Machine Learning: The Future Of Manufacturing

Machine learning

What happens when you give an industrial robot the intelligence to predict a problem before it occurs? As we enter the fourth industrial revolution, a new era is dawning characterized by the collaboration between humans, machines, and the Internet. 

As a result, manufacturers are increasingly turning to artificial intelligence and machine learning. By identifying patterns and areas of concern, machine learning gains crucial insight into the manufacturing process, creating opportunities for improvement. 

But what exactly is machine learning? Learn all there is to know about machine learning and how it is used in manufacturing to speed up the production process.

What is machine learning and how does it apply to manufacturing?

 Before we dive into the many uses of machine learning in manufacturing, let’s first define the software. Machine learning is a branch of artificial intelligence. This technology uses algorithms that allow computers to learn from previous experiences. This enables the software to solve the problem itself. 

The beauty of this process is that machines can learn without human input, they can learn from themselves. This process is called “reinforcement learning”, where the computer learns by trial and error until it achieves its goal. It’s going to revolutionize the way we do business because it will allow companies to make decisions based on data instead of intuition. 


Machine Learning VS Artificial Intelligence

People often use the terms machine learning and artificial intelligence interchangeably. However, there are some important differences between the two concepts:

Artificial intelligence (AI) is a broad term for computer systems that can perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making and translation between languages. AI research assumes that some aspects of human intelligence can be replicated by machines or algorithms.

Machine learning (ML) is a subset of AI that focuses on prediction and estimation. Machine Learning algorithms allow computers to automatically learn from data without having to be explicitly programmed by humans.


Benefits of machine learning

By implementing machine learning, you can significantly benefit from the previous experiences of your manufacturing operations. Some of the most obvious benefits of machine learning include:

Process optimization: The algorithms will automatically adjust parameters and find optimal solutions in complex systems. This helps increase efficiency.

Improve product quality: When machine learning is used, product quality is analyzed and recommendations are made on how to improve product quality or reduce defects.

Predictive maintenance:  Machine learning algorithms can be used to predict when a machine will fail or give an early warning of impending failure. This allows for preventive action to be taken before equipment fails and results in downtime or lost production.

Reduce costs: Using machine learning earlier in the process lowers production costs. As a result, the company saves money on equipment repairs, power expenses, and space use.


Basics of getting started with machine learning

Now that you know what Machine Learning is, it is also important to take into account that Machine Learning isn’t something you can simply implement without considering the challenges that come with it.

Before implementing Machine Learning, manufacturers need to prepare, develop different systems and processes that accelerate, simplify, digitize and automate data collection. Providing a continuous stream of the latest data within the organization.

With Azumuta you can collect and monitor all of your production data in real-time. By connecting all your data sources, you can use the information for cross-functional data analytics to get a high level view of your manufacturing activities. The gained data and insights allow you to continuously improve the performance of your machines and your overall production process.

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