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How Do Machines Learn?

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By John White Last Updated on Jun 14, 2021

Machine Learning, Internet of Things, Big Data, Virtual Reality, and Artificial Intelligence are vitally alerting the way factories work. Their influence is not limited to manufacturing, they are manipulating nearly every industry. This article tries to clarify Machine Learning and its worth in the world of manufacturing.

How do we leverage Machine Learning in factories?

Predictive Maintenance

Equipment on the shop floor makes tons of data during operation just like airplanes create a large size of data while flying. Most of these data were going virtually unnoticed. With the beginning of advanced analytics, using Big Data and Internet of Things platforms, these data can be examined. After a while, algorithms can predict the type of break downs that have never occurred. This helps prevent machine breakdowns and minimize unplanned downtime.

Suggested Read: How ML is saving time and money

Process Control

Just like equipment, processes create a large amount of data. These data cover a lot of information about process control as well as Out of Control (OOC) and Out of Spec (OOS) circumstances. In OOC and OOS, a series of steps need to be followed, which are known as OCAP (Out of Control Action Plan). With automation, OOC and OOS can repeatedly be noticed and to a good amount, OCAP steps can be automated. Machine Learning can help automation respond to OOC and OOS that has not been encountered in the past.

Predictive Quality

It’s important to see possible defects and their effect on Yield before we mass produce a product. Predictive models examine historical data connected with linked products as well with the similar product

productive analytics use cases

during its prototyping to forecast flaws and their quantum, which will, in turn, upset Yield. As predictive models mature, they will progress with regards accuracy and they can estimate defect rates for situations that have not been programmed.

Read: Python programming for data science and machine learning

Energy Management

Energy expenditure is one of the main overheads in a factory. Companies want to minimize energy-related cost. For a given production capacity, equipment clusters, floor outline, programs calculate the optimum level of energy is necessary. If there is a locked loop mechanism, it makes definite energy consumption is at an optimum level in cases it goes beyond arranged limits. However, in today’s world, the shop floor has a dynamic environment where various parameters can change.

Material Consumption

Material costs are the main contributor to the overall cost, especially in Process Manufacturing environments. Later, Process Manufacturing driven plants carefully monitor chemicals expended at dissimilar stations and by every process steps. Algorithms have been developed to monitor the consumption and to take helpful steps in cases when consumption starts going up outside bounds. They check a pre-programmed list of possible causes and take remedial actions as compulsory. Over time, these algorithms “improve” and “learn” to respond to some situations which are not explicitly defined in the program.

Read also: Difference between AI and ML

Yield Improvement

Scrap has the potential to drag Yield down. The real-time analysis provides insights into defects leading to scrap. This, in turn, helps come up with mitigation methods for each type of defects. Checks and Controls can be put in place to prevent defects.

Inventory Management

Manufacturing organizations need to transfer minimum inventory to escape carrying cost and to reduce working capital necessities. Organizations resort to dissimilar forecasting and planning approaches to minimize on-hand inventory. There are still breaks which result in stock-outs and higher inventory levels in plants and warehouses. This helps effectively managing inventory levels in plants.

Supply Chain

Planning it involves multiple decision-making steps, e.g. which plant should produce a specified product and what should be the production size, whether to create or buy a product, where to keep manufacturing facilities, where to basic raw materials from, etc. Recent models rely typically on historical data, hence, many times, they fail to forecast new situations.

Featured article: Why ML and AI will redefine the software testing in 2019

Conclusion

Machine Learning grips the promise to simplify improvement in various areas in manufacturing. It’s not “One size turns all.” Organizations must be cautious in selecting the right Use Case and in choosing the accurate technology platform and System Integrator (SI). We also need to keep in mind that it takes a while before we can see tangible benefits.