 10 of 11
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    A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process
    (MDPI, 2021-10) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; Agarwal, Mangilal; Mechanical and Energy Engineering, School of Engineering and Technology
    Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.
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    Computation of conductive thermal distribution using non-homogenous graph theory for real-time applications in metal PBF process
    (Elsevier, 2022-09) Malekipour, Ehsan; El-Mounayri, Hazim; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    The Powder Bed Fusion (PBF) process is inherently a thermal process with complex thermal interactions between different printed zones as well as different layers. There exist only a few methods such as finite element analysis (FEA), finite element differences (FDM), graph theory (GT), Goldak’s FEA, and Rosenthal equation, which are able to predict thermal temperature distribution throughout a printed layer (2D) or part (3D). All these approaches suffer from inherent limitations including the applied boundary conditions and computational time. A rapid and reliable method to compute thermal distribution throughout a printed part is pivotal to supporting real-time closed-loop monitoring and control, enabling thermal simulation software with rapid and precise prediction, and advancing current research on thermal-related abnormalities such as residual stress and distortion. The literature shows that the conventional graph theory is the fastest approach that generates relatively precise results in a fraction of the computational time of other techniques; however, the lack of a solution to the non-homogeneous governing thermal equation through GT has hampered this method in terms of thermal load resolution, accuracy in highly rapid process such as PBF, and scope of application. In this paper, we describe the characteristics that make GT a superior approach for real-time computation of thermal field compared to other similar approaches such as FDM. Also, we develop a solution to the non-homogeneous term of the thermal conduction equation by using GT. This solution represents a breakthrough for the development of precise real-time closed-loop monitoring and control systems by providing a precise numerical solution to the thermal conduction equation in a fraction of time compared with previous traditional methods such as FEA and FDM. Ongoing work includes the development of an intelligent monitoring and control system that leverages this solution in order to optimize scan strategy real-time in metal PBF.
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    Correlation Between Process Parameters and Mechanical Properties in Parts Printed by the Fused Deposition Modeling Process
    (Springer, 2019) Attoye, Samuel; Malekipour, Ehsan; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    Fused deposition modeling (FDM) represents one of the most common techniques for rapid prototyping and industrial additive manufacturing (AM). Optimizing the process parameters which significantly impact the mechanical properties is critical to achieving the ultimate final part quality sought by industry today. This work investigates the effect of different process parameters including nozzle temperature, printing speed, and print orientation on Young’s modulus, yield strength, and ultimate strength of the final part for two types of filament, namely, Poly Lactic Acid (PLA) and Acrylonitrile Butadiene Styrene (ABS). Design of Experiments (DOE) is used to determine optimized values of the process parameters for each type of filaments; also, a comparison is made between the mechanical properties of the parts fabricated with the two materials. The results show that Y-axis orientation presents the best mechanical properties in PLA while X-axis orientation is the best orientation to print parts with ABS.
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    Defects, Process Parameters and Signatures for Online Monitoring and Control in Powder-Based Additive Manufacturing
    (Springer, 2018) Malekipour, Ehsan; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    Additive Manufacturing (AM) is a process that is based on manufacturing parts layer by layer in order to avoid any geometric limitation in terms of creating the desired design. In the early stages of AM development, the goal was just creating some prototypes to decrease the time of manufacturing assessment. But with metal-based AM, it is now possible to produce end-use parts. In powder-based AM, a designed part can be produced directly from the STL file (Standard Tessellation Language/ stereolithography) layer by layer by exerting a laser beam on a layer of powder with thickness between 20 μm and 100 μm to create a section of the part. The Achilles’ heel of this process is generation of some defects, which weaken the mechanical properties and in some cases, these defects may even lead to part failure under manufacturing. This prevents metal-based AM technology from spreading widely while limiting the repeatability and precision of the process. Online monitoring (OM) and intelligent control, which has been investigated prevalently in contemporary research, presents a feasible solution to the aformentioned issues, insofar as it monitors the generated defects during the process and eliminates them in real-time. In this regard, this paper reveals the most frequent and traceable defects which significantly affect quality matrices of the produced part in powder-based AM, predominately focusing on the Selective Laser Sintering (SLS) process. These defects are classified into “Geometry and Dimensions,” “Surface Quality (Finishing),” “Microstructure” and the defects leading to “Weak Mechanical Properties.” In addition, we introduce and classify the most important parameters, which can be monitored and controlled to avoid those defects. Furthermore, these parameters may be employed in some error handling strategies to remove the generated defects. We also introduce some signatures that can be monitored for adjusting the parameters into their optimum value instead of monitoring the defects directly.
