Open the Door to Material Optimization

Introduction:

The world is evolving at a quantum speed with transforming technology. The human life cycle has enriched to tend towards advancement in every sector. Businesses have adopted the transformation & obtained the benefits of technology involved in their mainframe process to become multi-billion companies. Making the product a hero in every story, Engineering the product or designing a physical product demands focus on four major factors; Design, Analysis, Manufacturing, and Materials. Though the first three factors are digitally transformed, the materials field is still lagging. This article exclusively focuses on optimizing your right material choices.

Let’s start with an example of a company facing a similar conundrum.

Background:

Referring to the case of an OEM, that manufactures an industry renowned product.

Due to the stringent industry standards and to stay ahead of the competition, the company decided to optimize their existing product and its performance. Following the advancement, they relied on simulation results for quick feedback from different design iterations. Furthermore, parametric optimization yielded them even better results compared to the manual design iterations. Overall, they were able to achieve a 9% Reduction in Weight and a 5 % Increase in Efficiency through Design Optimization.

Much to the Team’s surprise, the product manager wasn’t satisfied with this result. Hence, he assembled his team to initiate an experiment with different material types. 

The broader idea of this activity was to understand how he could improve his product and cut down costs to the company, thereby naming this activity as Material optimization.

Optimized Product = Design Optimization + Material optimization

Following the superior’s directive, the team started dedicating their efforts to bring the best out of current results, in terms of cutting costs, reducing development time & raising standards of performance. Four of his team members heading the R & D were investigating the case study with different ideas as below. 

The first member tried to use the existing available material data with him to see if he can get the best possible combination;

The second member tried to use the materials preferred by his company to avoid supply chain issues;

The third member tried to reach out to suppliers/consultants for a piece of advice on material choices; and

The fourth member tried to browse on the internet for material data.

However, none of the above approaches answered the below questions

  1. Which material to choose?
  2. Is there a better material choice available?
  3. Is there a cheaper solution? 
  4. Is the chosen data, reliable?
Capture

This is where companies need a tool like, Granta Selector which does answer the above questions.

Granta Selector is a tool that can help optimize your material choices, which not only has material properties of metals, plastics, polymers, ceramics and various other classes of materials but also has features like search, plot and compare your choice of materials as shown in the pic.

Apart from the features mentioned above, Granta selector has:

  • FE export tool to export simulation ready material data to most of the FE platforms
  • synthesizer tool to estimate material and process cost 
  • Eco Audit tool to estimate the environmental impact of the selected material at the early stage of the design.

To use how to use above-mentioned tools, click here to understand how Tecumseh, a global leader of commercial refrigeration compressors used Granta Selector to reduce development time by three-fold and saved millions of euros of cost savings from making the right materials decisions.

please click here for more information on Material optimization.

Please feel free to connect with us at marketing@cadfem.in or +91-9849998435, for a quick Demo on this Product.

Author Bio:

Mr. Gokul Pulikallu, Technical Lead-South

CADFEM India

Mr. Gokul Pulikallu has done his Bachelor of Technology  & he is carrying 9 years of experience in the field of structural Mechanics simulation and optimization. His main focus is Design Optimization & Material Optimization and helps customers adopt these technologies efficiently.

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Robustness Evaluation – Why Bother?

This article will explain how ANSYS optiSLang can be used for robustness evaluation in virtual product development.

A successful product. Isn’t that the goal for every product company? It begins right from the step where engineers come up with world class product innovations to getting the right marketing mix that brings commercial success. Is every product successful? No. Is every product with a great design successful? Maybe.

The Symptom

Robustness Evaluation - Why Bother?
Courtesy: Android Authority

More often than not, we find market leaders stumble with product failures. The infamous Samsung’s Note 7 will come to your mind instantly. Hundreds of users were at the forefront of dangerous incidents where phones caught fire due to short-circuiting. Samsung conducted severe internal testing and several independent investigations. They found that, in certain extreme situations, electrodes inside each battery crimped, weakened the separator between the electrodes, and caused short circuiting. In some other cases, batteries had thin separators in general, which increased the risks of separator damage and short circuiting. Economics-wise, the incident caused Samsung to recall 2.5 million devices, lose over $5 billion and damaged its reputation.

Faulty Takata airbags’ inflators contained a defect that cause some of them to explode and project shrapnel into drivers and passengers. 50+ people worldwide lost their lives due to this design failure. 70 million Takata airbag inflaters were to be recalled at a cost of $9 billion to its automaker customers. For a Tier-I supplier, this liability was so huge that they filed for bankruptcy.

