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How Generative Design Can Make Your Product More Sustainable (and Help Your Company Make More Money)

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Generative Design for Lightweighting – Fusion 360

Using the generative design capabilities in Autodesk Fusion 360, you can quickly generate high-performing design alternatives—many that you’d never think of on your own—from a single idea or pre-existing product. In this article, we explore how a generatively designed part can outperform a traditional design in terms of functionality, manufacturing supply chain, and environmental impact.

What Is Generative Design?

Generative design mimics nature’s evolutionary approach to design. Designers or engineers input design goals into generative design software, along with parameters such as materials, manufacturing methods, and cost constraints. The software explores all the possible permutations of a solution, quickly generating multiple design alternatives. It tests and learns from each iteration what works and what doesn’t.

Lightweighting with Generative Design

Generative design is particularly helpful when seeking to improve the material efficiency of a design while maintaining performance parameters and functionality requirements. By nature, the software removes material where it isn’t needed, and organically restructures material based on stress and strain patterns. As a result, a generatively designed part can achieve the same functionality with as high as an 80% reduction in material!

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Figure 1. The original end-effector part (left) and the generatively designed part (right) in context.

In this article, we explore the design of a robot welder assembly subcomponent referred to as the “end-effector.” You can already see that the original part is bulky, heavy, and requires assembly (three sub-components and six fasteners). By inputting the functional and physical requirements of the original end-effector part into the generative design space in Fusion 360, we obtain a new, optimized part which requires no assembly and uses 77% less material!

Manufacturing of Lightweight AGD Parts

Now that we have our improved, lightweight model, we had to determine how to make it. Due to the complex, organic nature of generatively designed parts, traditional subtractive manufacturing is often not feasible. However, additive manufacturing (methods allow us to build these parts with minimal process steps - simplifying supply chain, inventory, and assembly logistics.

3D Printing for Generative Design

Additive manufacturing (AM), also known as 3D printing, allows for the efficient fabrication of highly complex and innovative designs that are often not easily manufactured with traditional methods. We explore two 3D printing methods to manufacture our improved end-effector part based on two potential material choices: steel and polycarbonate. To print in steel, we use powder bed fusion (PBF), an AM method where a laser fuses metallic powder layer by layer. To print in polycarbonate, we use fused deposition modeling (FDM), where a continuous filament of material resin is fed through a mobile nozzle onto the developing workpiece.

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Figure 2. Powder Bed Fusion (PBF) and Fused Deposition Modeling (FDM) technologies.

Autodesk Netfabb – 3D Printing Optimization

Autodesk Netfabb additive manufacturing software has tools that help streamline your workflow and quickly get from a 3D model to successfully printed parts. In particular, Netfabb allows you to optimize the required use of support structure, as well as feeds, speeds, orientations, bed arrangements and more to minimize the material use, energy use and environmental impact of the process itself.

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Lightweighting and Generative Design for Sustainability

To adequately understand the environmental impact factors of the part and process update, we perform a cradle-to-grave life cycle assessment (LCA) on the part. This analysis will confirm whether or not our updated part carries a lower embodied energy and carbon footprint as a result of the material reduction and material efficiency of additive manufacturing processes.

Life Cycle Assessment

A cradle-grave LCA takes into consideration the environmental impact of all steps of the manufacturing and use process including raw material processing, manufacturing, use and end-of-life. Performing such an analysis on our lightweight end-effector part provides us with a holistic understanding of the environmental impact embodied by the part, and how it compares to the original part and process. In this study, we compare the original part produced by subtractive machining and hand assembly, with a generatively designed version in both steel using PBF and polycarbonate using FDM. We analyze these parts and processes through the impact categories of Global Warming Potential (GWP) and Cycle Energy Demand (CED), using LCI data from Worldsteel as well as the sources below.

Results

Our study confirms that the generatively designed part fabricated with PBF carries a significantly lower embodied GWP and CED than the original part fabricated with subtractive methods. This is primarily due to the significant reduction in mass of the part, as well as the high material efficiency of the AM process.

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Figure 4. The total life-cycle Global Warming Potential in KgCO2 for each part and process.

A breakdown of the total GWP results is shown below. Although the energy required in the manufacturing process itself is similar or more for AM (Figure 6), it is significantly outweighed by the embodied energy of the input material required for the traditional process and part (Figure 5). Additionally, the recycling of more material does increase the saved carbon emissions from avoided virgin material production (Figure 8.), but is not enough to compensate for the overall excessive material usage in the traditional process. The magnitude of energy required in the end of life process itself is relatively insignificant in comparison to the embodied energy in input material (Figure 7).

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Figures 5 - 8. A breakdown of individual analyses within the LCA.
Results are added to obtain the total GWP.

