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.

But Will it Print?: Assessing Student Use of Design for Additive Manufacturing and Exploring its Effect on Design Performance and Manufacturability

2019 ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Undergraduate Students ◽  
Educational Interventions ◽  
Support Material ◽  
Design Performance ◽  
Process Characteristics ◽  
Build Time ◽  
And Performance ◽  
Am Processes ◽  
Design For Am

Abstract Additive manufacturing (AM) enables engineers to improve the functionality and performance of their designs by adding complexity at little to no additional cost. However, AM processes also exhibit certain unique limitations, such as the presence of support material, which must be accounted for to ensure that designs can be manufactured feasibly and cost-effectively. Given these unique process characteristics, it is important for an AM-trained workforce to be able to incorporate both opportunistic and restrictive design for AM (DfAM) considerations into the design process. While AM/DfAM educational interventions have been discussed in the literature, limited research has investigated the effect of these interventions on students’ use of DfAM. Furthermore, limited research has explored how DfAM use affects the performance of students’ AM designs. This research explores this gap through an experimental study with 123 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in an AM design challenge. The students’ final designs were evaluated for (1) performance with respect the design objectives and constraints, and (2) the use of the various aspects of DfAM. The results showed that the use of certain DfAM considerations, such as minimum feature size and support material mass, successfully predicted the performance of the AM designs. Further, while the variations in DfAM education did not influence the performance of the AM designs, it did have an effect on the students’ use of certain DfAM concepts in their final designs. These results highlight the influence of DfAM education in bringing about an increase in students’ use of DfAM. Moreover, the results demonstrate the potential influence of DfAM in reducing build time and build material of the students’ AM designs, thus improving design performance and manufacturability.

scholarly journals Adaptive Slicing in Additive Manufacturing Process Using a Modified Boundary Octree Data Structure

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Nandkumar Siraskar ◽  
Ratnadeep Paul ◽  
Sam Anand
Keyword(s):  
Data Structure ◽  
Additive Manufacturing ◽  
Layer By Layer ◽  
Adaptive Slicing ◽  
Stl File ◽  
Part Quality ◽  
Part Geometry ◽  
Build Time ◽  
Part Surface ◽  
Am Processes

In additive manufacturing (AM) processes, the layer-by-layer fabrication leads to a staircase error resulting in dimensional inaccuracies in the part surface. Using thinner slices reduces the staircase error and improves part accuracy but also increases the number of layers and the build time for manufacturing the part. Another approach called adaptive slicing uses slices of varying thicknesses based on the part geometry to build the part. A new algorithm to compute adaptive slice thicknesses using octree data structure is presented in this study. This method, termed as modified boundary octree data structure (MBODS) algorithm, is used to convert the stereolithography (STL) file of an object to an octree data structure based on the part's geometry, the machine parameters, and a user defined tolerance value. A subsequent algorithm computes the variable slice thicknesses using the MBODS representation of the part and virtually manufactures the part using these calculated slice thicknesses. Points sampled from the virtually manufactured part are inspected to evaluate the volumetric, profile, and cylindricity part errors. The MBODS based slicing algorithm is validated by comparing it with the uniform slicing approach using various slice thicknesses for different parts. The developed MBODS algorithm is observed to be more effective in improving the part quality while using lesser number of slices.

Complexity Analysis in Additive Manufacturing for the Production of Tissue Engineering Constructs

2014 ◽  
pp. 240-257
Author(s):  
Kouroush Jenab ◽  
Philip D. Weinsier
Keyword(s):  
Tissue Engineering ◽  
Additive Manufacturing ◽  
Human Tissue ◽  
Complexity Analysis ◽  
Three Dimensional ◽  
Solid Object ◽  
Tissue Replacement ◽  
3D Solid ◽  
Process Complexity ◽  
Am Processes

Additive Manufacturing (AM) is a process of making a Three-Dimensional (3D) solid object of virtually any shape from a digital model that is used for both prototyping and distributed manufacturing with applications in many fields, such as dental and medical industries and biotech (human tissue replacement). AM refers to technologies that create objects through a sequential layering process. AM processes have several primary areas of complexity that may not be measured precisely, due to uncertain situations. Therefore, this chapter reports an analytical model for evaluating process complexity that takes into account uncertain situations and additive manufacturing process technologies. The model is able to rank AM processes based on their relative complexities. An illustrative example for several processes is demonstrated in order to present the application of the model.