Such glaring errors after product launch, with severe economic implications, aren’t limited to Samsung and Takata alone. Honda, Michelin and many more companies have been involved in product recalls due to design failures.

Obviously, such design flaws need to be mitigated. Isn’t it?

The Probable Solution

To preempt design failures, today’s engineers use state-of-the-art engineering technology. Traditionally, product development teams used extensive prototyping and testing to validate design variants during the design life cycle. Of course, this is cumbersome, expensive and time-consuming.

Over the past few decades, engineering simulations have opened up a whole new range of possibilities for the design engineers. ANSYS, Inc., the market leader for engineering simulations, provides state-of-the-art technology to simulate systems involving mechanical, fluid, electrical, electronic and semiconductor components. With added insight, design engineers are able to test a lot more design variants on a virtual platform using this technology.

Consequently, the benefits – innovation, lowered cost of product development, higher product profitability and faster time-to-market. The staggering economic benefits and tremendous value on the offer have prompted several product companies to introduce simulations upfront using a Simulation-Driven Product Development approach.

Companies like Samsung and Takata were power users of engineering simulations. They used technology extensively in their design phase and perform virtual tests to validate designs. Only validated designs were put through production, QA and then sent off to the market. Despite simulating and validating designs, these companies witnessed monumental product failure in the market that caused loss of life, led to economic losses and damage to their reputation.

If they used simulation-driven product development, what went wrong?

The Cause

While the probable solution can mitigate and even eliminate design failures, there are other forces at play that you will need evaluate carefully. Hence it is imperative to understand the root cause for occurrence of design failures despite conducting extensive state-of-the-art simulations.

Many design engineers often undermine or do not consider one important aspect due to lack of proper understanding. Variability. Just as design parameters such as thickness or physical loads can be varied to test different design variants, some parameters display inherent variability.

Let me explain it with a material parameter: Young’s Modulus. If you’re an engineer by qualification, you would’ve come across the Universal Testing Machine (UTM) in your freshman or sophomore year of college. To test the Young’s Modulus of any given material (say steel), the UTM pulls a material specimen at extreme ends to create tension. Using mathematical calculations, you’ll arrive at a number close to 210 MPa as the Young’s Modulus of mild steel. Let’s say you repeat this test for 99 other specimens of the same material. Each test result will be different and it will never be the same. Other than the odd case of a faulty UTM apparatus, there’s only one reason for that. Natural Scatter.

The Hero: Robustness Evaluation

Such variability (statistical) will lead to variability in the performance parameters of the product. Obviously this is quite important and engineers need to assess designs for variability well ahead of product launch. For variability, you have only one way to assess designs for product failure or risks: Robustness Evaluation.

Robustness Evaluation with ANSYS optiSlang

The preferred choice of tool for robustness evaluation is ANSYS optiSLang. For better understanding, there is a lot of material available in more detail. Instead of reading, you may also want to consider watching these webinars here and here.

Can you attribute lack of design robustness to any other product failures that you have witnessed? Do you have alternate views? Please let me know in the comments section.

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Electromagnetic Simulation for Antennas

In this first part of a multi-part series, I will discuss many aspects of antenna design & analysis with the underlying theme of electromagnetic simulation-driven product development. In this part, I will briefly talk about performing stand-alone Antenna Design, Analysis & Optimization using Electromagnetic Simulation.

Increasing Importance of Electromagnetic Simulation (EM)

While still in university, I imagined antenna design to be very simple. Based on the given frequency, we will need to calculate dimensions and then fabricate the design. That’s it. A decade ago, I found simulation to appear like dark art or black magic. If the fabricated antenna did not work well, I needed to iterate the physical design till it gave good results.

During the recent years, several EM simulation tools have emerged to evaluate the exact solution of Maxwell Equations for estimating the electromagnetic behavior of the devices. These tools used underlying methods like Finite Element Method (FEM), Method of Moments (MoM) and Finite Difference Time Domain (FDTD). Generally, we can divide the part components of the electronic design into active and passive devices. The modelling of the active devices is based on nonlinear measurement data parameters like S-parameters and X-parameters. When we come across modelling of passive devices, they are very simple because of their linear nature. However, it is important to understand the limitations of those devices.

