YUFENG GUO: Hi.
My name is Yufeng Guo.
And today we're going to explore how
you can build a solution with Google Cloud AI
by taking a look at the use case of using AI in manufacturing
for increased productivity.
Our example comes from the machining industry.
So let's first talk a bit about what machining is.
Machining is the process for cutting
raw materials, such as metal, wood,
or plastic, into a desired final shape and size.
Modern machine shops use computer-controlled precision
machining tools for operations like cutting, drilling, boring,
milling, and so on.
Machining is an essential part of the manufacturing industry.
Historically, a major challenge of the machining industry
is the time-consuming task of cost estimation.
For instance, when a machine shop
takes an order for building parts like these,
it needs to determine the individual cuts and holes that
need to be made for each individual part
by carefully reading the blueprint provided
by the customer.
This manual process requires machining experts
who are dedicated to the cost estimation process, which
can act as a bottleneck for the productivity of the machine
shop as a whole.
A company called Kabuku in Japan has launched
a new solution for this challenge
by handling the data management of their machining process
with AI.
Kabuku is a startup focused on modernizing the manufacturing
industry with 3D printing, cloud,
and AI technologies for major manufacturing customers,
including Toyota, Honda, and Olympus.
Let's see how Kabuku's data management tool works.
First, a blueprint is uploaded to the tool in the web browser,
and a region is specified for estimation.
Then the user simply presses the Cost Estimation button.
The tool detects and highlights every object
that requires processing.
You can see the detected types of processing steps,
how many there are, how much each costs,
and the total estimated costs for the entire part.
The tool also allows users to add detailed info for each part
or to have experts update any part that requires minor fixes.
The tool can detect tens of processing parts
on the design in less than one second, which significantly
increases the productivity of cost estimation
by machining experts.
Masahiko Adachi, CEO of Kabuku, explains
the benefit of this tool.
"If machine shops can respond quickly
to cost estimation requests from their customers,
that's a better customer experience and more business
opportunities.
Additionally, this tool can act as a central repository
of expert knowledge for each machine shop.
This allows the shop to easily share knowledge
with junior staff members and learners
without any bottlenecks and make business operations much more
scalable."
Let's learn more about how Kabuku
built this tool with the power of Google Cloud AI.
Kabuku's tool was able to detect the machining operations
from the blueprint.
To achieve this, they used our AutoML Vision technology
with object detection.
First they collected thousands of images in preparation
for ML model training, with different labeled blueprints
for drilling, boring, and cutting operations on them.
Usually, it would take a lot of time
to build an object detection system using their training
data for production use.
You would need to start with data pre-processing,
and ML model design, hyperparameter tuning,
evaluation, and deployment, and would also
need to have resources to keep the model up to date.
And that would require hiring AI experts and ML engineers.
For faster time-to-market, Kabuku
chose our AutoML technology which automates
many steps in the ML lifecycle.
All they had to do was upload their training images
to AutoML Vision and press the Train button
to start the automated model training and development
process.
In half a day, they got an object detection model
generated by AutoML Vision with a detailed report of the model
accuracy and various metrics.
Then Kabuku downloaded the ML model
and used it with TensorFlow.js, which
allows you to run the object detection within a web browser.
That's the reason why Kabuku's machining data management
tool is able to detect tens of processing parts in just one
second.
Even though you could build the object detection
model much faster with AutoML Vision,
that's not the whole story.
One important requirement for a Kabuku's tool
is the ability to keep learning from the experts.
The practices and patterns used in blueprints
are different for each customer project or machining expert.
So the tool provides a UI for fixing the misdetected parts
or adding more details.
Based on this feedback from experts,
the tool reruns the ML training process
and deploys a better ML model that captures the practices
and patterns used in each case.
To implement this continuous ML training process,
Kabuku used Cloud AI platform pipelines.
This product allows users to build orchestrated ML pipelines
and manage them from data pre-processing
and validation all the way through
to model training, validation, and deployment.
Instead of asking AI experts again and again
for a manual rerun of the whole training process,
everything can be automated and continuously integrated
just by invoking the pipeline, making the whole service robust
and production ready.
With AI platform pipelines, Kabuku
was able to build a positive feedback
loop between the machining experts and model training
pipeline, resulting in ever-better accuracy
of their model and higher productivity
as the experts keep using the tool.
This loop of application and training
is called active learning in ML.
In summary, Kabuku was able to use the power of Google Cloud
AI for faster time-to-market, quick object detection,
and create a flexible ML pipeline for active learning.
What will your AI journey look like?
Get started today with these tools from Google Cloud AI.
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