Additive manufacturing (AM) and 3D printing have proven to be extremely capable technologies for creating some of the most innovative products on the market. But even the most innovative idea is only successful if it can be made profitably.
Making 3D printing successful across industry requires investment. Achieving the potential of AM reliably, profitably, and sustainably isn’t easy. Knowledge and best practices for AM need to be integrated from early design work through the build and production of the product. Furthermore, companies must also account for material circularity a the end of a product’s life.
Digitalization is a key enabler in creating a viable AM program to collect, analyze, and optimize the process. Design, production, and product usage data must be collected over the lifetime of a product. The right design toolset helps create the perfect part. The right build setup toolset can optimize the manufacturing process. And finally, the right production toolset ensures the parts emerge reliably, profitably, and sustainably. These digital toolsets exist today, and they are increasingly taking AM into the mainstream.
There are three major functions that make an AM design process successful for a business. These are designing for function, for performance, and for manufacturability. Designing AM parts as one might for more traditional manufacturing methods is ineffective—while the end goal is the same, the path to success is different.
Much of the benefit of additive comes from designing properly for the technology, being that the complex geometries are created through topology optimization or through algorithmically defined lattice structures. It is possible, with AM, to deliver more performance with less material if the design is right. But designing this way requires a digital toolset that can manage and manipulate the complex geometries that emerge from optimizing parts for performance, reliability, and other key metrics.
The performance gains enabled by these design improvements can also create sustainability advantages for a company by reducing material usage per part, improving part performance in the field, or aiding downstream processes for manufacturing or assembly. For example, lighter components in a vehicle might improve range, whereas hydrodynamically efficient pump-impeller geometry may reduce the energy costs for machinery.
Sometimes, these advantages can be pushed even further into manufacturing. For example, some companies have used AM to consolidate several individual parts into one, reducing assembly steps and thereby reducing energy usage. Over the past year we have seen many companies we work with at Siemens begin to explore AM for such reasons.
An optimized—or even theoretically perfect—design is nearly worthless if it can’t be manufactured efficiently, reliably, and profitably. Ensuring a proper build requires information from across the lifecycle of the product, making an integrated and digital toolset a key part of a successful AM program. A manufacturing engineer might optimize the production of a part with build and performance simulations to ensure machine efficiency during a print.
There is also a critical need for build preparation optimization in creating sustainable AM products. For example, optimizing the print capacity of a given printer might also result in minimizing the energy expended per print.
When delivering products in lot sizes approaching single digits, a failed print can be exceedingly expensive in the cost of material, energy, and time associated with the build. Using the right digital toolset can aid with generating a quality output; as companies move toward a stronger emphasis on sustainability, these savings become increasingly important.
At Siemens, we have watched as sustainability has become a driving force in corporate decision making and as companies have turned to AM to aid in that goal.
It is rare to find a part made with additive techniques that is production quality exiting the printer. Much like casted parts, there is a need to clean and precision machine parts for final installation, especially when they’re printed in metal.
Some of this work is mitigated through an understanding and subsequent optimization of the printing process being used. A data-reinforced model of the printing process between design, production, and real-world performance provides more accurate predictions in earlier stages of development that can lead to a reduction in build failures and material usage. Furthermore, the right digital toolset for production should also integrate post-processing operations into the optimization of the workflow.
Alongside the need to optimize the print time, material usage, and viability is a growing need to account for material across the lifecycle of a product, including post processing. Depending on the business, this can span from raw material sourcing all the way to end-of-life disposal of the material. Material traceability provides insight into points of loss in the lifecycle of a product, which has a direct impact on the costs and sustainability of manufacturing.
For example, historical data might uncover that one depowdering machine is not collecting unused material effectively or it may prompt designers to reshape the part slightly to improve removal with the available equipment. As the companies we work with expand their knowledge of the entire AM process, these are the types of considerations we are seeing become more prevalent to help plan and execute AM workflows.
Collecting, analyzing, and optimizing data across the production lifecycle will be crucial as more businesses shift toward creating sustainable processes and material circularity. Material loss has a direct impact on costs to manufacturers, but as some resources become harder or more expensive to extract in raw forms, the cost benefit of efficient material use will only grow. This can be the printing material, but it also applies to post processing coolants and machining fluids.
The “three R’s” of sustainability (reduce, reuse, recycle) are often mentioned when discussing a product’s lifecycle. Reuse is a topic that we have seen arise increasingly with regard to sustainability and AM. Part of creating a sustainable future is creating products that no longer have a planned obsolescence but are instead repaired and reused. Using AM to repair or replace parts on legacy machinery is the type of use case we see companies increasingly investigate as the desire for a less wasteful future combines with corporate initiatives around creating sustainable products.
While industry leaders, researchers, and entire supply chain networks are creating this sustainable future, it is clear that software will play a strong role by enabling tracking and prediction of product sustainability from early concept through end of life. Having a wealth of information readily available in a digital environment also provides the tools necessary to make processes more profitable while they are made more sustainable. A gram of material or kilowatt saved in manufacturing lowers the costs associated with the manufacturing of a product, and reduces the environmental impact.
AM offers opportunities for advancing the performance of products. This has been proven time and again by the amazing work of the companies we work with every day at Siemens. But AM also provides a pathway for advancement in the performance and sustainability of manufacturing. This starts with the design, build, and production of today’s products and will continue with the circularity of products across the complete lifecycle tomorrow.
Software tools are key to realizing this potential. They make the additive process a profitable, repeatable, and sustainable one that can drive manufacturing into the future. We have been excited by what companies have done over the past year to help build this future, and we look forward to helping them continue pursuing these goals in the coming years.
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