machine learning 3d printing

How Does Machine Learning Work In 3D Printing

In simple terms, machine learning is when the computer learns by itself. Computers and programmers work together to identify problems or objects using data. The objective is to use data and models for automation for most projects including 3D printers.

How accurate the machine is depends on the data. We also use this data to predict the future. Think of the weather. Using machine learning to predict the weather is perfect because there is specific data which helps predicts what’s going to happen.

3D printing isn’t much different. We can send an outline of what a model should look like through software and have the printer make it. The data is in the coordinates for 3D printers. In fact, it might be easier than weather predicting since models are more predictable.

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How machine learning affects 3D printers?

Machine learning is applied in different areas of additive manufacturing. It has been applied to improve the whole process for 3D printers.

From printing to workflow, this includes process optimization, cloud computing, and more. Machine learning will optimize work even when you’re away from home. Imagine you’re at work and are running a side hustle for 3D printed models.

Your printer can receive the orders via cloud computing and print the order all without you doing any manual work. Machine learning also helps with object recognition.

Object recognition uses what’s called computer vision. It combines computer vision with datasets to identify what’s in front of the computer. For 3D printers, they might not have the object in front of it, but it can see diagrams of a model and start printing. This is why CAD is used. 

The question now is how do 3D printers see the model.

Computer Vision Fields

Computer vision is just teaching computers to process image pixels. To do this, computers use an algorithm which is basically a set of logical rules a computer follows. 

For example, if something is true, then whatever the true statement is, is what is selected. An algorithm is basically a system where rules mitigate human labor.

For clarification, computer vision isn’t just one field. Computer vision can be broken down into 3 parts:

  • Object detection
  • Object localization
  • Image classification

Object recognition/object detection uses a box to lock on to objects. Once the target is in sight, it calculates the dimensions and stores it in the memory of the computer.

Object localization is almost the same as object detection except it looks for the main object in an image while object detection tries to identify all objects. 

Image classification on the other hand puts the images in a category. Take fruits for example. The computer will look at fruits and distinguish between oranges and apples and put them in their own category. Data scientists and A.I engineers use image classification to train models and 3D printers use it to recognize how the model should look.

Big-Data Approach

Engineers are also working on a 3D-Search engine. This engine works similar to Google. If you’re not familiar with how Google works, what they do to put websites on the result page is to crawl websites.

These robots are used to identify different keywords similar to what the user is looking for. Crawling the internet and collecting 1,000,000 STL files from the internet makes it possible to retrieve models with the printer’s interface. 

This is similar to a buffet. You see the food and put it on your plate. But, instead of your plate, users will get the model printed on their bed.

Browsing the internet for files requires an algorithm too. Google employees don’t scour the internet for similar keywords on every site. That would take too long. 

A downside is algorithms have biases. It’ll make it harder to find what you want if there is no data. The upside to this is this leaves the user with room for creativity. You can put your 3D printed model online and have other be inspired by your work.

Another advantage to combining machine learning and 3D printing is shaping the material. There’s a lot of manual calibration going on when setting up printers. Setting up the material with the right settings is one of those manual processes which can make or break your model. Machine learning helps with this.

3D printing also benefits from data with fixing errors. Big data and machine learning fixes errors in real-time. Think about all the problems a print can have. You have warping, shrinking, and more. Machine learning will help you save hours of your time.

Diving Deeper

Essentially, a 3D printer with computer vision can take the shape of the object and size up its layers, layer by layer.

If the printer has a camera placed on top of its enclosure, it can analyze the contours of the object. If there is a model template online, the algorithm will try to match the template iterating when needed.

Computer vision also allows examination of the internal structure of the object. Texture, warping, and more are observed. If there is a mismatch between template and object, iteration takes place again.

Any anomaly in the structure of the object can be identified using what’s called a “clustering algorithm”.

A clustering algorithm looks where there are a lot of data points in a graph and interprets it. These data points are created from the inputs which is the printer creating the object. This real-time correction has been a real issue in the additive manufacturing industry. Reducing failure rate is still an issue today which is why scientists and engineers are deploying supervised machine learning.

Supervised Machine Learning

Supervised machine learning is when a human is on standby to make necessary changes to make a better model. But for the most part, the machine is creating the model.

Let’s take a housing price example to elaborate on supervised machine learning. There are specific features which determine how much a house is worth. 

When we take the variables of bedrooms, neighborhood, and renovations to accurately determine the price, we have supervised machine learning.

Whether it’s supervised or unsupervised machine learning, one of the most important responsibilities of a computer is to reduce failure.

the Big Picture

Essentially, machine learning is used to detect problems and fix them on the fly. One example of this is failure detection.

This can be used for correcting the model on the fly or making sure the filament comes out how it’s needed.

There are two techniques for machine learning. The first is unsupervised learning where the model or computer discovers the information.

The second technique is supervised learning where human assistance comes through. There are advantages to both but the point here is both need information to work. The information given from a 3D printer is the model sent of what it should look like.

Using these techniques will enable cheaper manufacturing, faster production, and hopefully more use of 3D printers in the future.

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