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3 Ways Industry 4.0 Will Change Engineering

May 20, 2019
How breakthroughs in additive manufacturing, new computational models and sensors are transforming the profession.

The most promising emerging technologies of Industry 4.0 are improvements in operational processes, computational workflows, and new design tools that help engineers in manufacturing see the future more clearly than ever before.

Additive manufacturing breakthroughs. Previously unimaginable structures can now be created, sometimes from completely new materials, taking AM (aka 3D printing) beyond mere prototyping to final-part production. Design-engineering tools for AM are more powerful than ever, allowing for part consolidation and product simplification alongside greater internal complexity. The ability to include internal lattices in AM structures is of particular benefit to aerospace and automotive lightweighting initiatives. In healthcare, personalized customization of implants and medical devices is dramatically improving patients’ lives. AM is empowering individual engineers to think far outside the box of what is possible in product design—and entire companies to consider build-where-you-sell scenarios that dramatically change the production and distribution landscape.

New computational models are being developed. Coupled with the rise of additive manufacturing is a demand for more sophisticated, agile computational modelling systems that can help manufacturers take advantage of new manufacturing capabilities that, for example, produce organic shapes and lightweight, high-strength lattice structures. With traditional CAD engineering, these highly complex surfaces, shapes and interior passages are difficult or impossible to capture, then alter and analyze. But a new generation of design platforms can create and validate thousands of options in near automation to arrive at the single design with the best cost and performance—ready for output to additive manufacturing work cells.

Digital Twin technology is at the apex of this computation model world. With a complete, digital representation of the geometry, constraints, performance, and manufacturing parameters of a physical asset—from an individual part to full assemblies—design and production can be optimized and in agreement with each other.

Using these computational models, engineers collaborating in different locations have access and transparency throughout the design and manufacturing process over the lifecycle of a product. Think about it: if you’re making satellites, where each finished product might represent tens of millions of dollars of investment, you need to have a 100% success rate. Traditional ways of modeling new product designs make it too easy to break the models because they don’t incorporate the full complexity of the multiphysics involved; new Digital Twins are more robust, and enable designers to help maximize the manufacturer’s capabilities—and ability to innovate.   

Connecting analog tools and data structures. The Industrial Internet of Things (IIot) makes it possible to integrate sensors into many manufacturing processes, as well as some key aspects of the supply chain, better informing design from the earliest stages. Manufacturers are transitioning analog processes to digital, opening up huge potential for improving the way they do business.

With integrated sensors and the Smart Factory of the future, manufacturing companies and their engineering design teams will have better visibility and control into the entire process of designing and making parts and products. They will have data-informed decision-making power and be able to integrate more of the analog operational aspects—which used to happen on spreadsheets and on paper—into a digital model.

On the manufacturing side, there are still a lot of manual processes that need to happen to get a build running. New tools and data structures are being developed to provide transparency and connectedness in every part of the manufacturing process. 

Brad Rothenberg is the founder and CEO of nTopology, a company based in New York City that is developing algorithmic computer-aided engineering/design software for advanced manufacturing. Brad is a graduate of the Pratt Institute in Brooklyn, New York.

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