Top 5 Applications of Machine Learning in Manufacturing

With the Fourth Industrial Revolution, Latest manufacturing technologies are utilizing machine learning, artificial intelligence and Industrial internet of things technologies throughout the manufacturing process to improve productivity, product quality and reduce costs. Each of these technologies has their own applications in manufacturing. In this article we will focus on how machine learning in manufacturing is changing the way manufacturing units work and various applications of machine learning in manufacturing.

Machine learning is a subset of artificial intelligence that provides machines or computer programs the ability to learn from their own experience or available data without being explicitly programmed.

Above features of machine learning algorithms are used in manufacturing to do predictions by analyzing and identifying a pattern in data. Incorporating machine learning at various levels of the manufacturing process has the following advantages.

  • Reduced labor cost.
  • Reduced cycle time.
  • Effective utilization of resources or machines. 
  • Improved product quality.
  • Increased productivity.
  • Reduced machine downtime.

Applications of Machine Learning in Manufacturing Industry

Smart factories are incorporating machine learning in the entire manufacturing value chain from production process planning to predictive maintenance of machines. Following are the applications of Machine learning in the manufacturing sector.

  1. Machine Learning for Predictive Maintenance.
  2. Improve Product quality control and yield rate.
  3. Demand Forecasting.
  4. Supply Chain and inventory management.
  5. Production line monitoring and Resource management.

Let’s discuss in detail about the applications of machine learning in manufacturing. 

1. Machine Learning for Predictive Maintenance

In production, IoT connected sensors are used to monitor various machine parameters and their performance. These connected sensor send real time machine data to the cloud where machine learning predictive algorithms are used to forecast the machine failure before it actually occurs. In this way, the possibility of sudden failure in a connected machine is reduced.

IoT send real time machine sensor data to cloud. From where Service Engineer can monitor and analyze real time machine sensor data.
Preventive Maintenance of Manufacturing equipment using Machine Learning
Advantages of using Machine Learning for Predictive Maintenance

Utilization of machine learning algorithms in predictive maintenance in manufacturing has following advantages.

  • Reduced machine downtime.
  • Prevents breakdown on production line.
  • Preventive maintenance instead of scheduled maintenance is implemented. As a result machine maintenance is done only when it is actually required.
Example of machine learning application in Preventive Maintenance

Consider an example of preventive maintenance of a pump. When IoT sensors such as vibration, temperature etc. are connected to the pump. These sensors send real time pump vibration, temperature, water flow and used power data to the cloud.

In cloud machine learning algorithms analyze any sudden change in data. As soon any abnormal data is detected, an alert is sent to the maintenance engineer for detailed analysis.

2. Improve Product Quality Control and Yield Rate

Machine learning in manufacturing is used to improve product quality and yield rate. For example machine Learning computer vision algorithms are used in manufacturing to distinguish between good and bad samples on the production line. 

To achieve this, manufactured part real time Images are sent to the cloud for processing. In cloud machine learning algorithms identify good and bad samples. Machine learning models can also predict the problem before it actually occurs. Therefore by changing machine parameters, manufactured part quality problems can be avoided before it actually occurs.

Using Machine learning and internet of things part quality can be controlled in manufacturing.
Manufactured Part Quality Control using Machine Learning Algorithm
Advantages of using Machine Learning in Quality Control

Following are the advantages of using machine learning algorithms for product quality control on the production line.

  • Reduced rejection rate.
  • Improved product quality.
  • Increased productivity.
Application Example of Machine Learning in a Part Quality control

Industrial cameras are installed on the injection molding production line. Using machine vision firstly ML algorithms are trained to detect molding defects. After that machine vision along with ML algorithms are used to ensure part quality on the production line.

3. Machine Learning in manufacturing demand Forecasting

Machine learning algorithms utilizes historical and current data to forecast manufactured product future requirements. Planning engineers use forecasting data to plan manufacturing activities. In this way accurate forecasting ensures fulfilling customer orders on time. Which leads to higher customer satisfaction.

Application Example of Machine Learning in Demand Forecasting

Most companies have limited resources. If a company is in seasonal business such as manufacturing of air heaters and fans. They need to plan production activities in such a way that none of their product orders get impacted.

Therefore for effective resource utilization, machine learning algorithms utilizes past sale data, existing inventory and production / supplier capability in forecasting production activities.

4. Machine Learning in Supply Chain and Inventory Management

Supply chain and inventory management is required in the manufacturing industry to deliver manufacturing orders on time and reduce cost. Machine learning algorithms are used to predict the best time to release purchase orders by analyzing production, inventory, Sales orders and historical data.

This image shows the working of machine learning in supply chain management.
Workflow of Machine Learning Algorithms in Supply Chain and Inventory Management
Example of the Application of Machine Learning in Supply Chain Management

According to production planning, ML technology helps in suggesting the best time to float orders to suppliers. ML programs can also predict the best shortest route and time for shipments considering existing weather and historical data. This helps shipment companies in delivering orders on time and at reduced cost.

5. Machine Learning in manufacturing Line Monitoring and Resource Management

Connected machines and equipment send real time PLM and sensor data to the cloud. Where machine learning algorithms analyze real time data and send alerts to production managers. Production managers can also monitor real time machine data and make decisions accordingly. Machine learning algorithms also use this data for process planning and effective utilization of resources.

This image shows how machine learning is used in monitoring a production line and effective resource utilization.
Workflow of Production Line Management using Machine Learning
Example of the Application of ML in Resource utilization

Google has reduced approximately 40% electricity bills by managing one of their data centers using machine learning enabled system. This is achieved without any change in existing infrastructure.

To sum up, Machine learning is an emerging technology. It is changing the way we do manufacturing. It helps in monitoring the real time production data and processing that data in the cloud.

We will keep adding more updates to the applications of machine learning in manufacturing. Please add your suggestions, comments or questions to applications of machine learning in manufacturing.

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