Industrial Engineering

Manufacturing in 2050: The World Turned Upside Down?

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Manufacturing Engineering Magazine December 2018
By Ed Sinkora

Powerful trends will push manufacturing close to complete automation by 2050, while the people still working in the industry will be empowered to rapidly innovate like never before.

Multitasking and Automation

By 2050, the average machine tool will be fully automated and more capable. Multitasking will be common and perhaps nearly universal. The trend is well established.

In the early 2000s, the North American market for five-axis machines was 150. Today it’s 3,000. Machines are also combining grinding and milling, or laser metal deposition and milling, or grinding and work hardening. While the benefit is the ability to accomplish more inside the work envelope, the “nightmare” has been getting all these functions to operate properly and consistently. But that will change as technology, monitoring and software all get better.

The elephant in the room for many manufacturers is the extent to which 3D printing will alter the technology mix, and beyond that, its implications for product design and a host of other issues. So far, the speed limitations and high raw material costs of additive manufacturing have severely limited its viability beyond prototyping. But a principal consultant and president of Wohlers Associates, Terry Wohlers, said that speed won’t be an enemy by 2050.

Take a powder bed system: The bulk of production time is in tracing the surface with the laser to fuse the material, but systems are now available with many lasers working simultaneously on a build platform. Wohlers said that the energy from an electron beam can be split into as many as 100 beams to help speed the process. On the other hand, these approaches require a lot of energy, which is expensive. Wohlers thinks we will overcome those limitations by “harnessing the energy of the sun directly to melt material, rather than plugging into a 440 outlet”.

Wohlers added that directed energy deposition is inherently faster than the powder bed method for building metallic components, but “users are limited in the objects they can create and there is a trade-off in resolution, generally requiring machining, and sometimes a significant amount”. This brings us back to hybrid systems that combine additive with CNC milling. Wohlers believes the problems in getting these two approaches to work harmoniously will largely be solved in the next 32 years.

Another factor arguing for greater use of additive techniques is an expected drop in material costs and a wider array to choose from. Today’s machines work with only a few dozen thermoplastics, yet thousands are available for conventional manufacturing. Perhaps more important, the polymers currently used in 3D printing cost up to 50 times as much as similar polymers for conventional manufacturing. That puts the breakeven point in the hundreds to thousands of units depending on the size of the part. But Wohlers said many of the patents on machines that produce parts with polymers have expired, leading to new machines that use lower-cost materials. The breakeven point will improve dramatically so that additive will challenge injection molding for a much wider range of products, including higher volume applications.

Additive manufacturing does have one ace left, at least for some players. The ability to create forms that would otherwise be impossible. This not only opens up the potential for new products and features, it also helps alleviate 3D printing’s speed problem and also achieved the required strength and stiffness needed for many applications with far less material than a solid structure.

Automated, Creative Design

Manufacturing is poised to achieve its biggest productivity gains in two areas, one of which is in digitizing all the work that’s necessary to prepare a manufacturing process. Today a designer starts out with a digital model and at the end, an application engineer drip feeds the program to the machine and goes tool by tool and monitors how the cut sounds and how it looks and eventually he gets the machine to make the part. Engineer also refines all the motions so he can reduce the cycle time.

At the front end, generative design technology is helping an ever wider group of creatives to quickly explore new geometric possibilities. In the case of Fashion 360 from Autodesk (San Rafael, CA), the software runs on the cloud and uses machine learning and artificial intelligence (AI) to automatically generate hundreds of designs that each satisfy the designer’s criteria for strength, cost, manufacturing method, material and so forth. What’s more, explained Bob Yancey, Autodesk’s director of manufacturing and production strategy, the designs are “not just some impossible-to-use idealized geometry, they are real working CAD models that can be further manipulated in CAD software.”

They are also what Yancey refers to as “manufacturing aware,” which means they started with the desired manufacturing methods built in as a constraint from the beginning. So if you specify that the part needs to be able to be machined on a five-axis CNC, all of your design options will conform to that constraint. It doesn’t eliminate the need for a human designer, but it challenge the design with precision and expertise of an engineering skill that will not go away. What generative design software does is give you more design options than any human could conceive on their own, so you have greater confidence that you are considering far more options and getting better outcomes. We see this as a future of co-creation between engineer and computer, or human intelligence and artificial intelligence.

