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The Role of Machine Learning in the Manufacture Industry

As the world enters the fourth industrial revolution dubbed Industry 4.0, business practices evolve faster than ever before. New technologies such as artificial intelligence (AI) and machine learning (ML) provide incredible insights into manufacturing practices, leading to more efficient methods. 

ML is undoubtedly the backbone of this new revolution, as it's able to reduce operating costs, downtime while increasing productivity and efficiency. The machine learning industry is expected to grow from 1.41 billion dollars in 2017 to 8,81 billion in 2022. Let's take a closer look at the role of ML in manufacturing.

Definition of Machine Learning

Machine learning is the process of training computers to mimic human behavior. While people learn from experience and through their senses, machines or computers learn from data. 

The more data you feed to the ML solution, the better results you get. ML develops an autonomous thought process over time, allowing it to complete various tasks and business processes without supervision (but some ML models require supervision). Moreover, the role of ML in manufacturing is growing every day as new applications are introduced to improve existing practices and increase productivity.

Application of Machine Learning in Manufacturing

Machine learning applies to any practice or area as long as there's enough data for it to learn from.  IoT sensors placed across an entire production line generate endless amounts of data, allowing ML models to learn the insides of all processes. Of course, that is already a thing in manufacturing practices, and ML/AI solutions are already helping companies increase productivity and efficiency. Let's take a closer look at some of the ML applications in manufacturing.

1. Predictive Maintenance

We have to start our list with predictive maintenance. It's one of the most revolutionary manufacturing practices in history. It's possible only when an ML model applies to a manufacturing line. The machinery and components generate real-time data, sending it to the machine learning model that learns how the production line works. After some time, the model can predict the state of each component and make accurate predictions on when they will break down.

Knowing which machines are about to stop working can save you a lot of money and drastically reduce downtimes caused by faulty machinery. You will always know what's going on upfront, so you'll never be surprised by a machine breaking down suddenly. Moreover, the AI will tell you when to make repairs, so the process won't require large teams or long repair times.

2. Quality and Productivity Boost

Running a growing business means that you'll have to boost production to increase the rising demand. You won't be able to afford losses or increased downtimes. ML and AI work together to analyze your entire manufacturing system and product quality. Together, they can tell you exactly where your production is losing resources, energy, and which product features need improvement. 

AI-driven quality predictions will give you an edge over your competitors and ensure that your production costs never surpass your budget. Of course, you'll need a custom machine learning model to be able to run root cause analysis and monitor quality over time. 

Furthermore, AI-driven RCA is an impressive tool that helps track and reduce waste far more accurately than any other existing method. The learning industry is always looking for new ML applications to improve existing manufacturing practices.

3. Digital Twin Technology

As the machine learning industry kept evolving, it led to some truly impressive and revolutionary technologies. The Digital Twin (DT) technology is something the world has never seen before, and it's already redefining entire industries. But what is a digital twin?

A digital twin is a digital copy of a real-world product, process, service, or system. The digital copy is an accurate representation of real-world systems because all data comes from IoT sensors. Once manufacturers have a digital twin of their operations, they can run all kinds of simulations, process evaluations, and make accurate performance predictions. 

However, that's just some of the things you can achieve with a digital twin. It can also help improve product development by revolutionizing existing engineering practices with the use of AI. This technology leads to benefits such as drastic cost reductions, superior product quality, optimum performance and productivity, reduced risks, and many others.

4. Smart Manufacturing

The global smart manufacturing market is growing steadily and is expected to reach $314 billion in the next 5 years. Machine learning models and AI can work together and provide an endless number of design solutions to help overcome all potential issues such as size, materials, weight, etc. 

That way, engineers can address design problems even before the production starts. Machine learning can help create new product designs, compare generated product capabilities with existing products, and design multiple solutions for specific problems.

5. Accurate Energy Consumption Predictions

We touched on the topic of IoT sensors that generate data. When paired with machine learning models, these sensors generate all kinds of performance data such as temperature, vibrations, activity levels, and power consumption. Then the ML model learns how a system works, it can make accurate predictions about future energy consumption. Machine learning is all about identifying patterns in generated data, which also applies to energy consumption.

6. Smart Supply Chain Management

Industrial internet of things or IIoT technologies is also able to streamline supply chain management to help manufacturers improve their operations. Machine learning models can identify bottlenecks and monitor every step of the supply chain to prevent overstocking and improve warehouse control.

AI and ML use learning algorithms to spot market trends, track shipments, inventory, and make accurate weather forecasts. That leads to route optimization and better transport solutions. 

Machine Learning Benefits Overview

Machine learning and artificial intelligence use incoming data to produce deep insights and predictions to help improve decision-making in manufacturing. Their ability to spot trends and patterns makes it possible to create better business practices in the future. Together, AI and ML offer benefits such as:

  • Waste and resource reduction
  • A better understanding of current market trends
  • Safety improvement according to industry standards
  • Higher product quality
  • Streamlining the production process
  • Improved visibility throughout the entire operation
  • An early problem detection system
  • Significant reduction in maintenance costs and downtimes
  • Accurate root cause analysis
  • Machinery life cycle optimization
  • Enhanced quality control

Conclusion

The manufacturing industry is currently one of the most technically advanced sectors on the planet. Manufacturers have always been pioneers when it comes to adopting new technologies, and ML/AI solutions are the latest industry trend

These technologies surpass traditional manufacturing practices, allowing businesses to improve their operations, increase product quality while reducing costs. Machine learning is revolutionizing manufacturing practices before our eyes, and they will keep doing so in the future.