Mar 7
AI and Design… A unique Camaraderie
Authors: Niranjan Dhokarikar, Vedant Deokule, Chinmay Dhekne, Ganesh Dhere, Shritej Deshmukh
Engineering.. the traditional definition of this vast subject was the efficient and effective design of systems to suit the human requirements. When we considered design, it meant design from scratch.. from choosing materials to coping up with power transmission and electric characteristics, to name a few, parameters of the product.
Engineering was a job carried out with pencils and paper not all that long ago. Calculations were made by hand and designs on large sheets were sketched out. Physical models will be created from actual blueprints to figure out how the final product should look and be made.
This was a tenacious and a meticulous task. There were some backdrops regarding this method. A small change in design meant that the whole design would have to be remodified.
Fast forward to a few years, came the innovations of the software programs for various design departments such as the modelling of parts, or the simulation of these or the stress strain or fluid dynamic analysis. Some of the basic techniques that engineers deploy when designing new product designs are computer-assisted design, computational fluid dynamics, and finite-element analysis applications. Prototypes may be printed directly from machine files when physical models need to be checked.
Although these instruments have strengthened the capabilities of engineers, the engineer is still clearly in charge of the design process. That power, however, is now in doubt. Growing interest is being expressed in using emerging artificial intelligence and other innovations to achieve higher levels of product automation and drive new product innovation. Advances in AI, coupled synergistically with other innovations such as cognitive computing, the Internet of Things, 3-D (or even 4-D) printing, advanced robotics, virtual and mixed reality, and interfaces with human machines, are changing what, where and how goods are built, created, manufactured, delivered, serviced, and updated.
In his seminal work, famed economist Frank Knight wrote that the distinction between risk and uncertainty comes down to a matter of measurability. Since it is possible to measure risk, robust predictions can be made, provided all risks are known. Uncertainty, on the other hand, can’t be similarly measured and, therefore, poses unknown risks, which throw forecasts out of whack and precipitate unreasonable decisions.
Unfortunately, we live in a world characterized by volatility, uncertainty, complexity and ambiguity. Society is subject to forces different from any other era and reality is often hazy and easily misread. Change is influenced by multiple social, political, economic and technological forces, and is often abrupt and unpredictable.
An uncertain technological future requires adaptable and resilient engineers who can see through the fog to create robust engineering designs based on AI. They must understand the capabilities and limitations of both their environments and the cognition afforded through AI.
AI and it’s role in Design
This revolution will allow for a new kind of design process, one where, with little human interference, AI-enabled programs iterate and optimize. The resulting designs seem extremely complex, but are no more difficult to print than traditional designs, thanks to advanced printing technology. In commercial aircraft and other vital structures, parts that are the result of this generative design process are already being prepared for use.
The shift from drafting boards to CAD to engineering was disruptive. It is expected that the next transition to generative design will be more disruptive.
In leaps and bounds, artificial intelligence is moving forward (indeed some researchers are now talking about the development of artificial superintelligence-ASI) and much of the AI enthusiasm is targeted at applications where computer systems work with great autonomy. The self-driving car is the poster child for AI, however there are a range of interesting applications, from robotic doctors that can more reliably diagnose diseases than any human doctor to AI-directed businesses that can orchestrate business operations without flesh and blood management.
Existing artificial intelligence has already impacted the product-design process, and AI will change the way we embed connected sensors and use mixed or augmented reality headsets in the future.
Open-source tools, such as Amazon’s DSSTNE, Microsoft’s DMLT, and Google’s TensorFlow, contain software libraries that enable machine learning. Google, for example, recently released an open-source AI tool called DeepVariant that is able to provide a more accurate depiction of a person’s genome from gene sequencing data than other methods.
Amazon’s Alexa and Apple’s Siri use natural language processing to make decisions. Oncologists are training IBM Watson to help them diagnose and treat lung cancer. Tesla and Google are competing to bring autonomous self driving cars to consumers. The Israeli company, Zebra Medical Systems, is developing tools for radiology that have greater than human accuracy.
Next, in generating sophisticated designs, AI would be able to help. At the designer’s elbow, intelligent systems will work, propose alternatives, incorporate sensor-based data, produce design precursors, optimize supply chain processes, and then deliver the designs to smart manufacturing facilities.