Cost Analysis

Manufacturing

Assuming low-volume production, we analyze the cost of producing one end-effector by taking into consideration input material cost, operator compensation, and electricity usage of machinery. Results indicate that, despite material reduction, an increase in cost is associated with manufacturing the updated part via additive processes (Figure 7). This is primarily due to the relatively high cost of processed input material for additive manufacturing, as well as the additional post-processing requirements of additive manufacturing.

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Figure 7. Primary manufacturing costs including processed input material and machine electricity costs.

Operator Labor Cost (Time Value)

However, due to the simplified manufacturing process, less hands-on operator time is required for AM. For the traditional process, the operator has to perform a setup, calibration, and post processing for three individual components, as well final assembly of the components. They must then repeat each step for each individual part. In the updated process, the operator only sets up once and monitors occasionally until post-processing. In the case of PBF, parts are typically produced in small batches, therefore only one set-up and post-process is required for the entire batch. The saved time value in manufacturing logistics can compensate for the increased cost of materials.

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Figure 8. Operator time value per process. Simplified AM processes lead to decreased hands-on operator costs.

Conclusion

Generative design capabilities can help make products more sustainable by offering innovative design solutions that use less material, consolidate parts, and increase performance. Coupling these solutions with additive manufacturing technologies simplifies fabrication processes and ultimately reduces the impact your product has on the environment. Certain elements of this process can be more expensive. However, it can be integrated in such a way that optimizes supply chain and labor logistics, which ultimately can compensate for other increased costs.

Zoe Bezpalko is building her career at the intersection of sustainability, technology, and design. With a master’s degree in Environmental Engineering and an MBA in Design Strategy, she has international experience in helping organizations become sustainable, through innovative design solutions and business models. At Autodesk, she now leads sustainability strategy for the Design & Manufacturing industries, identifying technology to maximize value for Autodesk, its customers, and the planet.

References

Ashby, M. F., 2013: Materials and the Environment – Eco‐Informed Material Choice , 2nd ed., Butterworth‐Heinemann.

Baumers, M. et. al. (2010). A COMPARATIVE STUDY OF METALLIC ADDITIVE MANUFACTURING POWER CONSUMPTION, 278-288. Retrieved October 29, 2018, from http://sffsymposium.engr.utexas.edu/Manuscripts/2010/2010-25-Baumers.pdf.

Baumers, M. (2014). Raw Material Pricing and Additive Manufacturing. Retrieved October 25, 2018, from http://www.3dprintingmaterialsconference.com/wp-content/uploads/2014/06/MB_Raw_Materials_3.pdf.

Dudek P. et. al. Energy and Fuels. (2010). Cost, Resources and Energy Efficiency of Additive Manufacturing,1-8. Retrieved November 1, 2018, from https://www.e3s-conferences.org/articles/e3sconf/pdf/2017/02/e3sconf_ef2017_01040.pdf.

Damgaard, A., Larsen, A. W., & Christensen, T. H. (2009). Recycling of metals: accounting of greenhouse gases and global warming contributions. Waste Management and Research, 27(8), 773-780. DOI:10.1177/0734242X09346838.

Gutowski, T. et. al. (2017). Note on the Rate and Energy Efficiency Limits for Additive Manufacturing. Journal of Industrial Ecology,21(S1). doi:10.1111/jiec.12664.

Huang, R. (2016). Energy and Emissions Saving Potential of Additive Manufacturing: The Case of Lightweight Aircraft Components. Retrieved November 1, 2018, from https://www.sciencedirect.com/science/article/pii/S0959652615004849.

Karvinen, H. (n.d.). Life Cycle Assessment and Technical Performance of Recycled and Bio-ased Plastics. Retrieved November 3, 2018, from https://aaltodoc.aalto.fi/handle/123456789/15997.

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Nyamekye, P., Piili, H., Leino, M., & Salminen, A. (2017). Preliminary Investigation on Life Cycle Inventory of Powder Bed Fusion of Stainless Steel. Physics Procedia,89, 108-121. doi:10.1016/j.phpro.2017.08.017

Thomas, D. S., & Gilbert, S. W. (2014). Costs and Cost Effectiveness of Additive Manufacturing. DOI:10.6028/nist.sp.1176.

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We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
Trendkite
We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
Hotjar
We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
6 Sense
We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
Terminus
We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
StackAdapt
We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
The Trade Desk
We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

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We can access your data only if you select "yes" for the categories on the previous screen. This lets us tailor our marketing so that it's more relevant for you. You can change your settings at any time by visiting our privacy statement

Your experience. Your choice.

We care about your privacy. The data we collect helps us understand how you use our products, what information you might be interested in, and what we can improve to make your engagement with Autodesk more rewarding.

May we collect and use your data to tailor your experience?

Explore the benefits of a customized experience by managing your privacy settings for this site or visit our Privacy Statement to learn more about your options.