ANSYS HPC Distributed Parallel Processing Decoded: CUBE Workstation

ANSYS HPC Distributed Parallel Processing Decoded: CUBE Workstation

Meanwhile, in the real-world the land of the missing-middle:

This blog post is about distributed parallel processing performance in a missing-middle world of science, tech, engineering & numerical simulation. I will be using two of PADT, Inc.’s very own CUBE workstations along with ANSYS 17.2. to illustrate facts and findings on the ANSYS HPC benchmarks. I will also show you how to decode and extract key bits of data out of your own ANSYS benchmark out files. This information will assist you with locating and describing the performance hows and why’s on your own numerical simulation workstations and HPC clusters. With the use of this information regarding your numerical simulation hardware. You will be able to trust and verify your decisions. Assist you with understanding in addition to explaining the best upgrade path for your own unique situation. In this example I am providing to you in this post. I am illustrating a “worst case” scenario.

You already know you need to increase your parallel processing solves times of your models.“No I am not ready with my numerical simulation results. I am still waiting on Matt to finish running the solve on his model.” “Matt said that it will take four months to solve this model using this workstation. Is this true?!”

  1. How do I know what to upgrade an d you often find yourself asking yourself. Do I need to buy:
    1. One or three ANSYS HPC Packs?
    2. Purchase more compute power? NVidia TESLA K80’s GPU Accelerators? RAM? a Subaru or volvo?
  2. I have no budget. Are you sure? often IT departments set a certain amount of money for component upgrades and parts. Information you learn in these findings may help justify a $250-$5000 upgrade for you?
  3. These two machines as configured will not break the very latest HPC performance speed records. This exercise is a live real world example of what you would see in the HPC missing middle market.
  4.  Benchmarks were formed months after a hardware and software workstation refresh was completed using NO BUDGET, zip, zilch, nada, none.

Backstory regarding the two real-world internal CUBE FEA Workstations.

  1. Originally these two CUBE Workstations were built on a very tight budget. Only the bare minimum was purchased by PADT, Inc.
  2. These two internal CUBE workstations have been in live production, in use daily for  one or two years.
    1. Twenty-four hours a day seven days a week.
  3. These two workstations were both in desperate need of some sort of hardware and operating system refresh.
  4. Windows 10 Professional was upgraded free! as part of Microsoft upgrade initiative in 2016. FREE!

Again, join me in this post and read about the journey of two CUBE workstations being reborn and able to produce impressive ANSYS benchmarks to appease the sense of wining in pure geek satisfaction.

Uh-oh?! $$$

As I mentioned one huge challenge that I set for myself on this mission is that I would not allowed myself to not purchase any new hardware or software. What? that is correct, my challenge was that I would not allow myself to purchase new components for the refresh.

How would I ever succeed in my challenge? Think and then think again.

Harvesting the components off of old workstations that had recently been piling up in the IT Lab over the past year! That was the solution. That was how I could succeed in my NO BUDGET situation that I put myself in. First utilize existing compute components from old tired machines that had showed in the IT boneyard. Talk to your IT department, you never know what they find or remember that they had laying around in their own IT boneyard. Next, I would also use any RMA’d parts that I could find that had trickled in over the past year. Indeed by utilizing these old feeder workstations I managed to not spend any money on new components and I was pleased. The leftovers? please don’t email me for the discarded not worthy components hand-outs. Nope those components are long gone now a nice benefit of our recent in-house next PADT Tech Recycle event.

*** Public Service Announcement *** Please remember to reuse, recycle and erase old computer parts from the landfills.

CUBE Workstation Specifications

PADT, Inc. – CUBE w12ik Numerical Simulation Workstation

(INTENAL PADT CUBE Workstation “CUBE #10”)
1 x CUBE Mid-Tower Chassis (SQ edition)

2 x 6c @3.4GHz/ea (INTEL XEON e5-2643 V3 CPU)

Dual Socket motherboard

16 x 16GB DDR4-2133 MHz ECC REG DIMM

1 x SMC LSI 3108 Hardware RAID Controller – 12 Gb/s

4 x 600GB SAS2 15k RPM – 6 Gb/s – RAID0

3 x 2TB SAS2 7200 RPM Hard Drives – 6 Gb/s (Mid-Term Storage Array – RAID5)

NVIDIA QUADRO K6000 (NVidia Driver version 375.66)

2 x LED Monitors (1920 x 1080)

Windows 10 Professional 64-bit

ANSYS 17.2


PADT, Inc. CUBE w16i-k Numerical Simulation Workstation

(INTENAL PADT CUBE Workstation “CUBE #14″)
1 x CUBE Mid-Tower Chassis

2 x 8c @3.2GHz/ea (INTEL XEON e5-2667 V4 CPU)

Dual Socket motherboard

8 x 32GB DDR4-2400 MHz ECC REG DIMM

1 x SMC LSI 3108 Hardware RAID Controller – 12 Gb/s

4 x 600GB SAS3 15k RPM 2.5” 12 Gb/s – RAID0

2 x 6TB SAS3 7.2k RPM 3.5” 12 Gb/s – RAID1

NVIDIA QUADRO K6000 (NVidia Driver version 375.66)

2 x LED Monitors (1920 x 1080)

Windows 10 Professional 64-bit

ANSYS 17.2


The ANSYS sp-5 Ball Grid Array Benchmark

ANSYS Benchmark Test Case Information

  • BGA (V17sp-5)
    • Analysis Type Static Nonlinear Structural
    • Number of Degrees of Freedom 6,000,000
    • Equation Solver Sparse
    • Matrix Symmetric
  • ANSYS 17.2
  • ANSYS HPC Licensing Packs required for this benchmark –> (2) HPC Packs
  • Please contact your local ANSYS Software Sales Representative for more information on purchasing ANSYS HPC Packs. You too may be able to speed up your solve times by unlocking additional compute power!
  • What is a CUBE? For more information regarding our Numerical Simulation workstations and clusters please contact our CUBE Hardware Sales Representative at SALES@PADTINC.COM Designed, tested and configured within your budget. We are happy to help and to listen to your specific needs.

Comparing the data from the 12 core CUBE vs. a 16 core CUBE with and without GPU Acceleration enabled.

ANSYS 17.2 Benchmark  SP-5 Ball Grid Array
CUBE w12i-k 2643 v3 CUBE w12i-k 2643 v3 w/GPU Acceleration Total Speedup w/GPU CUBE w16i-k 2667 V4 CUBE w16i-k 2667 V4 w/GPU Acceleration Total Speedup w/GPU
2 878.9 395.9 2.22 X 888.4 411.2 2.16 X
4 485.0 253.3 1.91 X 499.4 247.8 2.02 X
6 386.3 228.2 1.69 X 386.7 221.5 1.75 X
8 340.4 199.0 1.71 X 334.0 196.6 1.70 X
10 269.1 184.6 1.46 X 266.0 180.1 1.48 X
11 235.7 212.0 1.11 X
12 230.9 171.3 1.35 X 226.1 166.8 1.36 X
14 213.2 173.0 1.23 X
15 200.6 152.8 1.31 X
16 189.3 166.6 1.14 X
11/15/2016 & 1/5/2017
CUBE w12i-k v17sp-5 Benchmark Graph 2017

CUBE w12i-k v17sp-5 Benchmark Graph 2017

CUBE w16i-k v17sp-5 Benchmark Graph 2017

CUBE w16i-k v17sp-5 Benchmark Graph 2017

Initial impressions

  1. I was very pleased with the results of this experiment. Using the Am I bound bound or I/O bound overall parallel performance indicators the data showed healthy workstations that were both I/O bound. I assumed the I/O bound issue would happen. During several of the benchmarks the data reveals almost complete system bandwidth saturation. Upwards of ~82 GB/s of bandwidth being created during the in-core distributed solve!
  2. I was pleasantly surprised to see a 1.7X or greater  solve speedup using one ANSYS HPC licensing pack and GPU Acceleration!