The AI generative design framework, such as Autodesk’s Dreamcatcher, explores the permutation of a design solution with the limits of the design problem identified, cycling rapidly through thousands or even millions of design choices and running performance analyses for each design. The device will tap available cloud computing processing resources for the most intensive calculations.
Its machine-learning algorithm is a core component of a generative design method. Without human guidance or interference, the algorithm detects patterns inherent in millions of 3-D models and generates taxonomies. Generative design software may use that skill to learn what all the components of a complex system are, define how they relate to each other and decide what they do. For a particular dimension of a piece, it can then serve up hundreds of different design options and provide them as parts for the next design.
PROJECT DREAMCATCHER
Dreamcatcher is Autodesk’s experimental platform to explore the potential of AI techniques and generative design methods in product development, from conceptual design to manufacturing.
The Autodesk dreamcatcher includes:
Designers’ methods for explaining design concerns. Solutions become scalable and accretive by pattern-based definition, thereby extending the quality and number of alternatives searched for in each design session.
Form synthesis tools, including several purpose-built methods that from a wide collection of input parameters, algorithmically generate designs of different types.
Exploration tools, offering a range of potential solutions to designers and their related solution strategies. These tools help designers create a mental model with high performance alternatives compared to all others in the package.
The designer can export the design to manufacturing tools or export the resulting geometry for use in other software tools until the design space has been explored to satisfaction.
The human re-enters the process once new designs have been created by the AI system. Based on the various choices of designs offered by the generative design method, he will review various options and then change the design goals and constraints to narrow down the options and optimize the available ones. Using the data, another collection of designs will then be iterated by the generative design method.
The most appropriate solution will be chosen by a combination of artificial intelligence and human intuition over the course of many of these periods.
Generative design techniques are not particularly fresh, but new excitement has been generated by integrating these deep reinforcement machine learning algorithms with cloud computing.
EVOLVING AN ANSWER
The generative design method may sound like something for the far future, but it has recently been applied to a real-world challenge involving a part of the Airbus A320 aircraft, one of the world’s most high-profile and costly items.
The portion was a partition separating the passenger cabin from the plane’s galley and supporting a flip-down seat during take-off and landing for flight attendants. Airbus engineers were searching for ways to reduce the weight and volume of the partition while maintaining enough power to bear flight attendants’ loads. It also had to hold up in case of a crash landing under the force of 16 g.
With a mix of generative design, biomimicry principles for material and structural design, and additive manufacturing, a group of Airbus designers turned to Autodesk and other collaborators to see if they could come up with an improved partition.
The generative modelling processes used by the team employed two biological model-derived algorithms. The first was focused on the slime mould adaptive networks: a single-celled organism that can expand, spread and aggregate with the minimum number of lines to form multicellular structures. In case a line fails, these systems have a built-in redundancy to maintain communication within the network. The construction of the bracing of the overall partition was informed by this algorithm.
The lattice that makes up each member was developed using a second algorithm derived from the microscale structure of mammal bones. Several different load cases were considered, some of which allowed the partition to contain more than 66,000 micro-lattice bars.
The generative design program (in this case, Autodesk Within) cycled through thousands of design variants after the design parameters were set. To determine the prototype, the human design team digitally mapped the various created options against weight, stress, and strength parameters.
A latticed structure that looks random but is based on the growth of mammal bones is the resulting design. The partition is thick at points of tension, like natural bone, but lightweight everywhere else. The feature called the bionic partition by Airbus and Autodesk, is 45 percent lighter than the conventionally built compartment divider used on current aircraft. The finished product, produced using additive manufacturing, needs only one-twentieth of the raw material compared to a partition constructed using conventional design processes.
In other high-profile projects, Autodesk has also implemented its generative design and AI software. To design the chassis of a race car, Autodesk partnered with the digital industrial engineering company Hack Rod. The company is also interested in the MX3D bridge project in Amsterdam. Not only will the bridge be generatively constructed, but multi-axis industrial robots will also print it on-site.
Given the power of generative design instruments and the near-omniscient artificial general intelligence that will soon weaken it, some will wonder whether it will make the human designer and engineer redundant. We think it is the wrong way to look at artificial intelligence’s contribution. Instead the ingenuity of designers will be enhanced by these innovations and their productivity will significantly improve.
