Applications of machine learning and artificial intelligence in manufacturing

Producing more products of higher quality at the lowest cost is an eternal goal of the manufacturing industry. The smart manufacturing revolution has enabled manufacturers to achieve this goal more successfully than ever before. One of the core technologies driving this wave of innovation is artificial intelligence. Data has become an extremely valuable resource, and the cost of acquiring and storing it is lower than ever. Today, thanks to the adoption of artificial intelligence technology (especially machine learning), more and more manufacturers are leveraging this data to significantly increase their revenue.

For many, this means significantly increasing productivity and throughput by eliminating the major causes of production losses and other related costs. Of course, deriving tangible business value from AI is often easier said than done. This is a complex technology with many different applications. How can manufacturers see through the hype and empty promises and invest in industrial AI that truly gives them a competitive advantage?

Keys to Artificial Intelligence and Machine Learning Success

It is impossible to miss the rapid rise of artificial intelligence technology, both in the context of manufacturing in general and within the manufacturing industry. As a result, expectations for AI tend to be very off-base, from comprehensive solutions to business problems to deep skepticism at the mere mention of AI.

Find the right use case

#But, as with any technology, the truth does lie somewhere in between. In the right environment, artificial intelligence can be very effective. Understanding these environments, and the AI ​​technologies applicable to them, is key to setting realistic business goals for AI applications.

Artificial intelligence is not a panacea. No solution will solve all or most of your problems. As a rule of thumb, AI works best when it is applied to solve a specific problem or a series of problems that are very closely related.

General AI is something to be wary of: If an AI vendor claims to do everything, they probably can’t do anything. Now back to the topic of artificial intelligence in manufacturing. There are many potential applications for artificial intelligence and machine learning in manufacturing, and each use case requires a unique type of artificial intelligence.

The following guide provides a simple and effective formula for selecting the right industrial artificial intelligence solution to address specific manufacturing challenges and goals.

The focus remains on machine learning and artificial intelligence because this is where the most exciting and impactful innovation is happening. This formula can be summarized in a simple diagram and methodology called the “Industrial AI Quadrant.”

Next generation optimization based on machine learning

The two main use cases of machine learning in manufacturing are predictive quality and yield and forecasting sexual maintenance.

(1) Perform maintenance only when necessary

Predictive maintenance is the more common of the two because maintenance issues and The associated issues can have significant costs, which is why it's now a fairly common target for manufacturers.

Predictive maintenance uses algorithms to predict the next failure of a component/machine/system rather than performing maintenance according to a predetermined schedule or using manually coded thresholds, alert rules and Configured SCADA system. Personnel can then be reminded to perform focused maintenance procedures to prevent failures, but not too early to waste unnecessary downtime.

In contrast, traditional manual and semi-manual methods do not take into account the more complex dynamic behavior patterns of machinery, or the scenario data related to the manufacturing process. For example, a sensor on a production machine might detect a sudden increase in temperature. A system based on static rules will not take into account the fact that the machine is being sterilized and will continue to trigger false positive alerts.

The advantages of predictive maintenance are many and can significantly reduce costs while in many cases eliminating the need for planned downtime.

By using machine learning algorithms to prevent failures, systems can continue to operate without unnecessary interruptions. When repairs are needed, it is very centralized, and technicians are informed of the parts that need to be inspected, repaired, and replaced; which tools to use, and which methods to follow.

Predictive maintenance can also extend the remaining useful life (RUL) of machines and equipment, as secondary damage can be prevented while requiring less labor to perform maintenance procedures.

(2) Find the hidden causes of losses

Predictive quality and yield (sometimes called predicted quality) is an aspect of industrial artificial intelligence A more advanced use case, it reveals the hidden causes of many long-term process-based production losses that manufacturers face every day. Examples include quality, yield, waste, throughput, energy efficiency, emissions, etc., essentially any loss caused by process inefficiency.

Predict quality and yield using continuous multivariate analysis, powered by machine learning algorithms uniquely trained to intimately understand each production process, automatically identifying the root causes of process-driven production losses reason.

Automated recommendations and alerts can then be generated to notify production teams and process engineers of impending issues and seamlessly share critical knowledge on how to prevent losses before they occur.

Reducing this type of loss has always been a difficult problem for all manufacturers. But in today's market, that mission is critical. On the one hand, consumer expectations are at an all-time high; global consumption habits are gradually changing, even as population growth continues.

According to multiple surveys, the global population will increase by 25% by 2050. On the other hand, consumers have never had so many choices, with almost every product imaginable available.

Recent surveys show that such a wealth of choice means consumers are increasingly likely to ditch their favorite brands for good.

In this context, manufacturers can no longer afford process inefficiencies and the resulting losses. Every loss in waste, yield, quality, or yield reduces their revenue.

The challenge many manufacturers face is that they end up hitting a bottleneck in process optimization. Some inefficiencies have no apparent cause and cannot be explained by process experts. This is where machine learning, especially automated root cause analysis, plays an important role.

Benefits of Artificial Intelligence and Machine Learning to the Manufacturing Industry

The introduction of artificial intelligence and machine learning represents a sea-changing change, bringing Many benefits come that go far beyond increased efficiency and open the door to new business opportunities.

Some of the direct benefits of machine learning in manufacturing include:

• Reducing common, painful process-driven losses such as yield, waste, quality and throughput
• Improve production capacity by optimizing production processes.
• Achieve large-scale growth and expansion of product lines through more optimized processes.
• Reduce costs through predictive maintenance, resulting in fewer maintenance activities, which means lower labor costs, reduced inventory and material waste. Predict remaining useful life (RUL). Understanding more about the behavior of machines and equipment can create conditions that improve performance while maintaining machine health. Predictive RUL eliminates "unpleasant surprises" that lead to unplanned downtime.
• Improve supply chain management through efficient inventory management and well-monitored and synchronized production processes.
• Improve quality control and provide actionable insights to continuously improve product quality.
• Improve human-machine collaboration, improve employee safety conditions, and improve overall efficiency.
• Consumer-centric manufacturing - able to respond quickly to changes in market demand.

To take full advantage of industrial AI/ML solutions, manufacturers need to know which AI solution is best suited to address the challenges they face.

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