A Comprehensive Study of Auxiliary Arrangements for Attaining Omnidirectionality in Additive Manufacturing Machine Tools

2020 ◽  
Vol 143 (5) ◽  
Author(s):  
Ambrish Singh ◽  
Seema Negi ◽  
Sajan Kapil ◽  
K. P. Karunakaran ◽  
Manas Das
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Directional Sensitivity ◽  
Delivery Vector ◽  
Design Variables ◽  
Fixed Set ◽  
Definition Of ◽  
Am Processes ◽  
Sheer Number

Abstract Anisotropy and omnidirectionality are the two most significant impediments to the growth of additive manufacturing (AM). While anisotropy is a property of the part, omnidirectionality is a characteristic of the machine tool. Omnidirectionality, implying invariance in AM processes with the goal of minimizing variations in material and geometric properties of the as-built parts, is often ignored during systems and process design. Disregard to directional sensitivity, which in some cases are inherent to the process (and/ or system), inadvertently changes the process parameter in-situ consequently, producing parts with non-uniform and often erratic properties. AM, attributing to its sheer number of processing variables, is especially susceptible to this subtle, yet significant system property. While some AM platforms, due to their nature of part production, are inherently omnidirectional, others require additional setup to ensure the same. Having an omnidirectional AM platform ensures that the parts are fabricated with process variables that are equally sensitive in all directions. In most AM systems, given a fixed set of process parameters, the spatial orientation of fusion (or joining) source vector, feedstock-delivery vector, and travel direction vector relative to each other governs omnidirectionality. Inconsistency or change in orientation of these three vectors results in non-uniform part properties and variations in geometric dimensions. Therefore, AM systems have to be omnidirectional to improve part performance and promote industrial acceptance. This paper, through a formal definition of omnidirectionality, analyses these three vectors individually along with their interplay with other process parameters and design variables.

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.

Fundamental Requirements for Data Representations in Laser-Based Powder Bed Fusion

2015 ◽  
Author(s):  
Shaw C. Feng ◽  
Paul W. Witherell ◽  
Gaurav Ameta ◽  
Duck Bong Kim
Keyword(s):  
Additive Manufacturing ◽  
Heterogeneous Systems ◽  
Powder Bed Fusion ◽  
Process Data ◽  
Data Interoperability ◽  
Powder Bed ◽  
Related Data ◽  
Data Representations ◽  
Am Processes

Additive Manufacturing (AM) processes intertwine aspects of many different engineering-related disciplines, such as material metrology, design, in-situ and off-line measurements, and controls. Due to the increasing complexity of AM systems and processes, data cannot be shared among heterogeneous systems because of a lack of a common vocabulary and data interoperability methods. This paper aims to address insufficiencies in laser-based Powder Bed Fusion (PBF), a specific AM process, data representations to improve data management and reuse in PBF. Our approach is to formally decompose the processes and align PBF process-specifics with information elements as fundamental requirements for representing process-related data. The paper defines the organization and flow of process information. After modeling selected PBF processes and sub-processes as activities, we discuss requirements for the development of more advanced process data models that provide common terminology and process knowledge for managing data from various stages in AM.

Effect of the process parameters in the additive manufacturing of in situ Al/AlN samples

2019 ◽  
Vol 46 ◽  
pp. 271-278 ◽  
Author(s):  
A. Riquelme ◽  
P. Rodrigo ◽  
M.D. Escalera-Rodriguez ◽  
J. Rams
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters

scholarly journals Performance of wearables and the effect of user behavior in additive manufacturing process

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
JuYoun Kwon ◽  
Namhun Kim
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Manufacturing Process ◽  
High Performance ◽  
Essential Role ◽  
User Behavior ◽  
Process Parameter ◽  
Am Processes

AbstractAdditive manufacturing (AM) which can be a suitable technology to personalize wearables is ideal for adjusting the range of part performance such as mechanical properties if high performance is not required. However, the AM process parameter can impact overall durability and reliability of the part. In this instance, user behavior can play an essential role in performance of wearables through the settings of AM process parameter. This review discusses parameters of AM processes influenced by user behavior with respect to performance required to fabricate AM wearables. Many studies on AM are performed regardless of the process parameters or are limited to certain parameters. Therefore, it is necessary to examine how the main parameters considered in the AM process affect performance of wearables. The overall aims of this review are to achieve a greater understanding of each AM process parameter affecting performance of AM wearables and to provide requisites for the desired performance including the practice of sustainable user behavior in AM fabrication. It is discussed that AM wearables with various performance are fabricated when the user sets the parameters. In particular, we emphasize that it is necessary to develop a qualified procedure and to build a database of each AM machine about part performance to minimize the effect of user behavior.