The main role of the simulation is for to engineers to be able to accurately predict how complex products will behave in real-world environment enabling the complete virtual prototyping. ANSYS HFSS, a state-of-the-art high-frequency electromagnetic simulation, helps to estimate the radiation characteristics of the antenna and optimize the design as per requirement.

Parameters To Be Considered For Antenna Simulation

In general, engineers know that dimension can be reduced by increasing the substrate dielectric constant. Using standardized equations, we can estimate the size of the patch. However we cannot estimate radiation characteristics among a few other quantities. Using simulation tools, we can replace physical iterations with virtual iterations; we can identify the optimal design that matches the required specifications.

Why do some engineers get different results? Is there anything else that needs to be considered? Yes, engineers who focus only on model dimensions and not on boundaries and excitation will obtain inaccurate results.

Modeling and Setup

Let me consider the example of a GPS antenna that needs to have a gain of 3.5dB. For this gain, we’ll need to identify a antenna design with the smallest possible antenna dimension.

Let’s look at three substrates RT Duriod 5880, FR4 and Alumina. Using ANSYS HFSS, you can model the full antenna by using in-built modeling options or import the design from external CAD software. Initial dimensions of the patch are calculated using standard formulas available in academic literature.

Image of 3D CAD model of a patch antenna before performing electromagnetic simulation
Antenna Model

Patch antennas can be fed power by various methods such as microstrip line or coaxial/SMA. While using coaxial input, many don’t consider the dimensions of the coaxial. A good engineer initially checks for the dimensions of the coax in order to get the characteristic impedance, which directly affects the frequency of operation and voltage standing wave ratio or VSWR.

For assignment of different materials for model, HFSS has an inbuilt material library where you can select the required material for substrate, conductors, etc. If you want to use a material which is not in the library or if you want to add some frequency-dependent properties, then you can modify or create a new material.

Image of Materials available in HFSS before performing electromagnetic simulation
HFSS Material Library

Image showing addition/modification of materials before performing electromagnetic simulation
New Material Creation

For antenna design, radiation is another important boundary in order to accurately estimate the EM emission. As a good practice, the distance of at least λ/4 or λ/8 must be maintained between the antenna and the boundary. For example, λ/4 will be a good distance for radiation boundary and λ/8 for PML boundary. This is an important aspect that many engineers fail to consider. Upon completion of the initial setup, I ran the simulation to check for its performance.

Parameterization of Antenna

After simulation, check the input electric field in coaxial and the impedance of the transmission line/coax in order to verify the expected excitation. In post-processing, do check important parameters for radiation characteristics like pattern and gain. Even the near field data, which is complex to obtain from measurement, can be estimated with simulation.

Since we are not considering any fringing field and probe effects, there will be variation of results. To further improve the design, I suggest using optimization algorithms such as Optimetrics or ANSYS optiSLang. Such tools also permit sensitivity of the design due to fabrication tolerances.

The available optimetrics options in HFSS
Optimetrics in HFSS

Image describes the effect on resonance frequency due to probe position variations while performing electromagnetic simulation
Probe position effect on resonance frequency

Optimal Design of Antenna

Finally, the best design can be selected after evaluating the gain characteristics of the all variations. For the three substrates, I evaluated the optimized dimensions of the patch using Optimetrics:

  • 12.5 x 10 cm² for Duroid
  • 9.5 x 7.5 cm² for FR4
  • 7 x 5 cm² for Alumina

Image shows the estimated gain plot for different substrate materials while performing electromagnetic simulation
Gain Variation vs Substrate

Per this, antenna with FR4 substrate meets the required gain of 3.5dB with the least possible dimension. Better performance can be obtained by varying other parameters such as height of the substrate, etc.

The next time you perform electromagnetic simulation of antenna, do remember to consider all the boundaries.

This concludes the first part of a multi-part series on antenna design & analysis. In the next part, I will discuss about antenna placement analysis.

If you have any questions, please feel free to comment or fill out the contact form.

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Weekly Simulation Round-Up | Issue 4

Banner for Weekly Simulation RoundupWelcome to this edition of Weekly Simulation Round-Up. As usual, we bring you the most interesting articles from this week.


Computational Fluid Dynamics for Patient-Specific Surgeries

In this article, a researcher of Cardiothoracic Surgery from the Shanghai Children’s Medical Center talks about simulation-driven medical surgeries. He states that “the long-term prognosis for babies born with single ventricle heart defects can depend on the location of vascular connections made during corrective surgery”. Based on the babies’ cardiovascular anatomy, the medical center researchers employ ANSYS CFD to determine the optimal personalized surgery for improving surgical effectiveness and to obtain a better quality of life for children (read more).