The when and where of numerical simulation performance bottleneck’s for numerical simulation. Very similar to the clock’s ticking on the wall over the years. Over the years I have focused on the question of, “is your numerical simulation compute hardware compute bound or I/O bound”. This quick and fast benchmark result will show general parallel performance of the workstation and help you find the performance sweet spot for your own numerical simulation hardware.

As a reminder, to determine the answer to that question you need to record the results of your CPU Time For Main Thread, Time Spent Computing Solution and  Total Elapsed Time. If the results time for my CPU Main is about the same as my Total Elapsed Time result. The compute hardware is in a Compute Bound situation. If the Total Elapsed Time result is larger than the CPU Time For Main Thread than the compute hardware is I/O bound . I did the same analysis with these two CUBE workstations. I am bit more picky in tuning my compute hardware and use a percentage around 95 percent. The percentage column below determines if the workstation is Compute Bound or O/O bound. Generally what I have found in the industry a percentage of greater than 90% indicates the workstation is wither Compute Bound, I/O bound or in worst case scenario’s both.

*** Conclusions and upgrade path suggestions for each CUBE workstation will be given below.

**** Result sets data garnered from the results.out files on these two CUBE workstations using ANSYS Mechanical distributed parallel solves.

Data mine that ANSYS results.out file!

The data is all there, at your fingertips waiting for you to trust and verify.

Compute Bound or I/O bound

Results 1 – Compute Cores Only


“CUBE #10”

Cores CPU Time For Main Thread Time Spent Computing Solution Total Elapsed Time % Compute Bound IO Bound
2 2 914.2 878.9 917.0 99.69 YES NO
4 4 517.2 485.0 523.0 98.89 YES NO
6 6 418.8 386.3 422.0 99.24 YES NO
8 8 374.7 340.4 379.0 98.87 YES NO
10 10 302.5 269.1 307.0 98.53 YES NO
11 11 266.6 235.7 273.0 97.66 YES NO
12 12 259.9 230.9 268.0 96.98 YES NO

“CUBE #14”

Cores CPU Time For Main Thread Time Spent Computing Solution Total Elapsed Time % Compute Bound IO Bound
2 2 925.8 888.4 927.0 99.87 YES NO
4 4 532.1 499.4 535.0 99.46 YES NO
6 6 420.3 386.7 425.0 98.89 YES NO
8 8 366.4 334.0 370.0 99.03 YES NO
10 10 299.7 266.0 303.0 98.91 YES NO
12 12 258.9 226.1 265.0 97.70 YES NO
14 14 244.3 213.2 253.0 96.56 YES NO
15 15 230.3 200.6 239.0 96.36 YES NO
16 16 219.6 189.3 231.0 95.06 YES NO

Results 2 – GPU Acceleration + Cores


“CUBE #10”

Cores  + GPU CPU Time For Main Thread Time Spent Computing Solution Total Elapsed Time % Compute Bound IO Bound
2 2 416.3 395.9 435.0 95.70 YES YES
4 4 271.8 253.3 291.0 93.40 YES YES
6 6 251.2 228.2 267.0 94.08 YES YES
8 8 219.9 199.0 239.0 92.01 YES YES
10 10 203.2 184.6 225.0 90.31 YES YES
11 11 227.6 212.0 252.0 90.32 YES YES
12 12 186.0 171.3 213.0 87.32 NO YES
CUBE 14 Cores + GPU CPU Time For Main Thread Time Spent Computing Solution Total Elapsed Time % Compute Bound IO Bound
2 2 427.2 411.2 453.0 94.30 YES YES
4 4 267.9 247.8 286.0 93.67 YES YES
6 6 245.4 221.5 259.0 94.75 YES YES
8 8 219.6 196.6 237.0 92.66 YES YES
10 10 201.8 180.1 222.0 90.90 YES YES
12 12 191.2 166.8 207.0 92.37 YES YES
14 14 195.2 173.0 217.0 89.95 NO YES
15 15 172.6 152.8 196.0 88.06 NO YES
16 16 177.1 166.6 213.0 83.15 NO YES

Identifying Memory, I/O, Parallel Solver Balance and Performance

Results 3 – Compute Cores Only


“CUBE #10”

Ratio of nonzeroes in factor (min/max) Ratio of flops for factor (min/max) Time (cpu & wall) for numeric factor Time (cpu & wall) for numeric solve Effective I/O rate (MB/sec) for solve Effective I/O rate (GB/sec) for solve No GPU Maximum RAM used in GB
0.9376 0.8399 662.822706 5.609852 19123.88932 19.1 78
0.8188 0.8138 355.367914 3.082555 35301.9759 35.3 85
0.6087 0.6913 283.870728 2.729568 39165.1946 39.2 84
0.3289 0.4771 254.336758 2.486551 43209.70175 43.2 91
0.5256 0.644 191.218882 1.781095 60818.51624 60.8 94
0.5078 0.6805 162.258872 1.751974 61369.6918 61.4 95
0.3966 0.5287 157.315184 1.633994 65684.23821 65.7 96

“CUBE #14”

Ratio of nonzeroes in factor (min/max) Ratio of flops for factor (min/max) Time (cpu & wall) for numeric factor Time (cpu & wall) for numeric solve Effective I/O rate (MB/sec) for solve Effective I/O rate (GB/sec) for solve No GPU Maximum RAM used in GB
0.9376 0.8399 673.225225 6.241678 17188.03613 17.2 78
0.8188 0.8138 368.869242 3.569551 30485.70397 30.5 85
0.6087 0.6913 286.269409 2.828212 37799.17161 37.8 84
0.3289 0.4771 251.115087 2.701804 39767.17792 39.8 91
0.5256 0.644 191.964388 1.848399 58604.0123 58.6 94
0.3966 0.5287 155.623476 1.70239 63045.28808 63.0 96
0.5772 0.6414 147.392121 1.635223 66328.7728 66.3 101
0.6438 0.5701 139.355605 1.484888 71722.92484 71.7 101
0.5098 0.6655 130.042438 1.357847 78511.36377 78.5 103

Results 4 – GPU Acceleration + Cores


“CUBE #10”

