Introduction: Artificial Intelligence the self-designing machine
Artificial intelligence the self-designing machine. Manufacturing is in the midst of a disruption brought on by technologies like artificial intelligence (AI) and 3D printing. “Additive manufacturing” has already worked itself into companies such as Porsche and Bugatti, and aircraft builder Airbus is experimenting with UAV THOR, a drone made entirely of 3D-printed parts.
On the other hand, AI is also coming into play in a variety of ways these days, ranging from analytics to manufacturing robotics. Hence, in order to get to the “factory of the future” which is envisaged by initiatives like Defense Advanced Research Projects Agency (DARPA)’s Adaptive Vehicle Make, the manufacturing process must be controlled by software, and the factory must be able to rapidly reconfigure itself to produce new products.
As AI has become more and more common in product design, with the use of generative design software, it continues to gain importance. Using AI-driven generative design software, humans and machines can work together to rapidly consider all the possible design options available and to test them all before selecting the one that is to be produced.
“In an AI-driven generative design paradigm, humans input design goals and material parameters,” explains Avi Reichental, the CEO and founder of XponentialWorks (a venture investment, corporate advisory, and product development company specializing in artificial intelligence, 3D printing, robotics, and digital transformation). “The software does the rest—exploring nearly infinite design permutations based on existing design concepts. This includes designs that are stronger, lighter and use less material than would be used otherwise to save money, increase scalability, and raise efficiency while enhancing form and function.”
During this increasingly connected manufacturing process, the form and features of a product need not even be completed before shipping. With a little Internet access, products themselves are taking part in improving their own design long after they’ve left the factory. As a result, they can “phone home” changes to their own design to increase efficiency or overcome unforeseen challenges.
As a result, human designers have seen their role begin to change: they are becoming co-designers of a product for its entire lifetime.
Manufacturing outside factory walls
AI devices were given the freedom to self-design, and eventually co-design, on the fly because no one told them their work had to be confined to an industrial setting. Design evolution became enticing without such a rule as taking manufacturing on the road became possible.
With modern generative design, efficiency, sustainability, and resilience are continuously improved at ever-increasing speeds. Any combination of artificial intelligence, machine learning, edge computing, cloud computing, and additive manufacturing (3D printing) can be used to develop a multitude of manufacturing design alternatives quickly. As a result of these technologies, things can also be able to execute changes to their own design even when they are far away from their creators’ facilities.
“For example, a robot on Mars might detect very loose sand and determine it cannot move about efficiently to complete its mission,” explains Ben Schrauwen, co-founder and CTO of Oqton, an autonomous manufacturing platform. “The robot could learn to suggest different modalities on how to move in that environment, and, with 3D printing technology and some local robotics, it’s very conceivable that the robot could reconfigure itself at a distance to continue its mission unimpeded.”
Aside from space travel and space missions, there is a lot of motivation to allow things to co-design or self-design here on Earth, too. Artificial Intelligence the self-designing machine is disrupting manufacturing in real time. This is a major disruption and we need to be prepared for this transformation.
“The most exciting change is the embedding of sensors within manufactured items to create a design system that is a self-improving circuit, where the sensors provide feedback to the design to cause it to respond and improve,” said Tod Northman, partner at Tucker Ellis, a law firm with a specialized practice in intellectual property and liability issues concerning autonomous vehicles and other artificially intelligent devices. “Such a system will become a self-improving loop, with better products resulting without human intervention.”
Occasionally, the self-improving loop will add to the design of the thing to upgrade its functionality and features. There are times, however, when it suggests a design change that will affect a real-time repair. The design of an AI-driven thing would be able to adapt to unforeseen circumstances, such as a robot breaking a leg or a vehicle blowing a tire or breaking an axle, and continue to work.
The day is coming when manufacturing will become autonomous-a time when everyone can manufacture anything on a home 3D printer or a regional 3D printer by selecting base models from a store and adding customizations to them as they wish. The AI will soon allow customers to provide a general description of the design they want or the functions of a product that they would like to have manufactured. After this has been completed, the AI will then take care of everything else, including the selection of materials, the activation of supply chains, as well as the optimization of design and manufacturing.
When you’re presented with a series of things that all meet your needs or desires, it’s only natural to develop some preferences among them. It is with respect to preference that branding is important, in order to be able to locate the preferred made easily in the future. This may be why the AI-future for manufacturing may not look so different: “AIs could develop their own brands and fan bases,” as Schrauwen puts it.
AI must also analyze the tools in order to innovate or invent according to them in order to build the most efficiently. A variety of raw materials are traditionally cut, stamped, or pressed into shapes by manufacturers. Substractive manufacturing is the process of cutting, stamping, or pressing raw materials into shapes. Currently, additive manufacturing, or 3D printing, utilizes layers upon layers of specialized inks to create a 3D object faster, with less waste, and with greater precision.
