Error message

Deprecated function: Array and string offset access syntax with curly braces is deprecated in include_once() (line 20 of /usr/www/users/qfinsgbjyt/includes/file.phar.inc).

Making Smart Material Choices with Ansys Granta Selector(TM)

Written By: 
Jeanri Van Tonder

1.  Material Mayhem

When developing a new product, one can say that the significance of reliable materials data is grossly underestimated.

When a product is designed, the three main factors that are taken into consideration is its shape, its material of construction and how it will be manufactured [1]. These three factors are varied and optimised for a specific product function, by utilising a variety of dedicated tools. The shape and function of a new product may be optimised through the application of computer-aided design (CAD) and computer-aided engineering (CAE) technologies, while manufacturing can be optimised through computer-aided manufacturing (CAM). While companies invest significant capital in these dedicated tools, they neglect to give the material consideration of a new product the necessary attention and fail to maintain reliable centralised materials information over the lifetime of the product [1].

Especially with the visibly increasing trends in additive manufacturing (AM), it is worrisome to note that for plastic products, the biggest cause of product failure has been determined as poor specification and material selection. During an investigation done by Smithers Rapa, who investigated thousands of product failures, it was found that 45% of the product failures in plastic products was due to this issue [2].

So why is the case and what can be done about it?

2. The First World Problems

There are a number of reasons why the digitalisation and integration of material information are lagging in the digital transformation. Not only are comprehensive material libraries and multi-dimensional mathematical models of material properties difficult to define and assemble, but managing the complexity and diversity of material information is a daunting task on its own. Material information can range from the mechanical properties that will be used by a structural engineer, to a number of other factors such as statutory compliance, environmental impact, cost, availability etc. Data for a material is generated in many different places, and sets of material data are sometimes handled independently, which makes maintaining traceability of the information a challenge [1]. There is a clear need for centralised and consistent material information within organisations to ensure that material properties can be obtained and validated quickly and easily over the product life time.

It also doesn’t help that the same material could be designated by two different international standards, and that numerous equivalent materials exist across the globe. How is one to filter through all this data and make an informed decision in a productive manner? During a survey done by Granta in 2016, it was found that 20% of material tests are repeated unnecessarily and that engineers spend an average of 30 minutes a day on searching for reliable material data [1]. It was estimated by an analyst firm that an enterprise that employs a thousand knowledge workers, wasted almost $2,5 million a year by not being able to locate and retrieve information [3].

Material selection is no easy task either, as the selection process is often subject to a number of uncertainties and constraints. Material specifications are meant to make this job easier, but it is often found that they are poorly defined or not directly comparable [4]. Furthermore, making appropriate material choices at the onset of a new product development is critical, as changing the material later on may have significant costs and challenges associated with it.

Finally, with the increasing interest and application of the digital twin – the way in which a physical asset is digitally reproduced and managed throughout its lifecycle - there is an increasing need for effective materials information management and digitalisation for these technologies to be fully effective. Learn more about the Ansys Twin Builder here.

3. Granta MI™ Accepts the Challenge

The good news is that there exists a market-leading solution - Granta MI™ - which is a single and robust database to capture and create a single source of the organisation’s corporate material information. The open ecosystem architecture is capable of ensuring that this data is fully integrated with the organisation’s engineering and business tools (CAD, CAE and PLM) [5]. Additionally, users of Ansys Granta MI™ are able to manage the full lifecycle of the material data, all the way from the testing phase to the design phase. Finally, Granta MI™ supports analysis to derive accurate design data, and regulatory and environmental information is also integrated into the database.

The benefits of implementing a specialist system such as Granta MI are numerous:

  • Increased productivity as engineers can find information fast and effectively
  • Protection of vital materials IP
  • Cost savings and reduced times to market
  • Reduced risk, as quality issues and legal liabilities are minimised
  • Improved product performance due to material optimisation

Moreover, when it comes to AM, direct integration of Granta MI in CAD, CAE and PLM tools reduces the time to market of a product by up to 20%. Read more on how Granta MI™ supports AM here.

4. Granta Selector™ – Enabling Smart Material Choices

If you are not all about building your own material database, and are more interested in making material selection a whole lot easier for your business, then Granta Selector™ should be number one on your wish-list.