Ratio of nonzeroes in factor (min/max) Ratio of flops for factor (min/max) Time (cpu & wall) for numeric factor Time (cpu & wall) for numeric solve Effective I/O rate (MB/sec) for solve Effective I/O rate (GB/sec) for solve % GPU Accelerated The Solve Maximum RAM used in GB
0.9381 0.8405 178.686155 5.516205 19448.54863 19.4 95.78 78
0.8165 0.8108 124.087864 3.031092 35901.34876 35.9 95.91 85
0.6116 0.6893 122.433584 2.536878 42140.01391 42.1 95.74 84
0.3365 0.475 112.33829 2.351058 45699.89654 45.7 95.81 91
0.5397 0.6359 103.586986 1.801659 60124.33358 60.1 95.95 94
0.5123 0.6672 137.319938 1.635229 65751.09125 65.8 85.17 95
0.4132 0.5345 97.252285 1.562337 68696.85627 68.7 95.75 97

“CUBE #14”

Ratio of nonzeroes in factor (min/max) Ratio of flops for factor (min/max) Time (cpu & wall) for numeric factor Time (cpu & wall) for numeric solve Effective I/O rate (MB/sec) for solve Effective I/O rate (GB/sec) for solve % GPU Accelerated The Solve Maximum RAM used in GB
0.9381 0.8405 200.007118 6.054831 17718.44411 17.7 94.96 78
0.8165 0.8108 122.200896 3.357233 32413.68282 32.4 95.20 85
0.6116 0.6893 122.742966 2.624494 40733.2138 40.7 94.91 84
0.3365 0.475 114.618006 2.544626 42223.539 42.2 94.97 91
0.5397 0.6359 105.4884 1.821352 59474.26914 59.5 95.18 94
0.4132 0.5345 96.750618 1.988799 53966.06502 54.0 94.96 97
0.5825 0.6382 106.573973 1.989103 54528.26599 54.5 88.96 101
0.6604 0.566 91.345275 1.374242 77497.60151 77.5 92.21 101
0.5248 0.6534 107.672641 1.301668 81899.85539 81.9 85.07 103

The ANSYS results.out file – The decoding continues

CUBE w12i-k (“CUBE #10”)

  1. Elapsed Time Spent Computing The Solution
    1. This value determines how efficient or balanced the hardware solution for running in distributed parallel solving.
      1. Fastest Solve Time For CUBE 10
    2. 12 out of 12 Cores w/GPU @ 171.3 seconds Time Spent Computing The Solution
  2. Elapsed Time
    1. This value is the actual time to complete the entire solution process. The clock on the wall time.
    2. Fastest Time For CUBE10
      1. 12 out of 12 w/GPU @ 213.0 seconds
  3. CPU Time For Main Thread
    1. This value indicates the RAW number crunching time of the CPU.
    2. Fastest Time For CUBE10
      1. 12 out of 12 w/GPU @186.0 seconds
  4. GPU Acceleration
    1. The NVIDIA QUADRO K6000 accelerated ~96% of the matrix factorization flops
    2. Actual percentage of GPU accelerated flops = 95.7456
  5. Cores and storage solver performance 12 out of 12 cores and using 1 Nvidia Quadro K6000
    1. ratio of nonzeroes in factor (min/max) = 0.4132
    2. ratio of flops for factor (min/max) = 0.5345
      1. These two values above indicate to me that the system is well taxed for compute power/hardware viewpoint.
    3. Effective I/O rate (MB/sec) for solve = 68696.856274 (or 69 GB/sec)
      1. No issues here indicates that the workstation has ample bandwidth available for the solving.

CUBE w16i-k (“CUBE #14”)

  1. Elapsed Time Spent Computing The Solution
    1. This value determines how efficient or balanced the hardware solution for running in distributed parallel solving.
    2. Fastest Time For CUBE w16i-k “CUBE #14”
      1. 15 out of 16 Cores w/GPU @ 152.8 seconds
  2. Elapsed Time
    1. This value is the actual time to complete the entire solution process. The clock on the wall time.
    2. CUBE w16i-k “CUBE #14”
      1. 15 out of 16 Cores w/GPU @ 196.0 seconds
  3. CPU Time For Main Thread
    1. This value indicates the RAW number crunching time of the CPU.
    2. CUBE w16i-k “CUBE #14”
      1. 15 out of 16 Cores w/GPU @ 172.6 seconds
  4. GPU Acceleration Percentage
    1. The NVIDIA QUADRO K6000 accelerated ~92% of the matrix factorization flops
    2. Actual percentage of GPU accelerated flops = 92.2065
  5. Cores and storage 12 out of 12 cores and one Nvidia Quadro K6000
    1. ratio of nonzeroes in factor (min/max) = 0.6604
    2. ratio of flops for factor (min/max) = 0.566
      1. These two values above indicate to me that the system is well taxed for compute power/hardware.
    3. Please note that when reviewing these two data points. A balanced solver performance is when both of these values are as close to 1.0000 as possible.
      1. At this point the compute hardware is no longer as efficient and these values will continue to move farther away from 1.0000.
    4. Effective I/O rate (MB/sec) for solve = 77497.6 MB/sec (or ~78 GB/sec)
      1. No issues here indicates that the workstation has ample bandwidth with fast I/O performance for in-core SPARSE Solver solving.
    1. Maximum amount of RAM used by the ANSYS distributed solve
      1. 103GB’s of RAM needed for in-core solve

Conclusions Summary And Upgrade Path Suggestions

It is important for you to locate your bottleneck on your numerical simulation hardware. By utilizing data provided in the ANSYS results.out files, you will be able to logically determine your worst parallel performance inhibitor and plan accordingly on how to resolve what is slowing the parallel performance of your distributed numerical simulation solve.

I/O Bound and/or Compute Bound Summary

  • I/O Bound
    • Both CUBE w12i-k “CUBE #10” and w16i-k “CUBE #14” are I/O Bound.
      • Almost immediately when GPU Acceleration is enabled.
      • When no GPU Acceleration is not enabled, I/O bound is no longer an issue compute solving performance. However solve times are impacted due to available and unused compute power.
  • Compute Bound
    • Both CUBE w12i-k “CUBE #10” and w16i-k “CUBE #14” would benefit from additional Compute Power.
    • CUBE w12i-k “CUBE #10” would get the most bang for the buck by adding in the additional compute power.

Upgrade Path Recommendations

CUBE w12i-k “CUBE #10”

  1. I/O:
    1. Hard Drives
    2. Remove & replace the previous generation hard drives
      1. 3.5″ SAS2.0 6Gb/s 15k RPM Hard Drives
    3. Hard Drives could be upgraded to Enterprise Class SSD or PCIe NVMe
      1. COST =  HIGH
    1. Hard Drives could be upgraded to SAS 3.0 12 Gb/s Drives
      1. COST =  MEDIUM
  2.  RAM:
    1. Remove and replace the previous generation RAM
    2. Currently all available RAM slots of RAM are populated.
      1. Optimum slots per these two CPU’s are four slots of RAM per CPU. Currently eight slots of RAM per CPU are installed.
    3. RAM speeds 2133MHz ECC REG DIMM’
      1. Upgrade RAM to DDR4-2400MHz LRDIMM RAM
      2. COST =  HIGH
  3. GPU Acceleration
    1. Install a dedicated GPU Accelerator card such as an NVidia Tesla K40 or K80
    2. COST =  HIGH
  4.  CPU:
    1. Remove and replace the current previous generation CPU’s:
    2. Currently installed dual  x INTEL XEON e5-2643 V3
    3. Upgrade the CPU’s to the V4 (Broadwell) CPU’s
      1. COST =  HIGH

