The document discusses the need for an accurate 3D print preview simulation tool to optimize additive manufacturing processes. Current simulation tools are too slow to model full-scale builds. The author's company, 3DSIM, has developed coupled process-material solvers and computational techniques like eigensolvers and banded vectorization to simulate builds millions to billions of times faster than other tools. Their goal is to enable real-time prediction of distortion, microstructure, properties and support needs before printing new parts.
University Course "Micro and nano systems" for Master Degree in Biomedical Engineering at University of Pisa. Topic: Software for additive manufacturing (part1)
University Course "Micro and nano systems" for Master Degree in Biomedical Engineering at University of Pisa. Topic: Introduction to additive manufacturing
University Course "Micro and nano systems" for Master Degree in Biomedical Engineering at University of Pisa. Topic: Software for additive manufacturing (part2)
What do you know about the eight additive manufacturing processes?Design World
When it’s time to print your part, which additive manufacturing/3D printing (AM / 3DP) process will work the best for you?
In this webinar, you will learn:
- How each AM/3DP process works
- The pros and cons of each of the present additive manufacturing/3D printing processes.
- Surface finish expectations and other dimensional information
- Who offers which process
Presented by Leslie Langnau, Managing Editor, Design World, WTWH Media
Leslie is the managing editor at Design World magazine and also manages the Make Parts Fast website, which is devoted to providing you news, analysis, and educational information on the additive manufacturing industry.
University Course "Micro and nano systems" for Master Degree in Biomedical Engineering at University of Pisa. Topic: Software for additive manufacturing (part1)
University Course "Micro and nano systems" for Master Degree in Biomedical Engineering at University of Pisa. Topic: Introduction to additive manufacturing
University Course "Micro and nano systems" for Master Degree in Biomedical Engineering at University of Pisa. Topic: Software for additive manufacturing (part2)
What do you know about the eight additive manufacturing processes?Design World
When it’s time to print your part, which additive manufacturing/3D printing (AM / 3DP) process will work the best for you?
In this webinar, you will learn:
- How each AM/3DP process works
- The pros and cons of each of the present additive manufacturing/3D printing processes.
- Surface finish expectations and other dimensional information
- Who offers which process
Presented by Leslie Langnau, Managing Editor, Design World, WTWH Media
Leslie is the managing editor at Design World magazine and also manages the Make Parts Fast website, which is devoted to providing you news, analysis, and educational information on the additive manufacturing industry.
Both traditional and modern manufacturing methods have changed the face of the manufacturing industry over the years. But which method is best for the job at hand? Here's an overview of how the most common manufacturing methods compare.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 3 focuses on Post Processing
Study on the Fused Deposition Modelling In Additive ManufacturingIJERD Editor
Additive manufacturing process, also popularly known as 3-D printing, is a process where a product
is created in a succession of layers. It is based on a novel materials incremental manufacturing philosophy.
Unlike conventional manufacturing processes where material is removed from a given work price to derive the
final shape of a product, 3-D printing develops the product from scratch thus obviating the necessity to cut away
materials. This prevents wastage of raw materials. Commonly used raw materials for the process are ABS
plastic, PLA and nylon. Recently the use of gold, bronze and wood has also been implemented. The complexity
factor of this process is 0% as in any object of any shape and size can be manufactured.
FDM Process introduction (A part of Additive Manufacturing Technique OR Commonly Known as 3D Printing). 3D printing is an evolved manufacturing technique; it is comparatively better than conventional substractive manufacturing. There is minimum wastage of material because material is added only at those locations where it is required. To make 3D model you need a 3D printer and feeding material and obviously power source. Any thermoplastic material whose melting temperature lies in the range of 150-240 deg. C can be used in FDM based 3D printing.
ExOne Direct Material Printing - Binder Jetting TechnologyRicardo Toledo
Unique binder-based 3D printing technology was developed at MIT.
ExOne uses Binder Jetting technology to 3D print complex parts in industrial-grade materials. Binder Jetting is an additive manufacturing process in which a liquid binding agent is selectively deposited to join powder particles. Layers of material are then bonded to form an object. The printhead strategically drops binder into the powder. The job box lowers and another layer of powder is then spread and binder is added. Over time, the part develops through the layering of powder and binder.
Binder Jetting is capable of printing a variety of materials including metals, sands and ceramics. Some materials, like sand, require no additional processing. Other materials are typically cured and sintered and sometimes infiltrated with another material, depending on the application. Hot isostatic pressing may be employed to achieve high densities in solid metals.
