Application of AI in Design

Authors: Jugal Budhlani (31), Mohit Bhutada (26), Siddharth Chaabria(40), Maitreyee Chaudhari (35), Rohit Daithankar (45).

AI magic is here to save the world, making us giddy with excitement and terrifying us at the same time. However, AI is still mostly unknown, and figuring out exactly how it will work in the design world is pretty much like trying to figure out how many angels can dance on the head of a pin.

AI (artificial intelligence) has become an over-hyped buzzword across many industries and the design world is no exception. There are ongoing conversations between designers and developers around the future impact of AI.

So, what does design bring to the conversation? With AI, new relationships will need to be established between customer and product. These interactions will be just the beginning of the ongoing conversation between business and consumer about what artificial intelligence can, and should be able to do for products and services. Designers will bring the necessary empathetic context for innovation, which is how a business will succeed with AI.

Progression from Manual designing to AI based design

Not all that long ago, engineering was a profession conducted with pencils and paper. Calculations were done by hand and designs were sketched out on large sheets. From actual blueprints, physical models would be made to work out how the final product should look and be made.

Today, of course, engineering is a discipline intensely involved with computational and software tools. Computer-assisted design, computational fluid dynamics, and finite-element analysis applications are some of the basic tools that engineers deploy when creating new product designs. When physical models must be tested, prototypes can be printed directly from the computer files.

Although these tools have enhanced the powers of engineers, the engineer is still clearly in control of the design process. But that control is now in question. There is increasing interest in using new artificial intelligence and other technologies to reach higher levels of product automation and accelerate innovation of new products.

Advances in AI, combined synergistically with other technologies such as cognitive computing, Internet of Things, 3-D (or even 4-D) printing, advanced robotics, virtual and mixed reality, and human machine interfaces, are transforming what, where, and how products are designed, manufactured, assembled, distributed, serviced, and upgraded.

AI in designing of Mechanical Engineering components.

At present, artificial intelligence technology is often used in the diagnosis of mechanical engineering failure [8–10]. In general, artificial intelligence-based fault diagnosis techniques include rule-based reasoning (RBR), case-based reasoning (CBR), and fault-based tree fault diagnosis. Based on the basic composition and basic principle of the traditional expert system, a mechanical fault diagnosis expert system based on RBR and CBR reasoning is constructed. The overall structure is shown in Figure 2. The system includes maneuver case database, fault diagnosis rule database, fault diagnosis database, fault reasoning machine, knowledge processing, fault diagnosis process interpreter, learning system and expert system man-machine interface.

This revolution will enable a new type of design process, one where AI-enabled programs iterate and optimize with little human intervention. The resulting designs seem impossibly complex, but thanks to advanced manufacturing technology, they are no more difficult to print than conventional designs. Already, parts that are the result of this generative design process are being readied for use in commercial aircraft and other critical systems.

The transition from drafting boards to CAD was disruptive to engineering. The next transformation to generative design is expected to be more disruptive.

Artificial intelligence is moving forward in leaps and bounds (indeed, some researchers now speak of developing artificial superintelligence — ASI) and much of the excitement about AI is directed toward applications where computer systems will operate with great autonomy. The self-driving car is the poster child for AI, but there are a number of intriguing applications — from robotic clinicians who will be able to diagnose illnesses more accurately than any human doctor to AI-directed corporations that can orchestrate company activities without flesh-and-blood management.

The product-design process has already been affected by existing artificial intelligence, and AI will change the way we embed connected sensors and employ mixed or augmented reality headsets going forward. Based on the current trajectory, it is likely we will see AI impact product design and the creation of engineering systems in three distinct stages in the coming decade.

First, artificially intelligent systems will ease the laborious tasks that designers face, such as having to continually search for appropriate content, fix errors, determine optimal solutions, communicate changes, and monitor for design failure. Machine learning will be able to take on those jobs and do them much, much faster.

Next, AI will be able to assist in the creation of sophisticated designs. Intelligent systems will work at the designer’s elbow, suggesting alternatives, incorporating sensor-based data, generating design precursors, optimizing supply-chain processes, and then delivering the designs to intelligent manufacturing facilities.

Steps in direction of AI-based design

The final stage would have more profound implications. Engineering systems that incorporate stronger AI will be able to function more like human assistants during the design and creation process. Actual human designers will be able to “design” merely by expressing intent and curating results, while intelligent systems and machines will act on these intentions to create new design iterations for review.

The AI would not approach the project the way a human designer would, however. Instead, the computing power would be harnessed to mimic Nature’s evolutionary approach — taking the best existing solution to a problem and iterating to optimize performance in a given environment. In this way, the AI would explore the variants of a design beyond what is currently possible using the traditional design process. This approach is called generative design.

Although much of the generative design process is conducted autonomously, the process starts with choices made by a human. That engineer or industrial designer sets high-level design goals, along with design parameters and constraints, including material type, manufacturing capability, and price points.

With the boundaries of the design problem established, the AI generative design system, such as Autodesk’s Dreamcatcher, explores permutation of a design solution, quickly cycling through thousands — or even millions — of design choices and running performance analyses for each design. For the most intensive calculations, the system can tap available cloud computing processing power.

One key component of a generative design system is its machine-learning algorithm. That algorithm detects patterns inherent in millions of 3-D models and generates taxonomies without human direction or intervention. Using that capability, generative design software can learn what all of the components of a complex system are, identify how they relate to each other, and determine what they do. It can then serve up dozens of different design options for a specific dimension of a component and provide them as components for the next design.