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    Development of a Cone CVT by SDPD and Topology Optimization
    (SAE, 2019-04) Patil, Nikhil; Malekipour, Ehsan; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    The automotive industries have undergone a massive change in the last few decades. Nowadays, automotive industries and OEM manufacturers implement various innovative ideas to ensure the desired comfort while minimizing the cost, weight, and manufacturing time. Transmission system plays a major role in the aforementioned items. This paper aims to develop a conical roller with belt Continuously Variable Transmission (CVT) System by employing the System Driven Product Development (SDPD) approach and topology optimization of its traditional design. Furthermore, this paper explains the design steps of the CVT and its advantages and limitations compared with the other automatic transmission systems.
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    A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process Using Artificial Neural Network (ANN)
    (2019) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; Mechanical and Energy Engineering, School of Engineering and Technology
    Powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. Research in the PBF process predominantly focuses on the impact of few parameters on the ultimate properties of the printed part. The lack of a systematic approach to optimizing the process parameters for a better performance of given material results in a sub-optimal process. This process needs a comprehensive study of all the influential parameters and their impact on the mechanical and microstructural properties of a fabricated part. Furthermore, there is a need to develop a quantitative system for mapping the material properties and process parameters with the ultimate quality of the fabricated part to achieve improvement in the manufacturing cycle as well as the quality of the final part produced by the PBF process. To address the aforementioned challenges, this research proposes a framework to optimize the process for 316L stainless steel material. This framework characterizes the influence of process parameters on the microstructure and mechanical properties of the fabricated part using a series of experiments. These experiments study the significance of process parameters and their variance as well as study the microstructure and mechanical properties of fabricated parts by conducting tensile, impact, hardness, surface roughness, and densification tests, and ultimately obtain the optimum range of parameters. This would result in a more complete understanding of the correlation between process parameters and part quality. Furthermore, these experiments provide the required data needed to develop an Artificial Neural Network (ANN) model to optimize process parameters (for achieving the desired properties) and estimate fabrication time.
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    Heat Conduction and Geometry Topology Optimization of Support Structure in Laser-based Additive Manufacturing
    (Springer, 2018) Malekipour, Ehsan; Tovar, Andres; El-Mounayri, Hazim; Mechanical Engineering, School of Engineering and Technology
    Laser-based metal additive manufacturing technologies such as Selective Laser Sintering (SLS) and Selective Laser Melting (SLM) allow the fabrication of complex parts by selectively sintering or melting metallic powders layer by layer. Although elaborate features can be produced by these technologies, heat accumulation in overhangs leads to heat stress and warping, affecting the dimensional and geometrical accuracy of the part. This work introduces an approach to mitigate heat stress by minimizing the temperature gradient between the heat-accumulated zone in overhangs and the layers beneath. This is achieved by generating complex support structures that maintain the mechanical stability of the overhang and increase the heat conduction between these areas. The architecture of the complex support structures is obtained by maximizing heat conduction as an objective function to optimize the topology of support structure. This work examines the effect of various geometries on the objective function in order to select a suitable one to consume less material with almost same conduction. Ongoing work is the development of an experimental testbed for verification.
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    Investigation of Layer Based Thermal Behavior in Fused Deposition Modeling Process by Infrared Thermography
    (Elsevier, 2018) Malekipour, Ehsan; Attoye, Samuel; El-Mounayri, Hazim; Mechanical Engineering, School of Engineering and Technology
    There are numerous research efforts that address the monitoring and control of additive manufacturing (AM) processes to improve part quality. Much less research exists on process monitoring and control of Fused Deposition Modeling (FDM). FDM is inherently a thermal process and thus, lends itself to being study by thermography. In this regard, there are various process parameters or process signatures such as built-bed temperature, temperature mapping of parts during deposition of layers, and the nozzle extrusion temperature that may monitor to optimize the quality of fabricated parts. In this work, we applied image based thermography layer by layer with the usage of an infrared camera to investigate the thermal behavior and thermal evolution of the FDM process for the standard samples printed by ABS filament. The combination of the layer based temperature profile plot and the temporal plot has been utilized to understand the temperature distribution and average temperature through the layers under fabrication. This information provides insights for potential modification of the scan strategy and optimization of process parameters in future research, based on the thermal evolution. Accordingly, this can reduce some frequent defects which have roots in thermal characteristics of the deposited layers and also, improve the surface quality and/or mechanical properties of the fabricated parts. In addition, this approach for monitoring the process will allow manufacturers to build, qualify, and certify parts with greater throughput and accelerate the proliferation of products into high-quality applications.