Ansys Granta Selector™ is a powerful material selection tool, which is based on the world-leading Ashby methodology for material selection [6]. It incorporates 25 years of development and refinement from its predecessor, CES Selector. It features world-leading materials reference data, that you can interpret by making use of the powerful built-in searching, comparison and visualisation tools. Vast databases of material and process properties form the foundation of Granta Selector™. The MaterialUniverse™ database is of particular value, containing over 4000 records of nearly any type of engineering material that may be purchased. Selecting a candidate material from thousands of possible options, while managing complex trade-offs between performance, cost, manufacturability, regulatory compliance and other technical aspects, becomes a simple task when done in Granta Selector.

There are also a number of advanced material data bundles that the Granta Selector™ core data may be supplemented with, giving users access to plenty of industry specific materials i.e., medical, aero-space and many others. Of particular interest is the Senvol Database™ which forms the foundation of the AM materials bundle.

The Senvol Database™ is the first of its kind, featuring a comprehensive database of AM machines and materials, allowing for materials and manufacturing technologies to be optimised for this niche industry [7]. The details of the machines include the manufacturer, price range, process, compatible material types and the build envelope size, while the details of the materials include material properties, information on similar conventional materials, the effect of the machine configuration and post-processing amongst many others [7].

5.  The Proof is in the Pudding

Don’t believe how easy it is? Let’s have a look shall we?

Consider the manufacturing of an impeller for a freshwater pump for which the current material – AISI 420 stainless steel - needs to be optimised. The end user of the pump impeller wishes to improve the current performance of the impeller to be able to handle higher loads in future generations. The requirements are given as:

  1. The new material should have maximum possible resistance to fast fracture and resistance to centrifugal loading. This will ensure that the material is not susceptible to rapid crack propagation around any possible material defects, and that it can handle higher loads and speeds, as required.
  2. The tensile strength of the new material must be more than 620 MPa.
  3. The elongation at break of the new material must be at least 10%.
  4. The new material must have excellent resistance against fresh water and wear.
  5. The impeller with a diameter of 250mm and width of 150mm should preferably be manufactured by AM technology

How can we use core data from the MaterialUniverse™ database and the Additive Manufacturing bundle in Granta Selector™ to study how this impeller could be improved and possibly manufactured using AM (Additive Manufacturing)?

Part 1 – Identifying the best conventional material for the application by using MaterialUniverse™ in Granta Selector™.

Subject to the above requirements and constraints, we start by selecting the most suitable conventional material for the impeller from the MaterialUniverse™ database, as durability and wear data are not available in the Senvol Database™. Once we have selected the best conventional material, we may determine whether there are any equivalent AM materials available and subsequently determine the best grade, process and machine.

In Granta Selector™, the “Performance Index Finder” enables you to plot performance indices or properties of materials against each other on a chart for a specific design situation, allowing for the materials with the highest values to easily be identified on the chart. When using the Performance Index Finder, you may select the applicable geometry and loading conditions from a list of options, and set the objectives to optimise specific properties. In this case, we would like to optimise the resistance to fast fracture and the resistance to centrifugal loading for the “Rotating Blade” design situation. The two properties are plotted against each other for all the materials contained within the MaterialUniverse™ database below.

To filter the materials further, we use the “Limit” stage to remove all the materials from the chart that do not fulfil the specific requirements that are given. We set the constraint for the minimum tensile strength as 620 MPa, and the minimum elongation as 10%, as provided. We also specify that the material needs to have excellent durability in a fresh water environment and excellent durability against adhesive wear, by selecting the applicable fields. By applying these constraints in the “Limit Stage”, the number of viable materials is reduced from more than 3000 to only 142.

Using the chart tools provided in Granta Selector™, the reference material may be highlighted and the best candidates may be annotated, as shown in the figure below.

From the plot it can be seen that the 17-4PH martensitic stainless steel ranges would be the best possible conventional material for the freshwater pump impeller.

Part 2 – Identifying the best AM material grade, process and machine for the application by using the Senvol Database™ in Granta Selector™.

In order to determine if there are any AM grades available for the materials, we use the “Tree”stage. The “Tree”stage in Granta Selector allows you to filter materials that are linked to any listed database, folder or record. In this case, we select “Additive manufactured materials” in the “Senvol Database” in order to filter the above materials that have equivalent AM counterparts. The materials in the above chart are further reduced from 142 to only 42, as shown below.

From the chart it can be seen that the 17-4PH martensitic stainless steel ranges do in fact have equivalent AM counterparts, allowing us to further our investigation in the Senvol Database™.