For example, a designer would be able to start a conversation with a cognitive design assistant using a system connected to a powerful voice recognition system. “He begins, “Let’s build an autonomous vehicle. Several distinct vehicle types appear. The designer chooses a category, defines the priorities and constraints of high-level design, and the design tasks begin with the cognitive assistant doing much of the work and creating many alternatives beyond what can be achieved by existing generative design systems. In assessing the various choices and making the final decision, the cognitive assistant will also assist the designer. It will also use the new explainable AI (XAI) tools to explain the logic of the chosen options to the designer. The cognitive assistant would automatically submit the files to an automated additive manufacturing facility to have a prototype made for the device after completing the design of a complex system.
ByteLake An AI driven CFD Software
Ever wondered how we could reduce the time taken for the results of computational fluid dynamic models we have simulated and trained. Also most of the times we have to fine tune our model based on the boundary conditions and material selection, and often this needs a lot of time.
ByteLake CFD software which is AI driven is designed for this similar purpose of time saving and giving precise results.
The key features of this enthralling software being as follows
CFD Suite accelerates time to results for conventional CFD solvers by a factor of at least 10x and keeps the accuracy at the level of at least 93%.
New AI models are constantly added by byteLAKE to the CFD Suite which gradually increases the number of CFD simulations that can be handled by the CFD Suite off-the-shelf. To do so byteLAKE collaborates with a growing number of industry leaders.
CFD Suite is an add-on to existing CAE/CFD tools and its integration is a straightforward process.
CFD Suite is a data-driven solution. Therefore, past simulations done by conventional CFD solvers are required to train its AI models so that they can predict the results.
CFD Suite is a scalable solution, and we observed a stable efficiency across cluster nodes.
The inference process is executed up to 9.5x faster on the Intel Xeon Gold with CFD Suite optimized with OpenVINO compared with V100 GPU.
By configuring the CFD Suite to use AI models in ACCURATE mode and using 2xXeon Gold CPUs, 90% of the simulation is predicted 111x faster than CFD solver computation, and 9.25x faster considering 10% overhead of the CFD solver.
ROLE OF ARTIFICIAL INTELLIGENCE IN MECHANICAL ENGINEERING
There are many areas where AI influences mechanical engineering processes. The idea behind the work of AI remains the same. It works without humans, but tends to increase compared to humans. This prioritizes the automated part of the task of supplying data to the computer and allowing the machine / process to continue its function according to the command. You can feel the effects of AI in various fields such as:
1. MANUFACTURING
Many manufacturing processes require mechanical engineering using components, products, processes, and so on. Artificial intelligence is currently used in similar mechanical engineering processes. Whether it’s a component, a product, or a process. It guarantees that its presence is felt. Many other processes and technologies are quickly and efficiently becoming simple with the help of artificial intelligence. The main goal here is a machine that can do more work than human effort and can do it with minimal human effort. If the above goals are achieved or implemented, they will have serious implications for different areas of the sector. (And in some places, connections are already starting to influence the scenario).
2. MECHANICAL DESIGN
Whenever we start the process of building a component/product/flow, the first step of it would be of Mechanical Design. Different sectors of services are provided through mechanical Design. To list the few as; Product Design, Machine Design, Mechanical Component Design, Tooling and Fixture Development, Mold Design, Casting Design. All are coming under the umbrella for Mechanical Design Services. A. I. can majorly impact Product Design Services when it comes to designing the concept, examining the product, and also during the manufacturing of the product.
WILL AI REPLACE ENGINEERS?
The response is NO. The position of the human engineer will in time, be that of a director rather than a producer. Humans may not be the ones carrying out the tasks, but we will select the path we want the system to follow and provide the most important feedback: if we are pleased with the performance.
Most of the technical aspect of engineering will be shifted to the machine-based design method, just as a good engineer today does not need to be able to operate a slide rule or complete an isometric drawing. To some degree, in a working partnership with an artificial intelligence that can find the solution as long as it knows what the problem is the programmer will become someone adept at interpreting the inchoate human desires for goods with a more elegant shape or using less resources or operating more efficiently. Engineering will be altered until computers know how to build, even how to design themselves, but engineers will still be highly trained. AI technologies can augment them cognitively, mentally, and perceptually. And thus, with a different set of skills, they will simply have to develop their abilities, including teaching the AI systems how to innovate and become successful collaborators in potential human-AI organizations.