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    Optimization of Chessboard Scanning Strategy Using Genetic Algorithm in Multi-Laser Additive Manufacturing Process
    (ASME, 2021-02) Malekipour, Ehsan; Valladares, Homero; Shin, Yung; El-Mounayri, Hazim; Mechanical and Energy Engineering, School of Engineering and Technology
    Residual stress and manufacturing time are two serious challenges that hinder the widespread industry adoption and implementation of the powder-bed fusion (PBF) process. Commercial Multi-Laser PBF (ML-PBF) systems have been developed by several vendors in recent years, which dramatically increase the production rate by employing more heat sources (up to 4 laser beams). Although numerous research works conducted toward mitigation of the effects of residual stress on printed parts in the Single Laser PBF (SL-PBF) process, no research work on this topic has been reported for the ML-PBF process to date. One of the most efficient real-time approaches to mitigate the influence of residual stress and as such the process lead time effectively is to improve the scanning strategy. This approach can be also implemented effectively in the ML-PBF process. In this work, we extend the previously developed GAMP (Genetic Algorithm Maximum Path) strategy for optimizing the scanning path in ML-PBF. The E-GAMP (the Extended GAMP) strategy manipulates the printing topology of the islands and generates more thermally efficient scanning patterns for the chessboard scanning strategy in ML-PBF. This strategy extends the single thermal heat source to multiple ones (2 as well as 3 lasers). To validate the effectiveness of the proposed strategy, we simulate the thermal distribution throughout a simple rectangular layer by ABAQUS for both the traditional successive scanning strategy and the E-GAMP strategy. The results demonstrate that the E-GAMP strategy considerably decreases the manufacturing time while it reduces the maximum temperature gradient, or in other words, generates a more uniform temperature distribution throughout the exposure layer.
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    Real-time Optimization of Printing Sequence to Mitigate Residual Stress and Thermal Distortion in Metal Powder-bed Fusion Process
    (2023-12) Malekipour, Ehsan; El-Mounayri, Hazim; Zhang, Jing; Qattawi, Ala; Al Hasan, Mohammad
    The Powder Bed Fusion (PBF) process is increasingly employed by industry to fabricate complex parts with stringent standard criteria. However, fabricating parts free of defects using this process is still a major challenge. As reported in the literature, thermally induced abnormalities form the majority of generated defects and are largely attributed to thermal evolution. Various methodologies have been introduced in the literature to eliminate or mitigate such abnormalities. However, most of these methodologies are post-process in nature, lacking adaptability and customization to accommodate different geometries or materials. Consequently, they fall short of adequately addressing these challenges. Monitoring and controlling temperature, along with its distribution throughout each layer during fabrication, is an effective and efficient proxy to control the thermal evolution of the process. This, in turn, provides a real-time solution to effectively overcome such challenges. The objective of this dissertation is to introduce a novel online thermography and closed-loop hybrid-control (NOTCH)©, an ultra-fast and practical control approach, to modify the scan strategy in metal PBF in real-time. This methodology employs different mathematical thermophysical concept-based or thermophysical-based models combined with optimization algorithms designed to optimize the printing sequence of islands/stripes/zones in order to avoid or mitigate residual stress and distortion. This methodology is adaptable to different geometries, dimensions, and materials, and is capable of being used with machines having varying ranges of specifications. NOTCH’s objective is to achieve a uniform temperature distribution throughout an entire layer and through the printed part (between layers) to mitigate residual stress and thermally related distortion. To attain this objective, this study explores modifying or optimizing the printing sequence of islands/stripes in an island or the strip scanning strategy. This dissertation presents three key contributions: First, this work introduces two potential models: the Genetic Algorithm Maximum Path (GAMP) strategy and Generalized Advanced Graph Theory. Preliminary results for a printed/simulated prototype are presented. These models, along with the Tessellation algorithm (developed in my M.Sc. thesis), were employed within NOTCH. Second, I developed two optimization algorithms based on the greedy and evolutionary approaches. Both algorithms are direct-derivative-free methods. The greedy optimization provides a definitive solution at each printing step, selecting the island/stripe that ensures the highest temperature uniformity. Conversely, the evolutionary algorithm seeks to obtain the final optimal solution at the end of the printing process, i.e., the printing sequence with the highest uniformity in the last printing step. This approach is inspired by the concept of Random Search algorithms, offering a non-definitive solution to find an optimal solution. Last, this work presents the NOTCH methodology, enabling real-time modification of printing sequences through the integration of a novel thermography methodology (developed in my M.Sc. thesis), developed models, and optimization algorithms.