Hybrid Processes in Additive Manufacturing

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Michael P. Sealy ◽  
Gurucharan Madireddy ◽  
Robert E. Williams ◽  
Prahalada Rao ◽  
Maziar Toursangsaraki
Keyword(s):  
Additive Manufacturing ◽  
Energy Sources ◽  
Cyclic Process ◽  
Friction Stir ◽  
Hybrid Processes ◽  
Part Quality ◽  
Manufacturing Technologies ◽  
And Function ◽  
Fully Coupled ◽  
Am Processes

Hybrid additive manufacturing (hybrid-AM) has described hybrid processes and machines as well as multimaterial, multistructural, and multifunctional printing. The capabilities afforded by hybrid-AM are rewriting the design rules for materials and adding a new dimension in the design for additive manufacturing (AM) paradigm. This work primarily focuses on defining hybrid-AM in relation to hybrid manufacturing (HM) and classifying hybrid-AM processes. Hybrid-AM machines, materials, structures, and function are also discussed. Hybrid-AM processes are defined as the use of AM with one or more secondary processes or energy sources that are fully coupled and synergistically affect part quality, functionality, and/or process performance. Historically, defining HM processes centered on process improvement rather than improvements to part quality or performance; however, the primary goal for the majority of hybrid-AM processes is to improve part quality and part performance rather than improve processing. Hybrid-AM processes are typically a cyclic process chain and are distinguished from postprocessing operations that do not meet the fully coupled criterion. Secondary processes and energy sources include subtractive and transformative manufacturing technologies, such as machining, remelting, peening, rolling, and friction stir processing (FSP). As interest in hybrid-AM grows, new economic and sustainability tools are needed as well as sensing technologies that better facilitate hybrid processing. Hybrid-AM has ushered in the next evolutionary step in AM and has the potential to profoundly change the way goods are manufactured.

A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Gustavo Tapia ◽  
Alaa Elwany
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Monitoring And Control ◽  
Part Quality ◽  
Industrial Sectors ◽  
Process Monitoring And Control ◽  
Traditional Manufacturing ◽  
Manufacturing Technologies ◽  
And Control ◽  
Am Processes

There is consensus among both the research and industrial communities, and even the general public, that additive manufacturing (AM) processes capable of processing metallic materials are a set of game changing technologies that offer unique capabilities with tremendous application potential that cannot be matched by traditional manufacturing technologies. Unfortunately, with all what AM has to offer, the quality and repeatability of metal parts still hamper significantly their widespread as viable manufacturing processes. This is particularly true in industrial sectors with stringent requirements on part quality such as the aerospace and healthcare sectors. One approach to overcome this challenge that has recently been receiving increasing attention is process monitoring and real-time process control to enhance part quality and repeatability. This has been addressed by numerous research efforts in the past decade and continues to be identified as a high priority research goal. In this review paper, we fill an important gap in the literature represented by the absence of one single source that comprehensively describes what has been achieved and provides insight on what still needs to be achieved in the field of process monitoring and control for metal-based AM processes.

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

scholarly journals Precision and Energy Usage for Additive Manufacturing

2013 ◽  
Author(s):  
Lee Clemon ◽  
Anton Sudradjat ◽  
Maribel Jaquez ◽  
Aditya Krishna ◽  
Marwan Rammah ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Energy Demand ◽  
Fused Deposition Modeling ◽  
Distinct Pattern ◽  
Energy Usage ◽  
Warm Up ◽  
Part Quality ◽  
Fused Deposition ◽  
Manufacturing Technologies ◽  
Control Part