Introduction to CAE and Element Properties.pptxDrDineshDhande
INTRODUCTION
USE OF CAE IN PRODUCT DEVELOPMENT
CONTENTS:
(1) DISCRETIZATION METHODS : FEM,FDM AND FVM
(2) CAE TOOLS
(3) ELEMET SHAPES
(4) SHAPE FUNCTIONS
Both traditional and modern manufacturing methods have changed the face of the manufacturing industry over the years. But which method is best for the job at hand? Here's an overview of how the most common manufacturing methods compare.
On July 10th Innovate UK and the KTN held a business innovation day to showcase 30 of the Innovate UK projects that are currently active in the area of Additive Manufacturing. The presentations and pitches made on the day are now available to download. Topic 3 focuses on Post Processing
Study on the Fused Deposition Modelling In Additive ManufacturingIJERD Editor
Additive manufacturing process, also popularly known as 3-D printing, is a process where a product
is created in a succession of layers. It is based on a novel materials incremental manufacturing philosophy.
Unlike conventional manufacturing processes where material is removed from a given work price to derive the
final shape of a product, 3-D printing develops the product from scratch thus obviating the necessity to cut away
materials. This prevents wastage of raw materials. Commonly used raw materials for the process are ABS
plastic, PLA and nylon. Recently the use of gold, bronze and wood has also been implemented. The complexity
factor of this process is 0% as in any object of any shape and size can be manufactured.
FDM Process introduction (A part of Additive Manufacturing Technique OR Commonly Known as 3D Printing). 3D printing is an evolved manufacturing technique; it is comparatively better than conventional substractive manufacturing. There is minimum wastage of material because material is added only at those locations where it is required. To make 3D model you need a 3D printer and feeding material and obviously power source. Any thermoplastic material whose melting temperature lies in the range of 150-240 deg. C can be used in FDM based 3D printing.
ExOne Direct Material Printing - Binder Jetting TechnologyRicardo Toledo
Unique binder-based 3D printing technology was developed at MIT.
ExOne uses Binder Jetting technology to 3D print complex parts in industrial-grade materials. Binder Jetting is an additive manufacturing process in which a liquid binding agent is selectively deposited to join powder particles. Layers of material are then bonded to form an object. The printhead strategically drops binder into the powder. The job box lowers and another layer of powder is then spread and binder is added. Over time, the part develops through the layering of powder and binder.
Binder Jetting is capable of printing a variety of materials including metals, sands and ceramics. Some materials, like sand, require no additional processing. Other materials are typically cured and sintered and sometimes infiltrated with another material, depending on the application. Hot isostatic pressing may be employed to achieve high densities in solid metals.
Introduction to CAE and Element Properties.pptxDrDineshDhande
INTRODUCTION
USE OF CAE IN PRODUCT DEVELOPMENT
CONTENTS:
(1) DISCRETIZATION METHODS : FEM,FDM AND FVM
(2) CAE TOOLS
(3) ELEMET SHAPES
(4) SHAPE FUNCTIONS
High Performance & High Throughput Computing - EUDAT Summer School (Giuseppe ...EUDAT
Giuseppe will present the differences between high-performance and high-throughput applications. High-throughput computing (HTC) refers to computations where individual tasks do not need to interact while running. It differs from High-performance (HPC) where frequent and rapid exchanges of intermediate results is required to perform the computations. HPC codes are based on tightly coupled MPI, OpenMP, GPGPU, and hybrid programs and require low latency interconnected nodes. HTC makes use of unreliable components distributing the work out to every node and collecting results at the end of all parallel tasks.
Visit: https://www.eudat.eu/eudat-summer-school
CAE is the use of computer software to simulate performance in order to improve product designs or assist in the resolution of engineering problems for a wide of industries this includes simulation validation and optimization of products processes and manufacturing tools
SolidWorks Simulation - How Can I... and How Do I... with SolidWorks Simulation?Hawk Ridge Systems
The Hawk Ridge Systems Simulation & Analysis team presented "How Can I, and How Do I... with SolidWorks Simulation" at SolidWorks World 2014 in San Diego.
Covered topics included handling hundreds of contacts in SolidWorks Motion, managing process configuration analyses in SolidWorks Plastics, Fatigue Analysis in SolidWorks Simulation Professional, submodeling in SolidWorks Simulation Professional,model preparation in SolidWorks Flow Simulation, and more.
We leave in the era where the atomic building elements of silicon computers, e.g., transistors and wires, are no longer visible using traditional optical microscopes and their sizes are measured in just tens of Angstroms. In addition, power dissipation per unit volume is bounded by the laws of Physics that all resulted among others in stagnating processor clock frequencies. Adding more and more processor cores that perform simpler and simpler tasks in an attempt to efficiently fill the available on-chip area seems to be the current trend taken by the Industry.