As well as the advantages, the disadvantages include poorer surface quality. As a result, many manufacturers today prefer to use both methods, combining their strengths in hybrid manufacturing processes.
Among the initial industry shifts driven by AI, companies are increasingly moving towards additive manufacturing.
“Generative design is the killer app for 3D printing/additive manufacturing,” said Richard D’Aveni, a 3D printing and generative design expert at Dartmouth. “For two reasons: first, many companies are slow to adopt additive due to resistance from veteran engineers and designers who don’t want to learn a new manufacturing approach all over again. Second, generative design promises a substantial boost in product performance and quality. Once product developers realize its power, they’ll insist on working with it—and will insist that their companies shift to additive to realize the full potential of generative design.”
Due to its versatility, efficiency, and lack of waste, AI-based generative design drives 3D printing adoption and improvement.
“Additive manufacturing has the potential to decentralize manufacturing because it’s basically a single machine with a single supply of material that can create an infinite stream of different parts,” explained Schrauwen. “It really doesn’t matter what they are. You don’t need to make molds, there’s no upfront investment to produce a new type of part. Many machines, spread across the world, can make different parts all day long.”
Interesting enough, despite all the talk about machine-driven disruption in manufacturing, it wasn’t technology that sparked this moment. The maker movement initiated it.
Despite the maker movement’s demonstration that anyone can create something, creativity in design remains an uncommon skill among humans, and nonexistent among machines. As manufacturing becomes more tech-driven, the demand for designers will increase. These new technologies may, in fact, create jobs within these communities, contrary to popular belief.
Some people, however, do not think AI and distributed manufacturing will benefit little guys like this. Dartmouth’s D’Aveni agrees that AI-based generative design will promote additive manufacturing (which will in turn lead to a distributed manufacturing model closer to customers), but that’s it. The AI-enhanced software digitally optimizes production and supply chains, he said, so it is likely to encourage the creation of software platforms to coordinate and optimize manufacturing in general. D’Aveni concludes that this will result in a landscape dominated by giant pan-industrial corporations and networks rather than small businesses.
Conclusion: Artificial Intelligence the self-designing machine
Artificial Intelligence the self-designing machine: Whoever benefits the most from AI, it is clear it will change manufacturing in every aspect, from design to production to business practices to tools themselves. As those changes slowly begin to mainstream in the near future, one thing is certain: your next co-designer may well be a self-designing machine with a few good ideas of its own. All you need is creativity.
Introduction: Artificial Intelligence the self-designing machine
Artificial intelligence the self-designing machine. Manufacturing is in the midst of a disruption brought on by technologies like artificial intelligence (AI) and 3D printing. “Additive manufacturing” has already worked itself into companies such as Porsche and Bugatti, and aircraft builder Airbus is experimenting with UAV THOR, a drone made entirely of 3D-printed parts.
On the other hand, AI is also coming into play in a variety of ways these days, ranging from analytics to manufacturing robotics. Hence, in order to get to the “factory of the future” which is envisaged by initiatives like Defense Advanced Research Projects Agency (DARPA)’s Adaptive Vehicle Make, the manufacturing process must be controlled by software, and the factory must be able to rapidly reconfigure itself to produce new products.
As AI has become more and more common in product design, with the use of generative design software, it continues to gain importance. Using AI-driven generative design software, humans and machines can work together to rapidly consider all the possible design options available and to test them all before selecting the one that is to be produced.
“In an AI-driven generative design paradigm, humans input design goals and material parameters,” explains Avi Reichental, the CEO and founder of XponentialWorks (a venture investment, corporate advisory, and product development company specializing in artificial intelligence, 3D printing, robotics, and digital transformation). “The software does the rest—exploring nearly infinite design permutations based on existing design concepts. This includes designs that are stronger, lighter and use less material than would be used otherwise to save money, increase scalability, and raise efficiency while enhancing form and function.”
During this increasingly connected manufacturing process, the form and features of a product need not even be completed before shipping. With a little Internet access, products themselves are taking part in improving their own design long after they’ve left the factory. As a result, they can “phone home” changes to their own design to increase efficiency or overcome unforeseen challenges.
As a result, human designers have seen their role begin to change: they are becoming co-designers of a product for its entire lifetime.
Manufacturing outside factory walls
AI devices were given the freedom to self-design, and eventually co-design, on the fly because no one told them their work had to be confined to an industrial setting. Design evolution became enticing without such a rule as taking manufacturing on the road became possible.
With modern generative design, efficiency, sustainability, and resilience are continuously improved at ever-increasing speeds. Any combination of artificial intelligence, machine learning, edge computing, cloud computing, and additive manufacturing (3D printing) can be used to develop a multitude of manufacturing design alternatives quickly. As a result of these technologies, things can also be able to execute changes to their own design even when they are far away from their creators’ facilities.