Project Dreamcatcher

Dreamcatcher is an experimental platform being built by Autodesk to explore the potential of AI techniques and generative design tools in product creation from conceptual design all the way to fabrication.

The Autodesk dreamcatcher includes:

  • Tools for designers to describe design problems. Through pattern-based description, solutions become modular and accretive, thereby expanding the quality and number of alternatives that are searched in each design session.
  • Tools for shape synthesis, including several, purpose-built methods that algorithmically generate designs of different types from a broad set of input criteria.
  • Tools for exploration, presenting designers with a set of possible solutions and their associated solution strategies. These tools help designers in building a mental model of which alternatives are high performing relative to all others in the set.

Once the design space has been explored to satisfaction, the designer is able to output the design to fabrication tools, or export the resulting geometry for use in other software tools.

Once new designs have been generated by the AI system, the human reenters the process. He will study different options based on the multiple choices of designs provided by the generative design system, and then modify the design goals and constraints to narrow down the options and refine the available ones. Using that input, the generative design system will then iterate another set of designs.

Over the course of several of these cycles, the most relevant solution will be selected through a combination of artificial intelligence and human intuition.

Generative design techniques are not especially new, but combining these deep reinforcement machine learning algorithms with cloud computing has produced new excitement.

Evolving an Answer

The generative design process may sound like something for the distant future, but recently it was applied to a real-world challenge involving a component for one of the most high-profile and expensive products in the world, the Airbus A320 aircraft.

The part was a partition that separates the passenger compartment from the galley of the plane and supports a flip-down seat for flight attendants during takeoff and landing. Airbus engineers were looking for ways to reduce the partition’s weight and volume while retaining enough strength to bear the loads of flight attendants. It also had to hold up under the force of 16 g in the event of a crash landing.

AI systems may soon design innovative new airframes and modular swappable interiors that can be customized to fit the needs of each flight.

A group of Airbus designers turned to Autodesk and other partners to see if they could come up with an improved partition through a combination of generative design, biomimicry concepts for material and structural design, and additive manufacturing.

The generative design process the team used employed two algorithms derived from biological models. The first drew from the adaptive networks of slime mold: a single-celled organism that can grow, stretch, and aggregate to form multicellular structures, with the minimum number of lines. These structures have a built-in redundancy to retain connectivity within the network, in case a line fails. This algorithm was used to inform the design of the bracing for the overall partition.

A second algorithm, derived from the microscale structure of mammal bones, was used to build the lattice that makes up each member. Several different load cases were considered, some requiring more than 66,000 micro-lattice bars in the partition.

Once the design parameters were set, the generative design software (in this case Autodesk Within) cycled through thousands of design variants. The human design team digitally mapped the different generated options against weight, stress, and strength parameters to decide which to prototype.

The resulting design is a latticed structure that looks random but is based on mammal bone growth. Like natural bone, the partition is dense at points of stress but lightweight everywhere else. The design, which Airbus and Autodesk call the bionic partition, is 45 percent lighter than the conventionally designed compartment divider found on existing aircraft. Fabricated using additive manufacturing, the finished product requires just one-twentieth the raw material compared with a partition built using traditional design processes.

AI and machine learning (ML) in Design for safety in automobiles.

Active Safety system

Active Safety includes set of safety features which reduce the chances of an accident or collision in the first place. Some manufacturers also call it as the ‘Primary Safety System’. Manufacturers employ the active safety systems mainly to avoid the accident. These systems activate before the accident takes place so that they could possibly avoid the accident.

The engineers/vehicle designers build a car with a high level of active safety through superior design. This includes characteristics such as road holding, visibility, comfort, handling, and ergonomics.

Therefore, the safety system such as the Anti-lock Braking System or ABS belongs to the active safety. However, the airbags, seatbelts, and others safety features come into play during an accident. Hence, they are called the passive safety systems.

Active systems respond to an abnormal event such as a safety problem. These systems can be activated manually by the driver or automatically by a computer-driven system (by an ECU). Or sometimes, they are part of the mechanical design itself. These systems are nothing but the derivatives of A and ML in designing a product for safety.

These are some of the active AI Based design features ensuring safety in automobile:

  1. Legible instrumentation and warning symbols
  2. Head up displays
  3. Collision warning/avoidance
  4. Anti-lock braking system
  5. Brake Assist
  6. Early warning of severe braking ahead
  7. Electronic Stability Control (ESP)
  8. Traction control
  9. Chassis assist
  10. Adaptive or autonomous cruise control system
  11. Intelligent speed adaptation


In time, the role of the human engineer may be that of a director rather than of a producer. Humans may not be the ones executing the tasks, but we will be choosing the direction we want the machine to take and we will be providing the most critical feedback: whether we are satisfied with the results.

Much of the technical aspect of engineering will be moved to the machine-based design system, just as one need not be able to operate a slide rule or complete an isometric drawing to be a successful engineer today. To a certain extent, the engineer will become someone adept at translating the inchoate human desires — for products with a more elegant shape or which use less energy or which perform more efficiently — into a working relationship with an artificial intelligence that will find the solution as long as it knows what the problem is.

Once machines know how to design — even how to design themselves — engineering will be changed, but engineers will still be highly skilled. They will be augmented cognitively, physically, and perceptually by AI technologies. And therefore, they will simply need to build their capacity with a different set of skills, including teaching the AI systems how to innovate and become effective partners in future human-AI organizations.