3DCS FEA Compliant Modeler - Finite Element Analysis and Tolerance AnalysisBenjamin Reese
Traditional variation analysis methods are considered to be "rigid-body" or "non-compliant" modeling; meaning, that every part within the assembly does not flex or would not be distorted through an assembly process such as welding, clamping or unclamping of an assembly fixture.
While this might be the case with a few machined components, most commodities and materials like sheet metal, plastics, aluminum, etc. can be heavily influenced through the manufacturing processes (both fabrication and assembly), thus changing the dimensional integrity or shape of the part/assembly. Finite Element Analysis (FEA) is used to determine the stresses and displacements in mechanical objects and systems and is the basis for this leading-edge advancement in predictive analysis.
3DCS FEA Compliant Modeler, an add-on module to the 3DCS software solutions, utilizes FEA methods to accurately simulate the variation of compliant parts and assemblies within the 3D Variation Analysis model.
Watch videos and learn more at https://www.3dcs.com/tolerance-analysis-software-and-spc-systems/add-ons/fea-compliant-modeler
3DCS FEA Compliant Modeler - Add Finite Element Analysis FEA to Tolerance Ana...Benjamin Reese
3DCS FEA Compliant Modeler, an add-on module to the 3DCS software solutions, utilizes FEA methods to accurately simulate variation of compliant parts and assemblies within the 3D Variation Analysis model.
Optimize Assembly and Manufacturing Processes
Determine optimal placement and order of operation for processes
When welding, bolting, riveting or assembling parts, the order and the process can have as much of an effect on final results as the parts themselves. Riveting can stretch aircraft aluminum skin, assembling can bend and cause spring back, and bolting can warp materials. Simulate, test and determine the best order of operations and the impact these processes will have on your parts.
Learn more at: http://www.3dcs.com/tolerance-analysis-software-and-spc-systems/add-ons/fea-compliant-modeler
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
2. AM can now enable
…control of the overall geometry of a part, which could
be made up of a truss network, where each truss has an
optimized thickness and could have an individually
controllable microstructure or material.
• But we can’t efficiently:
• Design structures this complex in CAD
• Predict what our machines will do when we print
a new geometry we haven’t printed before
• Predict the differences between printing the
same part in two different locations/orientations
• Predict how different process parameters affect
accuracy, microstructure and part performance
Courtesy David Rosen, Georgia Tech
3. Typical Process Variation
Effects
• 2 mm wall made from
Inconel 625
– XZ section showing
effects of scan pattern
variation on
microstructure
• Identical geometries in
the same build give
different distortions
4. (left) Prior beta interfaces ~100 μm
wide show the hatch spacing
(right) Prior beta interfaces not visible
in the bottom layers: microstructure
changes orientation each layer.
(3DSIM predicted values for angular
distortion is ~12-19º, which are in the
observed range.)
• Horizontal Tensile Specimens in
the top (last to be processed) layers
• Horizontal Tensile Specimens
in the bottom (lowest) layers
4
200 X
Microstructural Variations
due to Orientation in Ti6/4
1000 X
200X Bottom, θleast =12°
200X Intermediate layers
θmax =19°
5. 5
200X Bottom
Horizontal samples
200X Bottom
Vertical samples
• Identical process parameters
for identical parts
in an identical layer,
in the same build,
for the same material, but
in different orientations and locations,
result in
different microstructures and properties
• Less residual stress in Vertical samples
columnar grains
• High residual stress in Horizontal samples
martensitic streaks
Microstructural Variations
due to Orientation in Ti6/4
6. The “Support” Problem in
Metal Laser Sintering
• Supports today are
placed based upon
geometric relationships
and user experience
– Extra supports increases
post-processing costs
– Supports can ruin key
features
– Under-supporting regions
causes blade crashes
7. The Current Situation
• We Need An Accurate 3D “Print Preview”
– Based upon Real Process Parameters & Scan Vectors
– To Give us Accurate Geometry Prediction
• Including Distortion and Where we Need Supports
– Internal Microstructure Predictions
– Properties & Performance Predictions
• But what we have today is…
– A CAD file and a “Preview” of 2D slices of a build
– A lot of experimental data to tell us what “might” or
“probably” will happen under different situations
8. What’s Wrong with
Existing Simulation Tools?
• Manufacturing simulations of the past were
developed with the idea that we can take a long
time to get the right answer because we’ll make a
lot of the same thing over and over…
– Most are based upon 20-30 year-old formulations
• They are not optimized for multi-physics, multi-
scale modeling or compatible with GPUs.