CUBE w16i-k “CUBE #14”

  1. I/O: Hard Drives SAS3.0 15k RPM Hard Drives 12Gbps 2.5”
    1.  Replace the current 2.5” SAS3 12Gb/s 15k RPM Drives with Enterprise Class SSD’s or PCIe NVMe disk
      1. COST =  HIGH
    2. Replace the 2.5″ SAS3 12 Gb/s hard drives with 3.5″ hard drives.
      1. COST =  HIGH
    3. INTEL 1.6TB P3700 HHHL AIC NVMe
      1. Click Here:
  2. Currently a total of four Hard Drives are installed
    1. Increase existing hard drive count from four hard drives to a total ofsix or eight.
    2. Change RAID configuration to RAID 50
      1. COST =  HIGH
  3. RAM:
    1. Using DDR4-2400Mhz ECC REG DIMM’s
      1. Upgrade RAM to DDR4-2400MHz LRDIMM RAM
      2. COST =  HIGH

Considering RAM: When determining how much System RAM you need to perform a six million degree of freedom ANSYS numerical simulation. Add the additional amounts to your Maximum Amount of RAM used number indicated in your ANSYS results.out file.

  • ANSYS reserves  ~5% of your RAM
  • Office products can use an additional l ~10-15% to the above number
  • Operating System please add an additional ~5-10% for the Operating System
  • Other programs? For example open up your windows task manager and take a look at how much RAM your anti-virus program is consuming. Add in the amount of RAM consumed by these other RAM vampires.

Terms & Definition Goodies:

  • Compute Bound
    • A condition that occurs when your CPU processing power sites idle while the CPU waits for the next set of instructions to calculate. This occurs most often when hardware bandwidth is unable to feed the CPU more data to calculate.
  • CPU Time For Main Thread
    • CPU time (or process time) is the amount of time for which a central processing unit (CPU) was used for processing instructions of a computer program or operating system, as opposed to, for example, waiting for input/output (I/O) operations or entering low-power (idle) mode.
  • Effective I/O rate (MB/sec) for solve
    • The amount of bandwidth used during the parallel distributed solve moving data from storage to CPU input and output totals.
    • For example the in-core 16 core + GPU solve using the CUBE w16i-k reached an effective I//O rate of 82 GB/s.
    • Theoretical system level bandwidth possible is ~96 GB/s
  • IO Bound
    • The ability for the input-output of the system hardware for reading, writing and flow of data pulsing through the system has become inefficient and/or detrimental to running an efficient parallel analysis.
  • Maximum total memory used
    • The maximum amount of memory used by analysis during your analysis.
  • Percentage (%) GPU Accelerated The Solve
    • The percentage of acceleration added to your distributed solve provided by the Graphics Processing Unit (GPU). The overall impact of the GPU will be diminished due to slow and saturated system bandwidth of your compute hardware.
  • Ratio of nonzeroes in factor (min/max)
    • A performance indicator of efficient and balanced the solver is performing on your compute hardware. In this example the solver performance is most efficient when this value is as close to the value of 1.0.
  • Ratio of flops for factor (min/max)
    • A performance indicator of efficient and balanced the solver is performing on your compute hardware. In this example the solver performance is most efficient when this value is as close to the value of 1.0.
  • Time (cpu & wall) for numeric factor
    • A performance indicator used to determine how the compute hardware bandwidth is affecting your solve times. When time (cpu & wall) for numeric factor & time (cpu & wall) for numeric solve values are somewhat equal it means that your compute hardware I/O bandwidth is having a negative impact on the distributed solver functions.
  • Time (cpu & wall) for numeric solve
    • A performance indicator used to determine how the compute hardware bandwidth is affecting your solve times. When time (cpu & wall) for numeric solve & time (cpu & wall) for numeric facto values are somewhat equal it means that your compute hardware I/O bandwidth is having a negative impact on the distributed solver functions.
  • Total Speedup w/GPU
    • Total performance gain for compute systems task using a Graphics Processing Unit (GPU).
  • Time Spent Computing Solution
    • The actual clock on the wall time that it took to compute the analysis.
  • Total Elapsed Time
    • The actual clock on the wall time that it took to complete the analysis.


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Phoenix Business Journal: ​Um, the coffee machine needs more water and 5 rules to improve your user interface design game

Usually getting coffee is just getting coffee, but a recent trip turned into some deep thoughts on user interface design.  “​Um, the coffee machine needs more water and 5 rules to improve your user interface design game” explains my encounter with the office caffeine dispenser as well as five key rules that everyone should follow when developing a user interface for a product.

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Press Release: Concept Laser, Honeywell, and PADT Build Largest Additive Manufacturing Center in Southwest at Arizona State University

PADT-Press-Release-IconOn January 18th, ASU will officially Launch their Manufacturing Research and Innovation Hub, the Largest Additive Manufacturing  research and teaching center in the Southwestern US.  PADT is proud to have partnered with ASU as well as with Concept Laser and Honeywell to get this important piece of the local manufacturing ecosystem started and to keep it growing.

Located on the Polytechnic School at ASU in Mesa, Arizona, this facility is amazing.  And you can see it for yourself, the public is invited to the launch on January 18th, 2017 at 9:00 am.  ASU Polytechnic Dean Kyle Squires and the Director Ann McKenna will be speaking as will our very own Rey Chu, John Murray from Concept Laser, and Don Godfrey from Honeywell.  Tours will follow. Learn more and register for this free event that will bring together the local 3D Printing community here.

You can also learn more by reading the official press release from Concept Laser that outlines what the center does and the partnerships that make it possible:

Press Release:

Concept Laser, Honeywell, and PADT Build Largest Additive Manufacturing Center in Southwest at Arizona State University

GRAPEVINE, Texas, January 11, 2017 – The Polytechnic School at Arizona State University (ASU) offers the only manufacturing engineering undergraduate degree in Arizona; it is also one of only 22 ABET accredited manufacturing engineering programs in the United States. By forming a partnership with Concept Laser, Honeywell Aerospace, and PADT, Inc. the largest additive manufacturing research facility in the Southwest is now on the Polytechnic campus. The 15,000 square foot center holds over $2 million of plastic, polymer, and 3D metal printing equipment.

The lab has a Concept Laser M2 cusing and Mlab cusing machine which are dedicated to 3D metal printing, also known as metal additive manufacturing. Unlike conventional metal fabrication techniques, additive manufacturing produces fully-dense metal parts by melting layer upon layer of ultra-fine metal powder. The Polytechnic School is using the machines for a wide range of research and development activities including materials development and prototyping complex mechanical and energy systems.

Supporting quotes:

Don Godfrey, Engineering Fellow at Honeywell: “Honeywell is thrilled to be participating in the opening of the new additive manufacturing laboratory at the Arizona State University Polytechnic campus.  For many years, we have worked with ASU seniors on their capstone projects with three of these projects this school year additive manufacturing focused. In addition to our own additive manufacturing operations, we have provided mentorship to students in the program and assisted in the procurement of one machine for the schools’ new lab.  We look forward to growing our relationships with the university in developing brilliant minds to tackle and overcome industry challenges associated with aviation and additive manufacturing.”

