Four steps for successful application of artificial intelligence in manufacturing

Manufacturers can benefit from artificial intelligence in a variety of ways, such as improving production, quality control and efficiency. While AI offers several new applications for manufacturers, to gain the most value, companies must use it throughout the entire manufacturing process.

This means manufacturing engineers need to focus on four key aspects of AI data preparation, modeling, simulation and testing, and deployment to successfully operate in uninterrupted manufacturing Artificial intelligence is used in the process.

No Need to Be an Artificial Intelligence Expert

Engineers may think that developing artificial intelligence models takes a considerable amount of time, but this is often not the case. Modeling is an important step in the workflow process, but it is not the end goal. The key to successfully using AI is to identify any issues at the beginning of the process. This lets engineers know which aspects of the workflow require investing time and resources to get the best results.

When discussing workflow, there are two points to consider:

Manufacturing systems are large and complex, and artificial intelligence is only one part of it. Therefore, AI needs to work together with all other working parts on the production line in all scenarios. Part of this is using industrial communication protocols, such as OPCUA, and other machine software, such as control and monitoring logic and human-machine interfaces, to collect data from sensors on the equipment.

In this case, engineers are already set up for success when incorporating AI because they already understand the device, regardless of whether they have extensive AI experience. In other words, if they are not an AI expert, they can still use their expertise to successfully add AI to their workflow.

AI-driven workflow

Building an AI-driven workflow requires 4 steps:

1. Data preparation

When there is no good When using data to train AI models, projects are more likely to fail. Therefore, data preparation is crucial. Wrong data can cost engineers time to figure out why the model doesn't work.

Training the model is usually the most time-consuming step, but it is also an important step. Engineers should start with the cleanest, labeled data possible and focus on feeding the data into the model rather than focusing on improving the model.

For example, engineers should focus on preprocessing and ensuring that the data fed into the model is correctly labeled, rather than adjusting parameters and fine-tuning the model. This ensures that the model understands and processes the data.

Another challenge is the difference between machine operators and machine manufacturers. The former usually has access to the device's operation, while the latter requires this data to train AI models. To ensure that machine manufacturers share data with machine operators (i.e. their customers), both parties should develop protocols and business models to govern this sharing.

Construction equipment manufacturer Caterpillar provides a great example of the importance of data preparation. It collects large amounts of field data, and while this is necessary for accurate AI modeling, it means a lot of time is needed for data cleaning and labeling. The company successfully leveraged MATLAB to streamline this process. It helps the company develop clean, labeled data that can then be fed into machine learning models, leveraging powerful insights from machinery in the field. Additionally, the process is scalable and flexible for users who have domain expertise but are not AI experts.

2. Artificial Intelligence Modeling

This phase begins after the data is cleaned and properly labeled. In effect, this is when the model learns from the data. Engineers know they have entered a successful modeling phase when they have an accurate and reliable model that can make intelligent decisions based on inputs. This stage also requires engineers to use machine learning, deep learning, or a combination of both to decide which result is most accurate.

In the modeling phase, whether using deep learning or machine learning models, it is important to have access to several algorithms of the artificial intelligence workflow, such as classification, prediction, and regression. As a starting point, the various pre-built models created by the wider community may be helpful. Engineers can also use flexible tools such as MATLAB and Simulink.

It’s worth noting that while algorithms and pre-built models are a good start, engineers should find the most efficient path to their specific implementation by using algorithms and examples from others in their field. Target. That's why MATLAB provides hundreds of different examples for building AI models across multiple domains.

Also, another aspect to consider is that tracking changes and logging training iterations is crucial. Tools like Experiment Manager can help achieve this by interpreting the parameters that lead to the most accurate models and reproducible results.

3. Simulation and testing

This step ensures that the AI ​​model works correctly. AI models are part of a larger system and need to work with various parts of the system. For example, in manufacturing, AI models might support predictive maintenance, dynamic trajectory planning, or visual quality inspection.

The remaining machine software includes control, monitoring logic and other components. Simulation and testing let engineers know that parts of the model are working as expected, both on their own and with other systems. A model can only be used in the real world if it can be demonstrated that it works as expected and is effective enough to reduce risk.

No matter what the situation, the model must respond the way it should. Before using the model, engineers should understand several questions at this stage:

• Is the model highly accurate?
• In each scenario, does the model perform as expected?
• Are all edge cases covered?

Tools like Simulink allow engineers to check that the model behaves as expected before using it on a device. This helps avoid spending time and money on redesigns. These tools also help build a high level of trust by successfully simulating and testing the model's intended cases and confirming that expected goals are met.

4. Deployment

Once you are ready to deploy, the next step is to prepare the model in the language it will be used in. To do this, engineers often need to share an off-the-shelf model. This allows the model to be adapted to a specified control hardware environment, such as an embedded controller, PLC or edge device. Flexible tools like MATLAB can often generate final code in any type of scenario, providing engineers with the ability to deploy models in many different environments from different hardware vendors. They can do this without rewriting the original code.

For example, when deploying a model directly to a PLC, automatic code generation eliminates coding errors that may be included during manual programming. This also provides optimized C/C or IEC61131 code that will run efficiently on major vendors' PLCs.

Successful deployment of artificial intelligence does not require a data scientist or artificial intelligence expert. However, there are some key resources that can help engineers and their AI models prepare for success. This includes specific tools made for scientists and engineers, applications and capabilities to add AI to workflows, a variety of deployment options for use in non-stop operations, and experts ready to answer AI-related questions. Giving engineers the right resources to help successfully add AI will allow them to deliver the best results.

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