Market pressures on manufacturing enterprises incentivize minimum resource consumption while maintaining part quality. Facilities with advanced manufacturing tools often utilize rapid prototyping for production of complicated or specialty parts. Additive manufacturing offers an alternative to traditional production methods which are often time and resource expensive. This study aims to explore part quality and energy usage for additive manufacturing through a focused study of Fused Deposition Modeling and Photopolymer Jetting technologies. A control part is developed for maintaining test consistency across different machines. The control part design consists of various positive and negative features including width varied slots and walls, ramps, and curved features so that the manufacturing of different surfaces may be investigated. Several different machine models are tested to evaluate precision for a variety of applications. Part quality is quantified by measuring the surface roughness in two directions for the control test part printed on each machine. Qualitatively, part quality is assessed by positive and negative feature resolution. High quality machines resolve features closely to design specifications. Lower quality machines do not resolve some features. In addition to exploring the effects of advertised print precision, layup density is varied on two machines. Advertised print resolution does not well represent the achievable feature sizes found in this study. Energy usage is quantified by measuring electricity demands while printing the control part on each of the five different machines. Power consumption in additive manufacturing is found to follow a distinct pattern comprised of standby, warm up, printing and idle phases. Measurement and analysis suggest a relationship between the precision of these machines and their respective energy demand. Part quality is found to generally improve with increased initial and process resource investment. The energy and quality assessment methods developed in this study are applicable to a greater variety of additive manufacturing technologies and will assist designers as additive manufacturing becomes more production friendly. The presented data also provides designers and production planners insight for improvements in the process decision making.

scholarly journals Investigation of microstructure and hardness of a rib geometry produced by metal forming and wire-arc additive manufacturing

2018 ◽  
Vol 190 ◽  
pp. 02005 ◽  
Author(s):  
Markus Hirtler ◽  
Angelika Jedynak ◽  
Benjamin Sydow ◽  
Alexander Sviridov ◽  
Markus Bambach
Keyword(s):  
Additive Manufacturing ◽  
Metal Forming ◽  
Batch Size ◽  
Unit Costs ◽  
Batch Sizes ◽  
Traditional Manufacturing ◽  
Manufacturing Technologies ◽  
Forming Tools ◽  
Wire Arc Additive Manufacturing ◽  
Am Processes

Within the scope of consumer-oriented production, individuality and cost-effectiveness are two essential aspects, which can barely be met by traditional manufacturing technologies. Conventional metal forming techniques are suitable for large batch sizes. If variants or individualized components have to be formed, the unit costs rise due to the inevitable tooling costs. For such applications, additive manufacturing (AM) processes, which do not require tooling, are more suitable. Due to the low production rates and limited build space of AM machines, the manufacturing costs are highly dependent on part size and batch size. Hence, a combination of both manufacturing technologies i.e. conventional metal forming and additive manufacturing seems expedient for a number of applications. The current study develops a process chain combining forming and additive manufacturing. First, a semi-finished product is formed with forming tools of reduced complexity and then finished by additive manufacturing. This research investigates the addition of features using AlSi12 created by Wire Arc Additive Manufacturing (WAAM) on formed EN-AW 6082 preforms. By forming, the strength of the material was increased, while this effect was partly reduced by the heat input of the WAAM process.

scholarly journals An Overview of Additive Manufacturing Technologies—A Review to Technical Synthesis in Numerical Study of Selective Laser Melting

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3895 ◽  
Author(s):  
Abbas Razavykia ◽  
Eugenio Brusa ◽  
Cristiana Delprete ◽  
Reza Yavari
Keyword(s):  
Numerical Simulation ◽  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Powder Bed Fusion ◽  
Laser Melting ◽  
Powder Bed ◽  
Part Quality ◽  
Melt Pool ◽  
Wide Range ◽  
Am Processes