Speeding the Process

Krisztina “Z” Holly, founder and chief instigator of Make It In LA (Los Angeles) underlined the benefit of combining ever smarter software with 3D printing and other new technologies (like virtual reality) to greatly speed the iterative product development cycle. Besides the ability to get more feedback from the consumer earlier in the process, which may lead to much better products, she pointed out that the new tools democratize the design and build process.

The world will be a different place if we allow non-engineers to design the kinds of products they want and it will also be easier for entrepreneurs to start a manufacturing business. What kinds of products will become available if people can start a physical products business as easily as they can a digital products business.

One outcome she envisions: “Two sets of skills become incredibly valuable. One is in-depth technological skills for coding these systems and understanding the nitty gritty of what works and what doesn’t.” The other is empathetic understanding of the needs of the customer and market opportunities.

What skills become less critical? Actually running the machines. The process from design to CAM will be more or less automated. If the design model included information about the material (as is becoming standard), the machine should have the intelligence to do the rest. Take the right tools out of an 8,000 tool rack and follow the correct tool paths at the appropriate speeds. The machine should have the vision and audible monitoring capabilities to avoid collisions and also gauge the cutting conditions and adjust speeds and feeds accordingly. That’s what applications engineers do today.

Built to Order…Locally

Everyone seems to agree that manufacturing will become much more geographically dispersed, a process greatly aided by the multiplying capabilities of individual machine tools. Yancey said many manufacturers want to both decrease risk and make products closer to the customer to better adapt them to the market.

Transportation costs and environmental impacts will drive companies to produce locally. And there are also government mandated offsets, in which a manufacturer must produce a certain number of parts in a country in order to sell products in that country. Another motivator is maintaining profitability despite fluctuations in currencies, a problem exacerbated by tightening margins.

At the same time, there will be a much greater degree of customization and a much tighter supply chain. The practice of forecasting demand and mass producing parts to meet expected demand will be turned on its head. It will be closer to the customer telling the manufacturer exactly what they want and the manufacturer making it then and only then. Digitization and automation are making this dream more technically, and even economically, feasible.

Keeping Things Humming

Increased digitization, “hyper-connectivity,” and AI should greatly improve our ability to keep production running with a minimum of manpower and downtime. Most of the data now being collected is used to monitor what’s going on in the factory and throughout the supply chain. The next phase in this process is using AI and machine learning to enable autonomous responses.

In other words, with enough data to analyze, machine learning can accurately predict specific part failures. With good decision algorithms and knowledge about all the production demands on the shop floor, the system can also decide for itself what to do about the pending failure: order the part, schedule the downtime, move certain jobs to alternate machines, and so on. You could even envision a machine fixing itself or ordering the robot that can, though Walker said he doesn’t think we’ll ever get away from the need for human maintenance technicians. However, he does think the machines will communicate audibly about what needs to be done—no need for handheld devices or screens and controls.

Microsoft has a head-mounted product (HoloLens) that allows you to interact with holograms around you. It overlays digital info on top of reality, giving you super powers, in a sense. People are finding that augmented reality can be used to do things like provide assembly instructions, or QC instructions, or maintenance instructions, thus reducing the need for training. For example, a remote maintenance expert can assist a local technician by pointing to a part or indicating how to move a part, as if they are both looking at the same thing in the same shop together.

One of the beauties of machine learning is that the moment they get better, that capability or knowledge can be instantaneously broadcast to the entire world, because it’s just software. So everybody gets smarter and better, assuming we can share data.

What Won’t Change

To the extent it came up at all, the experts don’t seem to think that manufacturing precision will advance much in the next 30 years since they’re working at tolerances now where the metrology to determine the accuracy is the bigger problem. The next step to getting better tolerances would be molecular manipulation which no one envisioned. No one seemed to think that machining speed would be significantly faster either. Even the improvement to 3D printing speeds discussed earlier will be more evolutionary than revolutionary—not as significant as the increase in productivity due to software improvements. Likewise, our current ability to produce tiny components is already amazing.

If you’re worried about the changes, this thought might comfort you: “We’ve been tweaking manufacturing since the 1780s”. The next 30 years will be more tweaking, unless we come up with something truly revolutionary. If anyone said they’d figured out how to manipulate gravity so we could fly to the moon without burning fossil fuels, knowing the answer. Come to think of it, no one mentioned the dog who kept the man from making changes to the machine, either.