John Murray, President and CEO of US-based subsidiary Concept Laser Inc: “Changing the future of metal additive manufacturing begins with educated teachers and curious students. The educational leadership that the ASU Polytechnic School provides to the Southwest region and the industry will certainly be impactful. Concept Laser is proud to be a partner in this initiative.”

Rey Chu, Principal, Manufacturing Technologies at PADT, Inc: This partnership is the next and obvious step in the progression of additive manufacturing in the Southwest.  With Concept Laser’s outstanding technology, Honeywell’s leadership in applying additive manufacturing to practical Aerospace needs, PADT’s extensive network of customers and industry experience, and ASU’s proven ability to educate and work with industry, the effort will establish a strong foundation for the entire regional ecosystem.

Ann McKenna, Director of ASU’s Polytechnic School: “Partnering with these industry leaders provides us the capability to do additional research and enhance our education programs. With so few of these types of centers, this makes ASU more attractive among academic partners, federal agencies and corporations to advance additive manufacturing.

The ASU Polytechnic School will be hosting an open house to celebrate the launch of their Manufacturing Research and Innovation Hub on January 18, 2017 at 9am. There will be guided tours showcasing student projects. Honeywell, Concept Laser, and PADT will be in attendance. Please register your attendance at

About Concept Laser  

Concept Laser GmbH is one of the world’s leading providers of machine and plant technology for the 3D printing of metal components. Founded by Frank Herzog in 2000, the patented LaserCUSING® process – powder-bed-based laser melting of metals – opens up new freedom to configuring components and also permits the tool-free, economic fabrication of highly complex parts in fairly small batch sizes.

Concept Laser serves various industries, ranging from medical, dental, aerospace, toolmaking and mold construction, automotive and jewelry. Concept Laser machines are compatible with a diverse set of powder materials, such as stainless steel and hot-work steels, aluminum and titanium alloys, as well as precious metals for jewelry and dental applications.

Concept Laser Inc. is headquartered in Grapevine, Texas and is a US-based wholly owned subsidiary of Concept Laser GmbH. For more information, visit our website at

LaserCUSING® is a registered trademark of Concept Laser.

About Phoenix Analysis and Design Technologies

Phoenix Analysis and Design Technologies, Inc. (PADT) is an engineering product and services company that focuses on helping customers who develop physical products by providing Numerical Simulation, Product Development, and 3D Printing solutions. PADT’s worldwide reputation for technical excellence and experienced staff is based on its proven record of building long term win-win partnerships with vendors and customers. Since its establishment in 1994, companies have relied on PADT because “We Make Innovation Work.” With over 80 employees, PADT services customers from its headquarters at the Arizona State University Research Park in Tempe, Arizona, and from offices in Torrance, California, Littleton, Colorado, Albuquerque, New Mexico, and Murray, Utah, as well as through staff members located around the country. More information on PADT can be found at

 About Arizona State University

The Ira A. Fulton Schools of Engineering at Arizona State University include nearly 19,000 students and more than 300 faculty members who conduct nearly $100 million in research, spanning a broad range of engineering, construction and technology fields. Across the six schools contained within the Fulton Schools, 24 undergraduate and 32 graduate programs are offered on ASU’s Tempe and Polytechnic campuses and online. The schools’ educational programs emphasize problem solving, entrepreneurship, multidisciplinary interactions, social context and connections. Arizona State University includes more than 80,000 students and 1,600 tenured or tenure-track faculty on multiple campuses in metropolitan Phoenix as well as online. For more information, please visit

Press contact:
Joyce Yeung, Director of Marketing
Concept Laser
Phone: (817) 328-6500

PADT Contact
Eric Miller
PADT, Inc.
Principal & Co-Owner

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ANSYS Startup Program: The Significance of Simulation – Webinar

Phoenix Analysis & Design Technologies Presents:

ANSYS Startup Program: The Significance of Simulation 

Do you work for a startup of know someone who does?

PADT would like to invite you to attend our upcoming webinar in support of the ANSYS Startup Program.

Click Here to register for this webinar

Wednesday January 25th, from 12 pm – 1 pm MST

Join us as our own Co-Owner and Principal Eric Miller discusses how simulation software is helping new entrepreneurs and startup companies alike to shorten their time to market and reduce their manufacturing costs.

While many startups tend to avoid using simulation due to cost or a lack of accessibility, this is a key aspect of the modern manufacturing process and should not be ignored.

As a partner in the Startup Program, you will gain instant access to ANSYS solutions so you can start building virtual prototypes of your new products. These virtual prototypes can be modified and tested with simulation hundreds of times in the same time it would take to build and test one physical prototype – saving you time and money as you work to perfect your product design. The partnership gives you access to the full portfolio of multiphysics simulation bundles, including the Structural and Fluids bundle and the Electromagnetics bundle.

Click Here to register for this webinar

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Christmas Left-Right Gift Exchange Story: Western

For our Christmas parties at PADT we generally have over 40 employees so a traditional secret Santa gift exchange takes to long. So a couple of years ago we downloaded a right-left gift exchange story from the internet and it was a big hit. We ran out of stories on the internet, so we started writing our own, usually in some sort of over-the-top style.  This year, 2016, many of us had become addicted to West World, so a good old fashioned Western seemed appropriate.

Everyone gets their gift and forms a big circle in the middle of the room.  Someone with a strong voice reads the story and every time the world LEFT is read, everyone passes the package they have to the left. Every time the world RIGHT is read, everyone passes the package they have to their right.  You should pause a bit at each LEFT/RIGHT to give people a chance to pass.

You can find our older stories here

– Star Wars Christmas (2015)
– Fairy Tail Christmas (2014)
– Science Fiction Christmas (2013)
– Romance Christmas (2012)
– Film Noir Christmas (2011)

Trixie and the Christmas Miracle

A train whistle echoed in the distance as US Marshal Dilan McRightland brought his horse Righty to a stop on the left side of the ridge. Down below, right in the middle of the valley was his destination, the place he had been headed right towards for three weeks. Wrightville Gulch. He’d seen a lot of dusty towns, not much more than a few buildings on the right and left side of a crooked street. Left to his own devices he would have left his job and gone right back to his family farm on left bank of Ohio river, right below Louisville Kentucky. But he had sworn an oath to uphold justice, to make sure that wrongs were righted, and that no criminal was left free to cause more harm. It was the right thing to do.
He dug his heals into Righty and they headed right down the trial, towards an encounter that would have been best left alone.

As he entered the town from the… south, he surveyed both sides of the street. On the right was a bank, livery stable, and what looked like a hotel that may not be where the ‘right type of people’ stay. The other side of the street held a saloon, blacksmith, general store, and a Chinese restaurant: Right and Wrong Noodles. Dilan assumed that the fugitive he was seeking was in the saloon. So he tied up Righty, using a left hitch not, and went right in the swinging doors.

It took a while for his eyesight to adjust to the dim interior. A long mahogany bar filled the wall. On the… other side, there was a stair case that led to rooms on the second floor. Right in the middle of the room stood a giant Christmas tree. That was right, Dilan thought, today was Christmas Eve. It has been a long time since he had enjoyed a right proper Christmas. He began to day dream about snowy Christmas mornings when a shout brought him right back to reality.