Ontology-Based Laser and Thermal Metamodels for Metal-Based Additive Manufacturing

2016 ◽  
Author(s):  
Byeong-Min Roh ◽  
Soundar R. T. Kumara ◽  
Timothy W. Simpson ◽  
Panagiotis Michaleris ◽  
Paul Witherell ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Control ◽  
Process Parameters ◽  
First Principles ◽  
3D Model ◽  
Process Models ◽  
Process Ontology ◽  
Building Process ◽  
Graphical View ◽  
Am Processes

Additive manufacturing (AM) is a promising technology that is expected to revolutionize industry by allowing the production of almost any shape directly from a 3D model. In metal-based AM, numerous process parameters are highly interconnected, and their interconnections are not yet understood. Understanding this interconnectivity is the first step in building process control models that help make the process more repeatable and reliable. Metamodels can be used to conceptualize models of complex AM processes and capture diverse parameters to provide a graphical view using common terminology and modeling protocols. In this paper we consider different process models (laser and thermal) for metal-based AM and develop an AM Process Ontology from first-principles. We discuss and demonstrate its implementation in Protégé.

scholarly journals In Situ Monitoring of Additive Manufacturing Using Digital Image Correlation: A Review

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1511
Author(s):  
Filipa G. Cunha ◽  
Telmo G. Santos ◽  
José Xavier
Keyword(s):  
Additive Manufacturing ◽  
Digital Image Correlation ◽  
Digital Image ◽  
Field Measurements ◽  
In Situ Monitoring ◽  
Image Correlation ◽  
Full Field ◽  
The Impact ◽  
Am Processes

This paper is a critical review of in situ full-field measurements provided by digital image correlation (DIC) for inspecting and enhancing additive manufacturing (AM) processes. The principle of DIC is firstly recalled and its applicability during different AM processes systematically addressed. Relevant customisations of DIC in AM processes are highlighted regarding optical system, lighting and speckled pattern procedures. A perspective is given in view of the impact of in situ monitoring regarding AM processes based on target subjects concerning defect characterisation, evaluation of residual stresses, geometric distortions, strain measurements, numerical modelling validation and material characterisation. Finally, a case study on in situ measurements with DIC for wire and arc additive manufacturing (WAAM) is presented emphasizing opportunities, challenges and solutions.

scholarly journals Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1425
Author(s):  
Dayalan R. Gunasegaram ◽  
Ingo Steinbach
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Computational Modelling ◽  
Vital Role ◽  
Formation Mechanisms ◽  
Microstructure Formation ◽  
Metal Additive Manufacturing ◽  
The Status ◽  
Am Processes ◽  
Metal Additive

Microstructures encountered in the various metal additive manufacturing (AM) processes are unique because these form under rapid solidification conditions not frequently experienced elsewhere. Some of these highly nonequilibrium microstructures are subject to self-tempering or even forced to undergo recrystallisation when extra energy is supplied in the form of heat as adjacent layers are deposited. Further complexity arises from the fact that the same microstructure may be attained via more than one route—since many permutations and combinations available in terms of AM process parameters give rise to multiple phase transformation pathways. There are additional difficulties in obtaining insights into the underlying phenomena. For instance, the unstable, rapid and dynamic nature of the powder-based AM processes and the microscopic scale of the melt pool behaviour make it difficult to gather crucial information through in-situ observations of the process. Therefore, it is unsurprising that many of the mechanisms responsible for the final microstructures—including defects—found in AM parts are yet to be fully understood. Fortunately, however, computational modelling provides a means for recreating these processes in the virtual domain for testing theories—thereby discovering and rationalising the potential influences of various process parameters on microstructure formation mechanisms. In what is expected to be fertile ground for research and development for some time to come, modelling and experimental efforts that go hand in glove are likely to provide the fastest route to uncovering the unique and complex physical phenomena that determine metal AM microstructures. In this short Editorial, we summarise the status quo and identify research opportunities for modelling microstructures in AM. The vital role that will be played by machine learning (ML) models is also discussed.

Definition of Parameters by Process of Additive Manufacturing for Tactile Perception

2017 ◽  
Author(s):  
Sandra Regina Marchi ◽  
Maria Lucia Okimoto ◽  
ALESSANDRO MARQUES ◽  
Ramón Sigifredo Cortés Paredes ◽  
Rafael Lima Vieira
Keyword(s):  
Additive Manufacturing ◽  
Tactile Perception ◽  
Definition Of
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