Machine Learning in Manufacturing Industry

What is Machine Learning?

Machine learning is application to understand, analyze and act for your machine. It has ability to learn and improve automatically from the experienced process.

In simple word, you have experienced auto image tagging in Facebook, also auto spam email detection by email providers. Yes, they use machine learning to learn your machine’s activity or your profile activities. Filtered content and pattern of spamming emails.

It helps to solve many problems in real world like:

• Data entry process
• Detecting spam
• Product guide for correct recommendation
• Medical diagnosis
• Lifetime value prediction for better customer satisfaction
• Financial analysis
• Manufacturing industries

Here, we will discuss more on “ML in Manufacturing Industries”. Let’s get more understanding about Machine Learning.

In general, there are many methods/algorithms to develop machine learning application but here we will discuss main three methods:

A. Supervised machine learning algorithms:

In this method, Algorithms can apply what has been learned from the past trained dataset to new data using labeled examples to predict the outcome events. It required big-data set to sufficiently train algorithms. The learning algorithms can also take feedback from the outputs with correct, intended output and find errors in order to modify and improve the model.

B. Unsupervised machine learning algorithms:

In contrast, it’s not required any labeled or trained datasets. This learning studies how system can infer a function to describe a hidden structure from unlabeled data. The system doesn’t predict the right output, but it can draw inference from datasets to describe hidden structures from unlabeled data.

C. Reinforcement machine learning algorithms:

Machines and software agents get feature to automatically determine the ideal behavior within specific context using this algorithm and maximize its performance. This learning method interact with its environment by producing actions and discovers rewards (or errors) which is most relevant characteristics of reinforcement learning method. Agent requires reward feedback to learn which action is best, this is called as reinforcement signal.

ML helps software application to become more accurate in predicting outcomes without being explicitly programmed.

Industry Challenges:

Now, Lets discuss about the challenges faced by manufacturing industry. As we know that manufacturing is very established industry, however the importance of it cannot be rated high enough. Several mature economies experienced a reduction of manufacturing contribution towards their GDP over last decades.

The nature of manufacturing systems faces ever more complex and dynamic behaviour. To satisfy the demand for high-quality products with efficient manner, It is essential to utilize all mean available. Below listed the major challenges in manufacturing industry:

• Resource utilization and planning
• Production planning
• Product quality sustainability
• Maintenance planning

All above mentioned major points have been planned reactive/preventive in industry because of unknown events happened. This creates disaster in quality and delivery of product in competitive market.

How does ML help & Demanded?

Due to cyber revolution, the manufacturing industry today is experiencing a never seen increase in available data. This big data has potential to improve the manufacturing process and product quality sustainably. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and industrial consultant.

It helps to predict anomaly process, health of machine, assets monitoring and resource utilization. ML has capabilities to take your process from reactive/preventive to predictive and help to plan your production & downtime.

If you are OEM, does your machine have ML algorithm? Its demanded.

ML should embed into your machine because its required and demanded hugely in future. So its time to re-invent your machine to make it compatible for your smart client.

By 2020, 25% of leading manufacturer will rely on embedded intelligence with IoT, ML and AI. It helps to improve product quality by 35% of total manufacturing.

Bhavdipsinh Rathod