Artificial Intelligence In Design :
1.INTRODUCTION:
Artificial Intelligence
and Machine Learning seems to be the current buzzword as everyone seems
to be getting into this
subject. Artificial Intelligence seems to have a role in all fields of science.
According to Britannica, “Artificial intelligence (AI), is broadly defined as
the ability of a digital computer or computer-controlled robot to perform tasks
commonly associated with intelligent beings.” By intelligent beings it
basically means humans … but may be not all humans…so any way,
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.
Rising Above the Hype
AI holds a lot
of potential for the design world, but for this to happen the hype around it
needs to be deconstructed. It would better if designers cleared
their minds and didn’t think about AI as “artificial intelligence”–as though AI
was going to work as some kind of magic-tech. A more useful way to think about
AI—at least for the short-term—is “augmented
intelligence.”
Robots are
not replacing designers. IBM CEO Gini Rometty said that
“If I considered the initials AI,
I would have preferred augmented intelligence.”
AI is going to
be mostly about optimization and speed. Designers working with AI can create
designs faster and more cheaply due to the increased speed and efficiency it
offers. The power of AI will lie in the speed in which it can analyze vast
amounts of data and suggest design adjustments. A design can
then cherry-pick and approve adjustments based on that data. The most effective
designs to test can be created expediently, and multiple prototype versions can
be A/B tested with users. In this blog readers will get to know about how AI can
be used in design by an example mentioned :
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. 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.
THE AI CONTINUUM
Artificial intelligence
is embedded in products ranging from smart gadgets and devices to drones,
robots, and autonomous vehicles. Currently, the AI continuum can be divided
into three general areas according to complexity:
ASSISTED INTELLIGENCE:
AI automates basic repetitive and standardized tasks, working from clearly
defined rules. Humans are still making the key decisions. Examples include
automated assembly line robots as well as software-based agents that simulate
the online activities of humans.
AUGMENTED INTELLIGENCE:
AI enhances the ability of humans to accomplish tasks, and humans and machines
learn from each other. Examples include intelligent virtual assistants, some of
generative design systems, and systems that can bring unusual or noteworthy
events to human attention.
AUTONOMOUS
INTELLIGENCE: AI takes over some decision-making, but only after a human fully
trusts the machine or becomes a liability to the prompt accomplishment of a
task. Self-driving vehicles, currently in development by over 30 companies, are
just one example of autonomous intelligence.
More
than 100 separate pieces, made of a high-strength metal alloy developed by
Airbus, were 3-D printed and then assembled. The resulting partition is the
world's largest 3-D printed aircraft cabin component, and it more than
satisfies the Airbus team's requirements. It is thinner and stronger than the component
it will replace, and because it is 30 kg lighter, each bionic partition will
save approximately 3,180 kg of fuel per plane per year. The partition is
undergoing final testing and approval. Once complete, the final design could be
used in A320 aircraft next year. The lessons Airbus learned in designing the
bionic partition pave the way for changing how an entire aircraft is conceived
and manufactured. The next generation of Airbus planes will be made up of
components based on generative design, built by 3-D printing, using innovative
materials. Airbus plans to evolve its methods for producing larger structures
inside a plane: for example, the cockpit wall, which is twice the size of the
bionic partition and needs to be bulletproof to protect the pilots, or the
structure that houses the galley for food and beverage service.
AI used In Design of parts in Mechanical engineering :
We are
going to talk about Deep learning methods and its possible role in the field of
Mechanical Engineering. Some common examples could be Anomaly Detection (Machine
Learning) and Image based Part Classification (Deep Learning). The focus will
be on Image based part classifiers and why we need them.
Firstly,
what is an image classifier? The ever famous AI which recognizes cat-dog
pictures should come to mind. The data-set used contains images of cats and
dogs, the algorithm learns from it and then is able to guess with 97% accuracy
whether a randomly shown image is a cat or a dog.
We will
attempt a similar code but using Nuts, Bolts, Washers and Locating Pins as our
Cats and Dogs… because mechanical engineering.
So how does it work? An algorithm can classify images(efficiently) by using a Machine Learning algorithm called Convolutional Neural Networks (CNN) a method used in Deep Learning. We can a simple version of this model called Sequential to let our model distinguish the images into four classes Nuts, Bolts, Washers and Locating Pins. The model will learn by “observing” a set of training images. After learning we will see how accurately it can predict what an image (which it has not seen) is.