• They don’t have a unified computational
infrastructure that enables you to solve all parts of
the problem in one package.
9. • Process simulations that are faster than an AM machine
builds a part
– Predict residual stress and distortion so we know how to place
supports and how to pre-distort our CAD model
• Material simulations which can predict crystal level
details and the resulting mechanical properties
• Lightning fast solutions on GPU-based platforms
• We simulate only what we need to get a practical
answer as FAST as possible
Our Modeling Vision
10. Our Overall Approach
• Most Modeling Tools Link
Process Structure Properties
• We’ve developed two Separate Solvers:
– Process Solver gives – Process Structure
• Thermal history, distortion, residual stress, crystal structure…
– Material Solver gives – Structure Properties
• Based upon the crystal structure, what are the properties
12. Benefits of our Dynamic
Meshing Strategy
• Demonstrated to be 66x faster than
ANSYS for solving AM problems
• ANSYS assembles matrices and calculates
nodal connectivity (stiffness matrix) every
time-step
• Our “intelligent assembly” of matrices
solves an identical problem with no
recalculation of nodal connectivity
• Fine-scale mesh developed for a
particular energy source and/or machine
with no hanging nodes or improperly
skewed meshes
13. Our Core 3DSIM Code
• Formulated for moving
energy source problems
• Multi-scale mesh fits
any size geometry
– Nano-manufacturing to
meters of manufacturing
• Fits whatever energy
source size you choose
– Applicable to ‘n’ scales
of refinement
14. Top Surface Domain in the x direction
TopSurfaceDomainintheydirection
Thermal contours at arbitary time steps during 1st layer of Laser scanning
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 10
-3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
x 10
-3
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Unstable thermal contours at turns
Stable thermal
contours
Scan
Strategy
Simulation Results: Example
Thermal History
15. Effect of Powder Packing
Density on Melt Pool Geometry
(10%, 20%, 30%, 40%, 50%, 60%)
18. How difficult is the
Problem We Want to Model?
• Finite Element Modeling of a commercial full-scale build:
– 200mmx200mmx200mm powder bed size
– 10 microseconds time steps to capture melting
– 20 micron layer thickness
– 10 micron resolution small-scale mesh (2 elements/layer)
• 108 elements per layer, 1012 elements per build if fine meshed everywhere
– 50 hours of actual laser scan time
• 1010 total time steps
19. Time and Efficiency Comparisons
(assuming a 16 teraflops machine)
• BASED UPON OUR CALCULATIONS, WE PROJECT:
• Fine Gridding (using ANSYS or similar method) = 5.7 10 years
• ANSYS (with multi-scale) = 8.9 10 years (89 billion years)
• 3DSIM (with multi-scale) = 1.3 10 years (1.3 billion years)
– It will be much faster in C++, but not fast enough..
• This is why modeling experts only simulate simplified versions of the
problem
• We decided to keep trying to find faster ways to do the entire
problem…
21. Eigensolver
• Strategy
– Compute 3-4 layers using 3DSIM Multi-Scale FEA
– Use the Eigensolver when more than 3-4 layers away from the melt pool
• Advantages
– Time to get the SAME ANSWER is orders of magnitude less
• Disadvantages
– Mode computations are hard to derive for new problems, it is only
applicable to physics problems for which we’ve derived eigenmodal
solutions
• Our Eigensolver is tested and works well for thermal and decoupled
stress/strain problems, but we are still testing our approach for the Material
(crystal plasticity) Eigensolver
22. Comparing Thermal
Eigensolver Answers to FEA
0 1000 2000 3000 4000 5000
0
0.2
0.4
0.6
0.8
1
Linearthermalfieldsolution
(normalized)
# of nodal points
Modal Reconstruction
Finite Element Solution
Solution match for each node when comparing 3DSIM FEA against the
3DSIM Eigensolver for a point heat source
23. Banded Vectorization
Number Sorting
Eliminate Meaningless Computation
7 additively
manufactured
layers
Top surface Boundary condition Optimal tolerance FLOPS
point force 1000 7.00%
center line parallel to X axis 2511.8864 4.00%
Line along one of the diagonal 2511.8864 5.00%
Area force 63.09 40.00%
24. Periodic and Higher
Order Boundary
Conditions (PHOBC)
• We have derived and are testing an eigenmodal
approach to:
– Identify Symmetry & 1st to 4th Order Periodicity in
Boundary Conditions BEFORE calculating FEA for a
New Layer
• Calculation is Based upon Prior Layer Histories and the
Scanning Parameters that will be used for upcoming layer
• If periodicity occurs AND a prior answer is
known… then … feed forward the correct answer
into appropriate portions of the layer
– Calculate any unknown answers using FEA
25. Time and Efficiency Comparisons
(assuming 16 teraflops machine)
• Fine Gridding (using ANSYS or similar method) = 5.7 10 years
• ANSYS (with multi-scale) = 8.9 10 years (89 billion years)
• 3DSIM (with multi-scale) = 1.3 10 years (1.3 billion years)
• 3DSIM(…+Z direction Eigenmodes after 4 layers) = 208 years
• 3DSIM(…+Banded vectorization) = 22.1 years
• 3DSIM(…+PHOBC) = 22.1 10 years~0.2 hours
Typical Desktop Computer will do 3DSIM (…+PHOBC)=166 days
That’s why we buy $20k-$30k GPU computers…
US Fastest GPU Computer (TITAN)
3DSIM (…+Z Direction Eigenmodes)=54 days
3DSIM (…+Banded vectorization)=6 days
3DSIM (…+PHOBC)=720 µs
26. What are we working on
Currently?