Additive Manufacturing (AM) processes enable their deployment in broad applications from aerospace to art, design, and architecture. Part quality and performance are the main concerns during AM processes execution that the achievement of adequate characteristics can be guaranteed, considering a wide range of influencing factors, such as process parameters, material, environment, measurement, and operators training. Investigating the effects of not only the influential AM processes variables but also their interactions and coupled impacts are essential to process optimization which requires huge efforts to be made. Therefore, numerical simulation can be an effective tool that facilities the evaluation of the AM processes principles. Selective Laser Melting (SLM) is a widespread Powder Bed Fusion (PBF) AM process that due to its superior advantages, such as capability to print complex and highly customized components, which leads to an increasing attention paid by industries and academia. Temperature distribution and melt pool dynamics have paramount importance to be well simulated and correlated by part quality in terms of surface finish, induced residual stress and microstructure evolution during SLM. Summarizing numerical simulations of SLM in this survey is pointed out as one important research perspective as well as exploring the contribution of adopted approaches and practices. This review survey has been organized to give an overview of AM processes such as extrusion, photopolymerization, material jetting, laminated object manufacturing, and powder bed fusion. And in particular is targeted to discuss the conducted numerical simulation of SLM to illustrate a uniform picture of existing nonproprietary approaches to predict the heat transfer, melt pool behavior, microstructure and residual stresses analysis.

Thermal Modeling in Metal Additive Manufacturing Using Graph Theory

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
M. Reza Yavari ◽  
Kevin D. Cole ◽  
Prahalada Rao
Keyword(s):  
Finite Element ◽  
Graph Theory ◽  
Additive Manufacturing ◽  
Temperature Distribution ◽  
Process Parameters ◽  
Green’S Functions ◽  
Theory Approach ◽  
Finite Element Analyses ◽  
Part Quality ◽  
Am Processes

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

scholarly journals On the Impact of Additive Manufacturing Processes Complexity on Modelling

2021 ◽  
Vol 11 (16) ◽  
pp. 7743
Author(s):  
Panagiotis Stavropoulos ◽  
Panagis Foteinopoulos ◽  
Alexios Papapacharalampopoulos
Keyword(s):  
Additive Manufacturing ◽  
Performance Indicators ◽  
Part Quality ◽  
Build Time ◽  
Modelling Techniques ◽  
And Performance ◽  
The Many ◽  
Process Complexity ◽  
The Impact ◽  
Am Processes

The interest in additive manufacturing (AM) processes is constantly increasing due to the many advantages they offer. To this end, a variety of modelling techniques for the plethora of the AM mechanisms has been proposed. However, the process modelling complexity, a term that can be used in order to define the level of detail of the simulations, has not been clearly addressed so far. In particular, one important aspect that is common in all the AM processes is the movement of the head, which directly affects part quality and build time. The knowledge of the entire progression of the phenomenon is a key aspect for the optimization of the path as well as the speed evolution in time of the head. In this study, a metamodeling framework for AM is presented, aiming to increase the practicality of simulations that investigate the effect of the movement of the head on part quality. The existing AM process groups have been classified based on three parameters/axes: temperature of the process, complexity, and part size, where the complexity has been modelled using a dedicated heuristic metric, based on entropy. To achieve this, a discretized version of the processes implicated variables has been developed, introducing three types of variable: process parameters, key modeling variables and performance indicators. This can lead to an enhanced roadmap for the significance of the variables and the interpretation and use of the various models. The utilized spectrum of AM processes is discussed with respect to the modelling types, namely theoretical/computational and experimental/empirical.

A Graph Theoretic Approach for Near Real-Time Prediction of Part-Level Thermal History in Metal Additive Manufacturing Processes

2019 ◽  
Author(s):  
Reza Yavari ◽  
Kevin D. Cole ◽  
Prahalad Rao
Keyword(s):  
Finite Element Analysis ◽  
Heat Flux ◽  
Finite Element ◽  
Graph Theory ◽  
Additive Manufacturing ◽  
Layer By Layer ◽  
Theory Approach ◽  
Element Analysis ◽  
Part Quality ◽  
Am Processes