By selecting the Senvol Database™ and once again using the “Chart” stage, we may plot the ultimate tensile strength of the AM materials contained within the database against the elongation at break in order to easily identify the materials with the highest values on the chart.

 

In order to find an AM grade for the best identified conventional material - 17-4PH stainless steel - the “Limit” stage is used to specify the “Similar Conventional Material”, which reduces the number of candidate AM materials further, as indicated below. The coventional materials are depicted in green, while the AM grades are depicted in blue.

From the chart it can be seen that CT-PowderRange-174-AR-S is one of the best AM candidates, for which the process is indicated as “Powder Bed Fusion” on the material data sheet.

Finally, we would like to identify the most suitable machine that would be able to process the material, and that has a large enough build envelope to manufacture the component. Using the Senvol Database™ for machines and assuming that the customer would obviously also be concerned with the price of the machine, we use the “Chart” tool once again to plot the build envelope volumes of all the machines against their price ranges, as seen below.

Using the “Limit” stage, we filter the machines based on the material type, the AM process to be used as well as the provided build envelope dimensions, reducing the number of suitable machines from 1645 to 114 possible options. In order to further filter the machines that are able to process 17-4PH AM grades, we use the “Tree” stage - based on the known compatible materials that are are contained within the machine data sheets.

From the above plot it is noted that the Farsoon Technologies FS271M Series (500MW) is the best option, having the lowest price and the highest build envelope volume in its price class.

Within a few easy steps, the best conventional material, the AM grade, the AM process as well as the most suitable machine are selected for the optimisation of the freshwater pump impeller, subject to all the requirements given!

A detailed demonstration of the above workflow in Granta Selector™ is presented by Ansys in the below video [8].

Ansys Granta Workflow Demo Video

Please refer to https://www.qfinsoft.co.za/contact-us for our contact details if you would like to find out more about Ansys Granta MI™ and Granta Selector™.

 

Bibliography

[1]

A. F. Stephen Warde, The Business Case for Material Intelligence, ANSYS, Inc., 2020.

[2]

R. Kent, Quality Management in Plastics Processing, Elsevier Ltd., 2017.

[3]

S. C. Feldman S., The High Cost of Not Finding Information, IDC White Paper, 2001.

[4]

Woodhead Publishing, Fundamentals of Aluminium Metallurgy, Philadelphia: Woodhead Publishing, 2011.

[5]

Ansys Inc., “Ansys Granta MI,” 2022. [Online]. Available: https://www.ansys.com/resource-center/brochure/ansys-granta-mi. [Accessed 13 12 2021].

[6]

Ansys Inc, “Ansys Granta Selector,” 2022. [Online]. Available: https://www.ansys.com/resource-center/brochure/ansys-granta-selector. [Accessed 13 12 2021].

[7]

Ansys Granta, “Additive Manufacturing - the Senvol Database,” Ansys Granta, 2022. [Online]. Available: https://www.grantadesign.com/industry/products/data/applications/additiv.... [Accessed 6 January 2022].

[8]

ANSYS Inc, “ANSYS Release 2021R1,” 2021. [Online]. Available: www.ansys.com. [Accessed 15 December 2021].

 

 

Testimonials

  • I would like to hereby express my vote of confidence in Qfinsoft, in their ability to service and provide a strong support to us as a company.  We have been a customer of Qfinsoft for over a year and I am pleased to compliment them on their impeccable service offered to us thus far

    - Nico Gunther, Powertech Transformers

  • Since the introduction of ANSYS, more students are using first principles and FEA to validate their designs. This year, more than 60% of the projects were using FEA as a standard approach for numerical calculation. This number shows the huge impact ANSYS, had on University of Pretoria. FEA is not anymore a specialised tool for few users but a standard engineering tool easily integrated in the design process.

    - Francesco Pietra, University of Pretoria

  • For their consulting, training and technical support, Qfinsoft (Pty) Ltd must be the first choice of any firm looking for CAE experts

    - Prof G Akdogan, Process Engineering, University of Stellenbosch

  • At Aerospace, we have successfully used the ANSYS suite of tools to design high-performance gas turbine engines. Throughout this program, Qfinsoft provided valuable support and assistance to accelerate the development of our models. Their accessibility, short-turn around times and willingness to go beyond what is expected make them an effective partner on our projects. 

    - Dr. Hannes Pretorius

Our Clients