Simulation of “Material Other Than Grain” Separation Process

For farmers engaged in grain production, separation of stones, straw and dust from grains is a regular activity. Can simulation techniques help such farmers to reduce cost and time-to-market, and increase grain production ramp-up? The answer is yes!

Using Rocky DEM, a state-of-the-art discrete element particle simulator, along with computational fluid dynamics performed using ANSYS Workbench, the material other than grain (MOG) separation process can be simulated. There’s potential for the MOG equipment makers to redesign or improve for higher separation efficiency.


Optimus Prime, anyone? 

Antimon is a BMW 3-series car that transforms into a robot in under 30 secs. It converts from a BMW into a grand robot completed with powerful arm movements and a Transformers-like face. It took the company eight months to complete Antimon using an actual car (Watch on YouTube).


Diamonds Convert Nuclear Waste into Clean Energy

British Scientists have developed a method of turning nuclear waste into batteries using diamond. By encapsulating a short-range radioactive material in an artificial diamond, small electrical charge can be generated even after insulating harmful radiation. Researchers estimate that a carbon-based battery would generate 50% of its power in 5,730 years (read more).


Structural Design Optimization of Electrical Transformer Tanks

One of our customers, Crompton Greaves Ltd., presented their experiences with optimizing structural design of their electrical transformer tanks. To achieve this, their engineers used the state-of-the-art tool for optimization and variation analysis – optiSLang and ANSYS Mechanical. Through this exercise, they were able to obtain ~10% weight reduction and over 17% reduction in Equivalent Stress (read more).


So, folks, that was all for this week. We will be back again with a new edition next week. Do feel free to share your feedback or questions with us.

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Weekly Simulation Round-Up | Issue 2

Banner for Weekly Simulation Round-Up

I wish you a Happy New Year! Hope you had a great start to 2017. In this edition of the Weekly Round-Up, I’m sharing my favorite posts on how simulation has helped in thermal drying of automotive body-in-white, reconstruction of historic Berlin City, PCB support design and design & development of smart cities.


Simulation-Driven Development of a Drying Oven (Case Study)

High-quality standards apply to the drying process of car body paints. In this case study, you will understand how “with simulations of the oven behavior in the design phase, an optimized oven design was found which fulfills the required criteria of the drying process.” In the process of applying simulations, the customer said “virtual technology helped to avoid expensive changes after the oven is built, and to gain more insight into the manufacturing process (read more).”


Optimization of PCB Supports During In-Circuit Testing

Current automobile industry is driven by interacting electronic controls for which electrical verification tests needs to be carried out. This presentation by Robert Bosch, one of our oldest customers, at the 6th Optimization and Stochastic Days 2016, explains lots of interesting things. Their work explains that “engineer uses judgement and experience to place the supports, and does trials in simulations! Can this be eliminated.” The article then goes on to describe how optiSLang was used to optimize the number of PCB supports to aid the engineers (read more).


Role of CFD in Reconstruction of Berlin City Palace

The Berlin City Palace was a royal and imperial palace in the centre of Berlin. It was heavily damaged in World War II and later destroyed during conflict. Today this important historic site is being rebuilt and ANSYS CFD simulation has played and important role in helping “engineers optimize the many, and often conflicting, requirements of the climate-control system. Engineers are confident that the palace will meet all requirements, including energy conservation, comfort, artistic preservation and costs (read more).”


University of Cape Town uses Rocky DEM to Simulate Particle Behavior

University of Cape Town is gaining multiple advantages using Rocky DEM. In this interview, Dr. Indresan Govender says how “Rocky DEM will be instrumental in extending theories to include realistic shapes.” In addition, he adds by saying that “Rocky is the only DEM package that handles proper shapes. Other packages artificially achieve this by clumping spheres together. Rocky’s main advantages are realistic particle shapes and extension to GPU computing. (read more).


Building Digital City Twins

The combination of semantic 3D city model with numeric simulation offers a great potential for risk reduction. With the 3D City Model, you achieve illustrative scenarios for many applications, such as the analysis of dangerous situations and the planning of necessary preventive measures. Also complex consequences like climate change in the city are getting clearer and more visible (read more).

Thank you! Hope you have found this week’s posts interesting.

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