Abstract The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatial distribution of heat, called the heat flux or thermal history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the heat flux in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the heat flux in the part. For instance, constrained heat flux because of ill-considered part design leads to defects, such as warping and thermal stress-induced cracking. Existing non-proprietary approaches to predict the heat flux in AM at the part-level predominantly use mesh-based finite element analyses that are computationally tortuous — the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational thermal models to predict the heat flux, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared to finite element analysis techniques, the proposed mesh-free graph theory-based approach facilitates layer-by-layer simulation of the heat flux within a few minutes on a desktop computer. To explore these assertions we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach, with finite element analysis and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume, and (2) simulating the layer-by-layer deposition of three part geometries in a laser powder bed fusion metal AM process with: (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the heat flux predictions from the last two approaches with a commercial solution. From the first study we report that the heat flux trend approximated by the graph theory approach is found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the heat flux trends predicted for the AM parts using graph theory approach agrees with finite element analysis with error less than 15%. More pertinently, the computational time for predicting the heat flux was significantly reduced with graph theory, for instance, in one of the AM case studies the time taken to predict the heat flux in a part was less than 3 minutes using the graph theory approach compared to over 3 hours with finite element analysis. While this paper is restricted to theoretical development and verification of the graph theory approach for heat flux prediction, our forthcoming research will focus on experimental validation through in-process sensor-based heat flux measurements.

Efficient Process Planning Strategies for Additive Manufacturing

2017 ◽  
Author(s):  
Uppili Srinivasan Venkatesan ◽  
S. S. Pande
Keyword(s):  
Additive Manufacturing ◽  
Process Planning ◽  
Fitness Function ◽  
Performance Measure ◽  
Performance Parameters ◽  
Support Structures ◽  
Structure Generation ◽  
Part Quality ◽  
Material Utilization ◽  
Am Processes

This work reports the development of robust and efficient algorithms for optimum process planning of Additive Manufacturing (AM) processes needing support structures during fabrication. In particular, it addresses issues like part hollowing, support structure generation and optimum part orientation. Input to the system is a CAD model in STL format which is voxelized and hollowed using the 2D Hollowing strategy. A novel approach to design external as well as internal support structures for the hollowed model is developed considering the wall thickness and material properties. Optimum orientation of the hollowed part model is computed using Genetic Algorithm (GA). The Fitness Function for optimization is the weighted average of process performance parameters like build time, part quality and material utilization. A new performance measure has been proposed to choose the weightages for performance parameters to obtain overall optimum performance. The paper presents, in detail, the design and development of algorithms with results for typical case studies. The proposed methodology will significantly contribute to improving part quality, productivity and material utilization for AM processes.

scholarly journals New fabrication method using additive manufacturing technologies for the pattern of pressed lithium disilicate onlay restorations

2017 ◽  
Vol 20 (4) ◽  
pp. 149
Author(s):  
Marta Revilla-Leon ◽  
Marina Olea-Vielba ◽  
Ana Esteso-Díaz ◽  
Iñaki Martinez-Klemm ◽  
Jose Manuel Reuss Rodriguez-Vilaboa ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modelling ◽  
3 Dimensional ◽  
Material Extrusion ◽  
Fused Deposition ◽  
Direct Light ◽  
Accuracy And Precision ◽  
Manufacturing Technologies ◽  
Dental Applications ◽  
Am Processes

<p>There are 7 categories for the additive manufacturing (AM) technologies and a wide variety of materials that can be used to build a computer aided designed (CAD) 3-Dimensional (3D) object. The present article reviews the main AM processes for polymers for dental applications: stereolithography (SLA), direct light processing (DLP), material jetting (MJ) and material extrusion (ME). The manufacturing process, accuracy and precision of these methods will be reviewed, as well as, their prosthodontic applications.</p><p> </p><p><strong>Keywords: </strong>3D printing; Additive manufacturing technologies; Direct light processing; Fused deposition modelling; Material extrusion; Material jetting; Multijet printing; Prosthodontics; Stereolitography.</p>

Sign in / Sign up

Export Citation Format

Share Document