“Hey! You looking for me, stranger?” A man dressed in head to toe in black leather stood on the left side of the Christmas tree. “If you are Lefty Peterson, then yes.” Replied Dilan. “I’m US Marshal Dilan McRightland and I’ve traveled all the way to Wrightville Gulch, right here in the middle of no-where, to bring you to justice.”

The two men stared at each other across the room, their right hands hovering over their six shooters, which for now were left in their holsters. “I don’t think you have it right, Marshal. When I left the Stanton brothers for dead, right in the middle of Dodge City, I left that life behind me. I’m clean now, I’ve got a wife and kids. I started over. You know what I did was right, they deserved to die. So the right thing for you to do is get back on your horse and get right out of town.”

This left Marshall Dilan a bit baffled. What if Lefty was right? And then he stood up straight and looked Lefty in the eye. “The law is the law lefty, doesn’t matter if you think what you did was right, it is up to a jury to decide that.” Lefty looked right back, and snarled “The only way you are taking me back is as a corpse. If you don’t leave in a coffin yourself. Their right hands slowly moved to their guns.

Just when they were about to draw a girl dressed like a dancer the left bank of the Sein in Paris, dashed right between them. “Stop right now!” she shouted. “Gosh Nabit! It’s Christmas Eve. Have you any heart left, either of you?” She turned to Lefty “Lefty, darling, you don’t have to die. If you think what you did was… justified, go with the marshal, argue your case.” She spun to face Marshall McRightland “And you, you come riding in here on Christmas ever, where we was having ourselves a right nice party, and you threaten our friend Lefty, that just aint right either!”

Dilan stood. He could see the star on his chest reflected in the mirror behind the bar, and he could see the star right on top of the tree. And he looked right at the dancing girl, a small tear falling from her right eye. “Lefty” he said “you agree to let me handcuff you to that bar there, and we can have ourselves a right proper Christmas ever and morning. And then we ride out of here and you get your day in court. Does that sound…. All right?” Lefty thought for a minute, then responded “I right reckon that is the right thing to do. Right here and right now on Christmas eve, maybe some peace on earth is what we need.”

And so on that Christmas Eve in a dusty town right in the middle of no-where, a little Christmas spirit, and a fiery dancing girl named… Trixie, brought a little peace on earth and goodwill towards men to a place called Wrightville Gulch.

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Phoenix Business Journal: ​Is acquiring disruptive innovation good for everyone?

It seems like the trend these days is for large companies to not do R&D in house. Instead the let StartUps develop innovation and then buy it when the market proves it out.  I had to ask myself “​Is acquiring disruptive innovation good for everyone?”  I don’t think it is and explain why in this week’s blog post.

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Phoenix Business Journal: ​When did we start thinking amateurs were a good idea?

We have a problem. At some point it become not just OK, but prefered to count on amateurs to tackle difficult problems. In politics and in business it is a trend to go with people who have no background and no experience. Seeing the results, I a not a fan.  In “​When did we start thinking amateurs were a good idea?” I look at this disturbing reality, why it is a bad idea, and offer some suggestions on turning things around.

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PADT Events – January 2017

Welcome to 2017. We are all very excited about what we have planned for events this year. As we travel around the country, and the world, we hope to have to chance to meet many of you who follow PADT. 2017 will look a lot like 2016 except that, based on your feedback, we will be trying more on-line webinars and events.  As always, contact us if you have any questions.

Launch of ASU Manufacturing Research and Innovation Hub

ASU Polytenic Campus
Mesa, AZ

PADT will be on-hand at ASU Polytechnic school for the launch of ASU’s new Manufacturing Research and Innovation Hub. Stop by to see their new facilities and meet the students and staff along with partners like PADT that helped make it happen.
Learn more

ANSYS Startup Program Webinar: The Significance of Simulation


This seminar will discuss how ANSYS simulation software can be used by startups to shorten their time to market and reduce their manufacturing costs. We will discuss what simulation is and how to use it effectively, as well as go over the ANSYS Startup Program and how it gives early stage companies access to world class simulation.
Learn more

Invited Speaker at the 2017 Arizona Science Bowl (High School Event)

ASU West Campus
Glendale, AZ

PADT’s Dhruv Bhate, PhD will speak to students at the High School Science Bowl. This is a great event, and if you have never been, you should go. The level of technology and scientific rigour fo these Middle School and High School kids is amazing.
Learn more

Tesla Test Drive at PADT

PADT Tempe
Tempe, AZ

Yes, you read that right. We will be inviting customers to come to PADT and see how the simulation and 3D Printing technologin we sell, support, and use is applied to advanced automotive systems – Cool Cars! Tesla Motors has been kind enough to partner with us to allow a select few the oportunity to test drive a Tesla. Look for your invite via email and register quickly, space is limited.
Learn more
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Metal AM Magazine Article: Modeling the Mechanical Behaviour of Additively Manufactured Cellular Structures

Fig 1. Metal AM Magazine Cover: Winter 2016 (Vol. 2, No. 4)

Metal AM Magazine publishes an article by PADT!

Our 10-page article on “Modeling the Mechanical Behavior of Cellular Structures for Additive Manufacturing” was published in the Winter 2016 edition of the Metal AM magazine. This article represents a high-level summary of the different challenges and approaches in addressing the modeling specific aspects of cellular structures, along with some discussion of the design, manufacturing and implementation aspects associated with AM.

Click HERE for link to the entire magazine, our article starts on page 51. Digital editions are free to download. Swing by PADT in the new year to pick up a hard copy or look for it at our table when you visit us at trade shows.

To stay in touch with the latest developments at the intersection of AM and Cellular Structures, connect with me on LinkedIn, where I typically post 1-2 blog posts every month on this, or related subjects in Additive Manufacturing.

Fig 2. Dimensional tolerances and how the influence models – one of the many concepts discussed in the article

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The next webinar of the ANSYS Breakthrough Energy Innovation Campaign is now available!

 Register here to watch

Thermal Optimization for Energy Efficiency 

Nearly everything has an optimal operating temperature and thermal condition. Millions of dollars each year are spent generating and transporting thermal energy to achieve thermal goals. Thermal optimization not only improves the economy of transporting energy, maintaining building temperatures, manufacturing processes and products, it improves their efficiency as well. Engineers use simulation to reveal detailed pictures of thermal processes, providing a deep understanding of all aspects of thermal management.

Join our experts for this Webinar to learn how you can capture thermal processes in powerful simulations, seamlessly identify multiphysics interactions that impact performance, and quickly achieve thermal optimization using integrated design optimization tools.
Register Here – or Click Here for more information on Thermal Optimization

This webinar is presented by Richard Mitchell and Xiao Hu

Richard Mitchell is the Lead Product Marketing Manager for Structures. He joined ANSYS in 2006 working in pre-sales and support roles. Before this Richard was an ANSYS user working for a high tech company in the UK. He worked as an analyst on space and vacuum tube technologies.