“For example, a robot on Mars might detect very loose sand and determine it cannot move about efficiently to complete its mission,” explains Ben Schrauwen, co-founder and CTO of Oqton, an autonomous manufacturing platform. “The robot could learn to suggest different modalities on how to move in that environment, and, with 3D printing technology and some local robotics, it’s very conceivable that the robot could reconfigure itself at a distance to continue its mission unimpeded.”
Aside from space travel and space missions, there is a lot of motivation to allow things to co-design or self-design here on Earth, too. Artificial Intelligence the self-designing machine is disrupting manufacturing in real time. This is a major disruption and we need to be prepared for this transformation.
“The most exciting change is the embedding of sensors within manufactured items to create a design system that is a self-improving circuit, where the sensors provide feedback to the design to cause it to respond and improve,” said Tod Northman, partner at Tucker Ellis, a law firm with a specialized practice in intellectual property and liability issues concerning autonomous vehicles and other artificially intelligent devices. “Such a system will become a self-improving loop, with better products resulting without human intervention.”
Occasionally, the self-improving loop will add to the design of the thing to upgrade its functionality and features. There are times, however, when it suggests a design change that will affect a real-time repair. The design of an AI-driven thing would be able to adapt to unforeseen circumstances, such as a robot breaking a leg or a vehicle blowing a tire or breaking an axle, and continue to work.
The day is coming when manufacturing will become autonomous-a time when everyone can manufacture anything on a home 3D printer or a regional 3D printer by selecting base models from a store and adding customizations to them as they wish. The AI will soon allow customers to provide a general description of the design they want or the functions of a product that they would like to have manufactured. After this has been completed, the AI will then take care of everything else, including the selection of materials, the activation of supply chains, as well as the optimization of design and manufacturing.
When you’re presented with a series of things that all meet your needs or desires, it’s only natural to develop some preferences among them. It is with respect to preference that branding is important, in order to be able to locate the preferred made easily in the future. This may be why the AI-future for manufacturing may not look so different: “AIs could develop their own brands and fan bases,” as Schrauwen puts it.
AI’s effect on tooling design
AI must also analyze the tools in order to innovate or invent according to them in order to build the most efficiently. A variety of raw materials are traditionally cut, stamped, or pressed into shapes by manufacturers. Substractive manufacturing is the process of cutting, stamping, or pressing raw materials into shapes. Currently, additive manufacturing, or 3D printing, utilizes layers upon layers of specialized inks to create a 3D object faster, with less waste, and with greater precision.
As well as the advantages, the disadvantages include poorer surface quality. As a result, many manufacturers today prefer to use both methods, combining their strengths in hybrid manufacturing processes.
Among the initial industry shifts driven by AI, companies are increasingly moving towards additive manufacturing.
“Generative design is the killer app for 3D printing/additive manufacturing,” said Richard D’Aveni, a 3D printing and generative design expert at Dartmouth. “For two reasons: first, many companies are slow to adopt additive due to resistance from veteran engineers and designers who don’t want to learn a new manufacturing approach all over again. Second, generative design promises a substantial boost in product performance and quality. Once product developers realize its power, they’ll insist on working with it—and will insist that their companies shift to additive to realize the full potential of generative design.”
Due to its versatility, efficiency, and lack of waste, AI-based generative design drives 3D printing adoption and improvement.
“Additive manufacturing has the potential to decentralize manufacturing because it’s basically a single machine with a single supply of material that can create an infinite stream of different parts,” explained Schrauwen. “It really doesn’t matter what they are. You don’t need to make molds, there’s no upfront investment to produce a new type of part. Many machines, spread across the world, can make different parts all day long.”
Democratization, commercialization, personalization
Interesting enough, despite all the talk about machine-driven disruption in manufacturing, it wasn’t technology that sparked this moment. The maker movement initiated it.
Despite the maker movement’s demonstration that anyone can create something, creativity in design remains an uncommon skill among humans, and nonexistent among machines. As manufacturing becomes more tech-driven, the demand for designers will increase. These new technologies may, in fact, create jobs within these communities, contrary to popular belief.
Some people, however, do not think AI and distributed manufacturing will benefit little guys like this. Dartmouth’s D’Aveni agrees that AI-based generative design will promote additive manufacturing (which will in turn lead to a distributed manufacturing model closer to customers), but that’s it. The AI-enhanced software digitally optimizes production and supply chains, he said, so it is likely to encourage the creation of software platforms to coordinate and optimize manufacturing in general. D’Aveni concludes that this will result in a landscape dominated by giant pan-industrial corporations and networks rather than small businesses.
Conclusion: Artificial Intelligence the self-designing machine
Artificial Intelligence the self-designing machine: Whoever benefits the most from AI, it is clear it will change manufacturing in every aspect, from design to production to business practices to tools themselves. As those changes slowly begin to mainstream in the near future, one thing is certain: your next co-designer may well be a self-designing machine with a few good ideas of its own. All you need is creativity.
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