Complexity Analysis in Additive Manufacturing for the Production of Tissue Engineering Constructs

2020 ◽  
pp. 370-393
Author(s):  
Kouroush Jenab ◽  
Philip D. Weinsier
Keyword(s):  
Tissue Engineering ◽  
Additive Manufacturing ◽  
Human Tissue ◽  
Complexity Analysis ◽  
Three Dimensional ◽  
Solid Object ◽  
Tissue Replacement ◽  
3D Solid ◽  
Process Complexity ◽  
Am Processes

Additive Manufacturing (AM) is a process of making a Three-Dimensional (3D) solid object of virtually any shape from a digital model that is used for both prototyping and distributed manufacturing with applications in many fields, such as dental and medical industries and biotech (human tissue replacement). AM refers to technologies that create objects through a sequential layering process. AM processes have several primary areas of complexity that may not be measured precisely, due to uncertain situations. Therefore, this chapter reports an analytical model for evaluating process complexity that takes into account uncertain situations and additive manufacturing process technologies. The model is able to rank AM processes based on their relative complexities. An illustrative example for several processes is demonstrated in order to present the application of the model.

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.

But Will It Build? Assessing Student Engineering Designers’ Use of Design for Additive Manufacturing Considerations in Design Outcomes

2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Rohan Prabhu ◽  
Scarlett R. Miller ◽  
Timothy W. Simpson ◽  
Nicholas A. Meisel
Keyword(s):  
Additive Manufacturing ◽  
Undergraduate Students ◽  
Contact Area ◽  
Educational Interventions ◽  
Support Material ◽  
Design Task ◽  
Process Characteristics ◽  
Build Time ◽  
And Performance ◽  
Part Complexity

Abstract Additive manufacturing (AM) enables engineers to improve the functionality and performance of their designs by adding complexity at little to no additional cost. However, AM processes also exhibit certain unique limitations, such as the presence of support material. These limitations must be accounted for to ensure that designs can be manufactured feasibly and cost-effectively. Given these unique process characteristics, it is important for an AM-trained workforce to be able to incorporate both opportunistic and restrictive design for AM (DfAM) considerations into the design process. While AM/DfAM educational interventions have been discussed in the literature, few studies have objectively assessed the integration of DfAM in student engineering designers’ design outcomes. Furthermore, limited research has explored how the use of DfAM affects the students’ AM designs’ achievement of design task objectives. This research explores this gap in literature through an experimental study with 301 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in a design challenge. The participants’ final designs were evaluated for (1) build time and build material (2) the use of the various DfAM concepts, and (3) the features used to manifest these DfAM concepts. The results show that the use of certain DfAM considerations, such as part complexity, number of parts, support material mass, and build plate contact area (corresponding to warping tendency), correlated with the build material and build time of the AM designs—minimizing both of which were objectives of the design task. The results also show that introducing participants to opportunistic DfAM leads to the generation of designs with higher part complexity and lower build plate contact area but a greater presence of inaccessible support material.

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 The Impact of Performance Audit: the New Zealand Experience

2021 ◽  
Author(s):  
◽  
Nurul Athirah Abd Manaf
Keyword(s):  
New Zealand ◽  
Public Sector ◽  
Performance Improvement ◽  
Managerial Practices ◽  
The Public ◽  
Performance Audit ◽  
And Performance ◽  
Auditor General ◽  
The Many ◽  
The Impact

<p>Performance audit, compared to the traditional financial and compliance audits, is a relatively new innovation that emerged amidst accountability concerns in the public sector. Economic crises, ministerial scandal and inefficiencies were among the impetus that led the public to demand better performance and greater accountability in the public sector, and performance audit was among the many responses to such demand. In New Zealand, performance audit is carried out by the Controller and Auditor General (the AG) under the mandate granted by the Public Audit Act 2001. Adapting the methodology from grounded theory, this study looks at the impact of performance audit on seven entities audited in 2006 by the AG. This study found that the entities were impacted through the manifestation of implemented audit recommendations and the attainment of performance audit goals. In particular, there is a high acceptance and implementation rate to the audit recommendations made in the seven audits. The implementation of accepted recommendations consequently led to the changes within the entities in terms of managerial practices, as well as internal systems and processes. In some entities, these changes were translated into performance improvement, where the entities experienced changes in the way that they carried out their operations. However, based on interviewees' accounts being the auditees of the audits, most interviewees viewed performance audit as having a greater role for performance accountability compared to performance improvement. Whilst the auditees found the audit recommendations useful, the impact on performance in their view has not been significant. Rather, the auditees viewed performance audit as having a more important role as an assurance tool in terms of their accountability to the public.</p>

scholarly journals Four Dissemination Pathways for a Social Media–Based Breastfeeding Campaign: Evaluation of the Impact on Key Performance Indicators (Preprint)