Xiao is a principal engineer at ANSYS Inc. Xiao has spent a combined 12 years of his career at ANSYS and Fluent corporation working with customers in the modeling and simulation of powertrain related applications. Xiao spent his earlier years with Fluent working on engine CFD applications.

Keep checking back to the Energy Innovation Homepage for more updates on upcoming segments, webinars, and other additional content.

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Metal 3D Printing a Shift Knob

I have always had an issue with leaving well enough alone since the day I bought my Subaru. I have altered everything from the crank pulley to the exhaust, the wheels and tires to the steering wheel. I’ve even 3D printed parts for my roof rack to increase its functionality. One of the things that I have altered multiple times has been the shift knob. It’s something that I use every time and all the time when I am driving my car, as it is equipped with a good ol’ manual transmission, a feature that is unfortunately lost on most cars in this day and age.


I have had plastic shift knobs, a solid steel spherical shift knob, a black shift knob, a white shift knob, and of course some weird factory equipment shift knob that came with the car. What I have yet to have is a 3D printed shift knob. For this project, not any old plastic will do, so with the help of Concept Laser, I’m going straight for some glorious Remanium Star CL!

One of the great things about metal 3D printing is that during the design process, I was not bound by the traditional need for a staple of design engineering, Design For Manufacturing (DFM). The metal 3D printer uses a powder bed which is drawn over the build plate and then locally melted using high-energy fiber lasers. The build plate is then lowered, another layer of powder is drawn across the plate, and melted again. This process continues until the part is complete.

The design for the knob was based off my previously owned shift knobs, mainly the 50.8 mm diameter solid steel spherical knob. I then needed to decide how best to include features that would render traditional manufacturing techniques, especially for a one-off part, cost prohibitive, if not impossible.   I used ANSYS Spaceclaim Direct Modeler as my design software, as I have become very familiar with it using it daily for simulation geometry preparation and cleanup, but I digress, my initial concept can be seen below:2016-10-18_16-19-33

I was quickly informed that, while this design was possible, the amount of small features and overhangs would require support structure that would make post-processing the part very tedious. Armed with some additional pointers on creating self supporting parts that are better suited for metal 3D printing, I came up with a new concept.


This design is much less complex, while still containing features that would be difficult to machine. However, with a material density of 0.0086 g/mm^3, I would be falling just short of total weight of 1 lb, my magic number. But what about really running away from DFM like it was the plague?


There we go!!! Much better, this design iteration is spec’d to come out at 1.04 lbs, and with that, it was time to let the sparks fly!


Here it is emerging as the metal powder that has not been melted during the process is brushed away.


The competed knob then underwent a bit of post processing and the final result is amazing! I haven’t been able to stop sharing images of it with friends and running it around the office to show my co-workers. However, one thing remains to make the knob functional… it must be tapped.


In order to do this, we need a good way to hold the knob in a vise. Lucky for us here at PADT, we have the ability to quickly design and print these parts. I came up with a design that we made using our PolyJet machine so we could have multiple material durometers in a single part. The part you need below utilizes softer material around the knob to cradle it and distribute the load of the vise onto the spherical lattice surface of the knob.


We quickly found out that the Remanium material was not able to be simply tapped. We attempted to bore the hole out in order to be able to press in an insert, and also found out the High Speed Steel (HSS) was not capable of machining the hole. Carbide however does the trick, and we bored the hole out in order to press in a brass insert, which was then tapped.


Finally, the shift knob is completed and installed!



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Phoenix Business Journal: ​How far away are we from 3D Printing the androids on ‘Westworld?’

A bit of a twist for this weeks Phoenix Busines Journal blog post… “​How far away are we from 3D Printing the androids on ‘Westworld?‘”  In discussing this great new reboot of a classic, and yet another fantastic cautionary tale from Michael Crhichton, a couple people started wondering how far off the tech in the show is.  The answer, well you will have to read the article.

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ANSYS Breakthrough Energy Innovation Campaign – Thermal Optimization

Information regarding the next topic in the Breakthrough Energy Innovation Campaign has been released, covering Thermal Optimization and how ANSYS simulation software can be used to help solve a variety of issues related to this topic, as well as capture all thermal processes.

Additional content regarding thermal optimization can be viewed and downloaded here.

This is the next topic of a campaign that covers five main topics:

  1. Advanced Electrification 
  2. Machine & Fuel Efficiency
  3. Thermal Optimization
  4. Effective Lightweighting
  5. Aerodynamic Design

Information on each topic will be released over the course of the next few months as the webinars take place.

Sign Up Now to receive updates regarding the campaign, including additional information on each subject, registration forms to each webinar and more.

We here at PADT can not wait to share this content with you, and we hope to hear from you soon.

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Modeling 3D Printed Cellular Structures: Approaches

How can the mechanical behavior of cellular structures (honeycombs, foams and lattices) be modeled?

This is the second in a two-part post on the modeling aspects of 3D printed cellular structures. If you haven’t already, please read the first part here, where I detail the challenges associated with modeling 3D printed cellular structures.

The literature on the 3D printing of cellular structures is vast, and growing. While the majority of the focus in this field is on the design and process aspects, there is a significant body of work on characterizing behavior for the purposes of developing analytical material models. I have found that these approaches fall into 3 different categories depending on the level of discretization at which the property is modeled: at the level of each material point, or at the level of the connecting member or finally, at the level of the cell. At the end of this article I have compiled some of the best references I could find for each of the 3 broad approaches.

1. Continuum Modeling

The most straightforward approach is to use bulk material properties to represent what is happening to the material at the cellular level [1-4]. This approach does away with the need for any cellular level characterization and in so doing, we do not have to worry about size or contact effects described in the previous post that are artifacts of having to characterize behavior at the cellular level. However, the assumption that the connecting struts/walls in a cellular structure behave the same way the bulk material does can particularly be erroneous for AM processes that can introduce significant size specific behavior and large anisotropy. It is important to keep in mind that factors that may not be significant at a bulk level (such as surface roughness, local microstructure or dimensional tolerances) can be very significant when the connecting member is under 1 mm thick, as is often the case.

The level of error introduced by a continuum assumption is likely to vary by process: processes like Fused Deposition Modeling (FDM) are already strongly anisotropic with highly geometry-specific meso-structures and an assumption like this will generate large errors as shown in Figure 1. On the other hand, it is possible that better results may be had for powder based fusion processes used for metal alloys, especially when the connecting members are large enough and the key property being solved for is mechanical stiffness (as opposed to fracture toughness or fatigue life).

Fig 1. Load-displacement curves for ULTEM-9085 Honeycomb structures made with different FDM toolpath strategies

2. Cell Level Homogenization

The most common approach in the literature is the use of homogenization – representing the effective property of the cellular structure without regard to the cellular geometry itself. This approach has significantly lower computational expense associated with its implementation. Additionally, it is relatively straightforward to develop a model by fitting a power law to experimental data [5-8] as shown in the equation below, relating the effective modulus E* to the bulk material property Es and their respective densities (ρ and ρs), by solving for the constants C and n.


