ML Use Cases In Manufacturing Industries.

Introduction:

The terms ML/AI many times are used altogether in common slang. Although both are interrelated but are a bit different from each other. And there’s also a term related to both and that is — deep learning. The infographic above tries to explain all three terms in a brief.

Deep learning is a subset of Machine Learning which in turn is a subset of Artificial Intelligence. And the essence of the idea behind this tech is to make machines smarter by providing them data so that they can identify some patterns and take decisions on their own.

what exactly does AI/ML in manufacturing mean?

AI/ML technology in manufacturing essentially aims to automate complex or repetitive industrial activities by providing cognitive abilities to machines. Cognitive abilities or intelligence is provided to machines by identifying patterns in the manufacturing processes or workflows, which in turn is achieved by first gathering enough data and then analyzing it to unearth the useful patterns.

Here we discuss some of the important use cases of ML in Manufacturing Industries.

(1) Digital Twins:

Digital Twins are a virtual or digital model of a machine or a process that is created using computing power and a variety of sensors installed on a piece of machinery to study that object.

Note that this is a little bit different or we can say advanced from simulation. Simulations can study only one process but digital twins can perform more than one simulation. And not only this, you can’t provide real-time data in simulation while this is possible in the case of digital twins.

So, using real-time input data combined with ML/AI capabilities, these virtual machines can estimate precise and accurate outputs, which will ultimately help in better R&D and product development.

(2) Smart Manufacturing

Smart manufacturing involves smart robots, which are AI-powered robots that don’t need to be programmed each time to perform required tasks. Not only this, since they are robots, they don’t need a break(or a long break) from their task and can perform repetitive tasks without any complaints. They also are less susceptible to error, unlike humans, as per a report from McKinsey that states collaborative and context-aware robots can improve productivity in labor-intensive settings.

So self-learning robots can be made to perform important manufacturing activities like welding, painting, drilling, die castings, etc.

(3) Product Design

This might surprise you, but AI/ML tech is now advanced enough to create new designs for a product. A generative design software created with ML capabilities, where engineers just need to provide input parameters like raw material, size & weight, manufacturing methods, budget, etc, can create a variety of designs of a product to choose from.

An example of using AI in the design process is the automotive manufacturer Nissan.

(4) Self-driving vehicles

Self-driving cars, as the name itself suggests, are cars that don’t need any human to drive. This is another innovation that has been made possible through ML/AI tech.

Self-driving vehicles gather data from their surrounding through a variety of sensors like radar, lidar, sonar, GPS, odometry, etc. This data gathered is then analyzed with AI algorithms that help the car to identify the object and take necessary action.

(5) Energy consumption forecasting

Finally, using ML/AI algorithms, it’s possible to forecast energy consumption too. By gathering and analyzing data of various parameters like temperature, lighting, and movement level within a building facility, it’s possible with ML/AI to create a predictive model that can forecast energy usage in the future. This energy management achieved up to this level of efficiency will not only save energy costs but will also reduce GHG emissions.

(6) Predictive maintenance

Predictive maintenance is one of the key use cases for ML in manufacturing because it can preempt the failure of vital machinery or components using algorithms. By analysing data from previous maintenance cycles, machine learning can identify patterns that can be used to predict equipment failures and when future maintenance will be needed. This information can then be used to schedule maintenance before problems occur. This, in turn, could save manufacturers significant time and money since it allows them to tackle specific issues exactly when needed — and in a highly focused way. This benefits manufacturers by:

• significantly reducing planned and unplanned downtime and, thus, costs.
• providing technicians with focused inspection, repair and tool requirements.
• prolonging the remaining useful life (RUL) of machinery by preventing any secondary damage during repairs.
• reducing the size of the technical team needed to make repairs.

Conclusion

Machine learning in manufacturing offers benefits to manufacturers.

ML helps in equipment maintenance and production, Product design from input parameters, digital twins helps in research and product development.

Finally self -driving vehicles are coming as use case of machine learning and artificial intelligence which identifies surrounding objects and take necessary actions.

ML helps in energy consumption forecasting in future.

ML in Manufacturing reduces cost of production, shortage of man power, improves quality and recognize defects.