To create the model, we will have to follow 7 steps:
1. Data Collection
2. Data Preparation
3. Model Selection
4. Train Model
5. Evaluate the Model
6. Hyperparameter Tuning
7. Make Predictions
AI used
in CAD software /Quality Control Software /Manufacturing Software:
Artificial
intelligence (AI) is a part of science that deals with study and development of
the theory, technology and application systems for simulating and extending
human intelligence, involving disciplines such as psychology, cognitive
science, thinking science, information science, systems science and bioscience.
With the
continuous progress of science and technology, mechanical engineering is also
constantly evolving and changing, from the traditional mechanical engineering
to the electronic-mechanical engineering. And attempts are being made by
research to increase the level of automation and intelligence of a device. With
development of AI, we see that most of the mechanical operation could be
automatized and the accuracy and productivity of an operation could be
increased with help of AI.
One of
the traditional mechanical engineering processes is Designing & Drafting.
What if this process could be assisted by a device with artificial
intelligence? We use Computer Aided Designing (CAD) software to design and for
drafting. Just imagine if AI could design with the data as simple as basic
dimension, load carrying capacity, factor of safety etc., i.e., the basic
requirement. Based on the input data a set of designs would be automatically
generated and by selecting the most suitable designing we could proceed for
analysis and then manufacturing process.
Basic
workflow of AI based designing system, or a Generative Design system is:
- Defining
the rules of designing during development of AI based system. Entering basic
requirements of desired product or object.
- System
then generates designs automatically based on the instruction and definitions
seeded into the system.
- Since the
process is automated by AI it shows many possible solution i.e designs. Then
the most suitable design is selected for fabrication.
AI
improvements will directly affect our ability to be creative as engineers.
Also, it increases our productivity and reduces drudgery to great extent.
Therefore, it is important as an Mechanical Engineers to understand AI as it
could help in many aspects of Mechanical Engineers apart from Designing, like
Manufacturing, Metrology, Quality Assurance etc.
AI used Quality Control and Data-driven manufacturing (Renishaw) :
The foundation for all
of AI systems will be data
Most manufacturers
already have access to metrology data, but with the assistance of Renishaw
Central, you can deploy that data consistently. Renishaw's portfolio of
industrial metrology and additive manufacturing (AM) products, which can be
used throughout manufacturing processes, provides the right data at the right
time, enabling you to control your processes.
The Renishaw Central
manufacturing data platform provides a consistent method of connecting your
Renishaw measurement and manufacturing devices, to make it easy for a variety
of systems and processes to access Renishaw device data.
Renishaw Central enables you to:
- store and visualize your data
- consume data into your digital systems
- use standards-based connections
Connectivity
- Automated efficient data collection.
- Plug-and-play solution for easy implementation and maintenance.
- Standards-based outputs allow connectivity to third-party platforms.
- A single up-to-date view of factory data.
Control
- Continuous improvement through analysis of end-to-end process data across operations, cells and time periods.
- Predict, identify and correct process errors before they happen by characterising acceptable process trends and performance.
- Enhance understanding of process performance, part quality and product design by using metrology data captured throughout your processes.
Consistency
- Process automation for unattended 'lights out' productive manufacturing.
- Intelligent and automated decision making requires less human intervention.
- Use of data from multiple devices and operations for superior control of upstream processes.
- Shorter feedback loops enable rapid reaction and adjustment to process variation.
- Increase machine utilization and productivity while reducing scrap.
How it works
Renishaw Central
provides consumers of Renishaw data with a storage and interface layer that
contains up-to-date machine and job information, including metrology, machine
status and alert data. This information is made available to customers in
several ways, including standards-based
output (such as MT Connect), the Renishaw Central API, and web browser visualization.
Conclusion:
Deep Learning
(Artificial Intelligence) is a field of study that has immense possibilities as
it enables us to extract a lot of knowledge from raw data. At its core it is
merely data analysis. In this age of the internet, data is everywhere and if we
are able to extract it efficiently, a lot can be accomplished.
This field has a lot of
possible applications in the domain of Mechanical Engineering as well. Since
almost all studies in Deep Learning need a domain expert it would be advisable
for all engineers with interest in Data Analytics, even though they haven’t
majored in Computer Sciences, to learn about data science, machine learning and
examine its possibilities. The knowledge of the domain plus the skills of data
analysis will really help us excel in our own fields.
References:
1. Paper: Mechanical Product Lifecycle Management meets Product Line Engineering by Charles W. Krueger
2. https://youtu.be/pJvG6dr1sQQ
3. https://builtin.com/artificial-intelligence
5. https://www.renishaw.com/en/data-driven-manufacturing--14152
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