While a homogenization approach is useful in generating comparative, qualitative data, it has some difficulties in being used as a reliable material model in analysis & simulation. This is first and foremost since the majority of the experiments do not consider size and contact effects. Secondly, even if these were considered, the homogenization of the cells only works for the specific cell in question (e.g. octet truss or hexagonal honeycomb) – so every new cell type needs to be re-characterized. Finally, the homogenization of these cells can lose insight into how structures behave in the transition region between different volume fractions, even if each cell type is calibrated at a range of volume fractions – this is likely to be exacerbated for failure modeling.

3. Member Modeling

The third approach involves describing behavior not at each material point or at the level of the cell, but at a level in-between: the connecting member (also referred to as strut or beam). This approach has been used by researchers [9-11] including us at PADT [12] by invoking beam theory to first describe what is happening at the level of the member and then use that information to build up to the level of the cells.


Fig 2. Member modeling approach: represent cellular structure as a collection of members, use beam theory for example, to describe the member’s behavior through analytical equations. Note: the homogenization equations essentially derive from this approach.

This approach, while promising, is beset with some challenges as well: it requires experimental characterization at the cellular level, which brings in the previously mentioned challenges. Additionally, from a computational standpoint, the validation of these models typically requires a modeling of the full cellular geometry, which can be prohibitively expensive. Finally, the theory involved in representing member level detail is more complex, makes assumptions of its own (e.g. modeling the “fixed” ends) and it is not proven adequately at this point if this is justified by a significant improvement in the model’s predictability compared to the above two approaches. This approach does have one significant promise: if we are able to accurately describe behavior at the level of a member, it is a first step towards a truly shape and size independent model that can bridge with ease between say, an octet truss and an auxetic structure, or different sizes of cells, as well as the transitions between them – thus enabling true freedom to the designer and analyst. It is for this reason that we are focusing on this approach.


Continuum models are easy to implement and for relatively isotropic processes and materials such as metal fusion, may be a good approximation of stiffness and deformation behavior. We know through our own experience that these models perform very poorly when the process is anisotropic (such as FDM), even when the bulk constitutive model incorporates the anisotropy.

Homogenization at the level of the cell is an intuitive improvement and the experimental insights gained are invaluable – comparison between cell type performances, or dependencies on member thickness & cell size etc. are worthy data points. However, caution needs to be exercised when developing models from them for use in analysis (simulation), though the relative ease of their computational implementation is a very powerful argument for pursuing this line of work.

Finally, the member level approach, while beset with challenges of its own, is a promising direction forward since it attempts to address behavior at a level that incorporates process and geometric detail. The approach we have taken at PADT is in line with this approach, but specifically seeks to bridge the continuum and cell level models by using cellular structure response to extract a point-wise material property. Our preliminary work has shown promise for cells of similar sizes and ongoing work, funded by America Makes, is looking to expand this into a larger, non-empirical model that can span cell types. If this is an area of interest to you, please connect with me on LinkedIn for updates. If you have questions or comments, please email us at or drop me a message on LinkedIn.

References (by Approach)

Bulk Property Models

[1] C. Neff, N. Hopkinson, N.B. Crane, “Selective Laser Sintering of Diamond Lattice Structures: Experimental Results and FEA Model Comparison,” 2015 Solid Freeform Fabrication Symposium

[2] M. Jamshidinia, L. Wang, W. Tong, and R. Kovacevic. “The bio-compatible dental implant designed by using non-stochastic porosity produced by Electron Beam Melting®(EBM),” Journal of Materials Processing Technology214, no. 8 (2014): 1728-1739

[3] S. Park, D.W. Rosen, C.E. Duty, “Comparing Mechanical and Geometrical Properties of Lattice Structure Fabricated using Electron Beam Melting“, 2014 Solid Freeform Fabrication Symposium

[4] D.M. Correa, T. Klatt, S. Cortes, M. Haberman, D. Kovar, C. Seepersad, “Negative stiffness honeycombs for recoverable shock isolation,” Rapid Prototyping Journal, 2015, 21(2), pp.193-200.

Cell Homogenization Models

[5] C. Yan, L. Hao, A. Hussein, P. Young, and D. Raymont. “Advanced lightweight 316L stainless steel cellular lattice structures fabricated via selective laser melting,” Materials & Design 55 (2014): 533-541.

[6] S. Didam, B. Eidel, A. Ohrndorf, H.‐J. Christ. “Mechanical Analysis of Metallic SLM‐Lattices on Small Scales: Finite Element Simulations versus Experiments,” PAMM 15.1 (2015): 189-190.

[7] P. Zhang, J. Toman, Y. Yu, E. Biyikli, M. Kirca, M. Chmielus, and A.C. To. “Efficient design-optimization of variable-density hexagonal cellular structure by additive manufacturing: theory and validation,” Journal of Manufacturing Science and Engineering 137, no. 2 (2015): 021004.

[8] M. Mazur, M. Leary, S. Sun, M. Vcelka, D. Shidid, M. Brandt. “Deformation and failure behaviour of Ti-6Al-4V lattice structures manufactured by selective laser melting (SLM),” The International Journal of Advanced Manufacturing Technology 84.5 (2016): 1391-1411.

Beam Theory Models

[9] R. Gümrük, R.A.W. Mines, “Compressive behaviour of stainless steel micro-lattice structures,” International Journal of Mechanical Sciences 68 (2013): 125-139

[10] S. Ahmadi, G. Campoli, S. Amin Yavari, B. Sajadi, R. Wauthle, J. Schrooten, H. Weinans, A. Zadpoor, A. (2014), “Mechanical behavior of regular open-cell porous biomaterials made of diamond lattice unit cells,” Journal of the Mechanical Behavior of Biomedical Materials, 34, 106-115.

[11] S. Zhang, S. Dilip, L. Yang, H. Miyanji, B. Stucker, “Property Evaluation of Metal Cellular Strut Structures via Powder Bed Fusion AM,” 2015 Solid Freeform Fabrication Symposium

[12] D. Bhate, J. Van Soest, J. Reeher, D. Patel, D. Gibson, J. Gerbasi, and M. Finfrock, “A Validated Methodology for Predicting the Mechanical Behavior of ULTEM-9085 Honeycomb Structures Manufactured by Fused Deposition Modeling,” Proceedings of the 26th Annual International Solid Freeform Fabrication, 2016, pp. 2095-2106

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PADT Events – December 2016

PADT-Events-LogoWelcome to December! The holiday season is upon us as is the end of 2016.  It has certainly been an eventful year, although we don’t have a lot going on event wise this month, just two things.

We will take this oportunity to send a Happy Hollidays! to everyone and wishing all a very merry New Year!  Come back in January and we will have lots to share, it’s going to be a busy year.

As a reminder, PADT is closed the week of December 26-30, 2016.


December 1: Phoenix, AZ
BioAccel Solutions Challenge for BioTech Startup in Arizona

This is a fantastic event that puts a nice cap to the year for Biotech startups in Arizona, and PADT is proud to be a sponsor.  We will be at the “Scorpion Pit” competition as well as the networking event after. See you there.

The full agenda and all the details for this event are here.


December 6: Albuquerque, NM
Medical Device Product Development for Startups, The Bitter Pill

We will be in New Mexico for this lunch time event looking in to the harsh realities of doing a Medical Device startup.  All are welcome!  We hope this is the first of many regular seminars with the New Mexico Technology Council.

Get the details and register here.


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