2019 ◽  
Author(s):  
Kassandra Harding ◽  
Rafael Pérez-Escamilla ◽  
Grace Carroll ◽  
Richmond Aryeetey ◽  
Opeyemi Lasisi
Keyword(s):  
Social Media ◽  
Performance Indicators ◽  
Performance Indicator ◽  
Low Cost ◽  
Social Media Marketing ◽  
Campaign Evaluation ◽  
Indicator Data ◽  
And Performance ◽  
Top 40 ◽  
The Impact

BACKGROUND Social media utilization is on the rise globally, and the potential of social media for health behavior campaigns is widely recognized. However, as the landscape of social media evolves, so do techniques used to optimize campaign dissemination. OBJECTIVE The primary aim of this study was to evaluate the impact of 4 material dissemination paths for a breastfeeding social media marketing campaign in Ghana on exposure and engagement with campaign material. METHODS Campaign materials (n=60) were posted to a Facebook and Twitter campaign page over 12 weeks (ie, baseline). The top 40 performing materials were randomized to 1 of 4 redissemination arms (control simply posted on each platform, key influencers, random influencers, and paid advertisements). Key performance indicator data (ie, exposure and engagement) were extracted from both Facebook and Twitter 2 days after the material was posted. A difference-in-difference model was used to examine the impact of the dissemination paths on performance. RESULTS At baseline, campaign materials received an average (SD) exposure of 1178 (670) on Facebook and 1071 (905) on Twitter (n=60). On Facebook, materials posted with paid advertisements had significantly higher exposure and engagement compared with the control arm (<italic>P</italic>&lt;.001), and performance of materials shared by either type of influencer did not differ significantly from the control arm. No differences in Twitter performance were detected across arms. CONCLUSIONS Paid advertisements are an effective mechanism to increase exposure and engagement of campaign posts on Facebook, which was achieved at a low cost.

scholarly journals Spatial evapotranspiration, rainfall and land use data in water accounting – Part 2: Reliability of water accounting results for policy decisions in the Awash basin

2014 ◽  
Vol 11 (1) ◽  
pp. 1125-1167 ◽  
Author(s):  
P. Karimi ◽  
W. G. M. Bastiaanssen ◽  
A. Sood ◽  
J. Hoogeveen ◽  
L. Peiser ◽  
...  
Keyword(s):  
Stochastic Simulation ◽  
Performance Indicators ◽  
Input Data ◽  
Standard Procedure ◽  
Policy Decisions ◽  
Policy Makers ◽  
Essential Information ◽  
Water Accounting ◽  
And Performance ◽  
The Impact

Abstract. Water Accounting Plus (WA+) is a framework that summarizes complex hydrological processes and water management issues in river basins. The framework is designed to use satellite based measurements of land and water as input data. A concern associated with the use of satellite measurements is their accuracy. This study focuses on the impact of the error in remote sensing measurements on water accounting and information provided to policy makers. The Awash basin in the central rift valley in Ethiopia is used as a case study to explore the reliability of WA+ outputs, in the light of input data errors. The Monte Carlo technique was used for stochastic simulation of WA+ outputs over a period of three years. The results show that the stochastic mean of the majority of WA+ parameters and performance indicators are within 5% deviation from the original values. Stochastic simulation can be used as part of a standard procedure for WA+ water accounting because it provides the error bandwidth for every WA+ output, which is essential information for sound decision making. The majority of WA+ parameters and performance indicators have a Coefficient of Variation (CV) of less than 20% which implies that they are reliable. The results also indicate that the "utilized flow" and "basin closure fraction" (the degree to which available water in a basin is utilized) have a high margin of error and thus a low reliability. As such it is recommended that they are not used to formulate important policy decisions.

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