• Converting all our Matlab and Fortran code into
C++ and C# code to run on a CUDA GPU
• Running sensitivity analyses on each module as it
is developed
• Validating each module against
– Analytical solutions
– Other software tools
– Our software prior to turning on each new module
– Experiments
27. Our Products
• Full-blown “Everything 3DSIM Offers” Products:
– Simulating problem sets for others as consultancy
– Cloud-based solutions on a per-use basis
– Licenses for combined hardware/software platforms
• Specialty Software Tools:
– Distortion prediction and compensation tool
– Optimum support structure tool
– Future machine control software
– …and more…
28. • An accurate “3D Print Preview” is becoming a reality
• We have developed a modeling infrastructure with never-
before-seen modeling efficiencies
– Combines “upgraded” FEA with Eigensolvers to solve for every
point in space within a machine for every time step to achieve
highly accurate solutions
• 3DSIM tools will:
– Provide guidance to machine users on how to best optimize their
existing machines and build layouts
– Enable rapid materials insertion, optimization & qualification
– Provide a prediction of part performance before building a part
– Make possible the design and manufacture of better AM machines
Conclusions &
Significance
31. 3DSIM Software has Been Developed
and/or is Being Validated Via the
Following Projects
Involving Both 3DSIM and the University of Louisville
• Development of Distortion Prediction and Compensation Methods for Metal Powder-
Bed AM – America Makes, 2014-2015
• Predicting Residual Stress in Metallic Additive Manufacturing – STFC EU consortium,
2014-2015
• Further Development of 3DSIM Models – DARPA (anticipated) 2014-2015
• Modeling of DMLS Ti6/4 Residual Stress & Supports -- AFRL/MLPC, 2012-2015
Based Research at the University of Louisville
• Modeling of DMLS In625 -- NIST, 2013-2015
• Rapid Qualification of DMLS/EBM Ti6/4 -- America Makes, 2013-2015
• Modeling of DMLS Ti6/4 Arbitrary Powders –AFRL/MLPC, 2013
• Modeling of Friction Stir AM -- NSF, 2012-2015
• Modeling & Closed Loop Control of UC -- ONR, 2011-2014
• Multi-Material UC – ONR, 2007-2011
33. Arbitrary Powders in Metal
Laser Sintering
• Takes simple powder tests as inputs
– Powder density, morphology & chemistry
• Uses empirical relationships to convert powder
tests into important processing variables
– Powder bed absorptivity, thermal conductivity, etc.
• Runs our simulation algorithms near previously
determined “good” operating parameters for a
well-known powder type to find equivalent
“good” parameters for the new powder
36. Approach for Polymers
• Find and derive algorithms for the Material &
Process Solvers
– Mathematical relationships which correlate thermal
history to % crystallinity, spherulite morphology, chain
entanglement, molecular weight, % porosity, etc.
– Correlate microstructural features to mechanical
properties mathematically and via experiments
37. Our Estimation Method
• Total # of time steps=50 hours=
∗
1.8 10
• Total Number of Layers ( )= 10
• Time step/layer
Total # of time steps
1.8 10
• Total number of thermal degrees of freedom in a
layer( )= 4 10
38. Theoretical Computational
complexity (in flops)
• Uses Forward substitution for complexity (This is the
expensive term backward is one order less.)
• # of flops= ∑ =
• Since N>>>1, N~N+1
• # of flops=
• Flop Speed per second=F
• Total time=