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It's a well-known fact that when you watch YouTube, Netflix, or Amazon Prime, they recommend related feeds to the one you just watched. But does anyone understand how it occurs? How does an app at first know what you want to stream? It's due to machine learning. You've probably heard of this term, and now we'll talk about it and the various ways it supports manufacturing.

These cutting-edge technologies, such as Machine Learning or Artificial Intelligence, can present some outstanding opportunities in the constantly evolving business world, where companies are always looking for new ways to maximize profits and are eager to provide the best goods or services. Let's explore the possible use cases of machine learning and Artificial Intelligence in manufacturing. Here, we listed several well-known and effective techniques applied in this industry.

What Exactly is Machine Learning in Manufacturing?

While the terms artificial intelligence and machine learning are frequently used interchangeably, machine learning is an application of AI that enables computers and machines to learn without being explicitly programmed by humans. Artificial intelligence refers to the larger concept of using computers and machines to simulate human thinking.

Manufacturers use two main machine learning models because they suit the production process and related activities well. These include. 

⦁ supervised machine learning

⦁ unsupervised machine learning 

The former entails using AI to extract patterns from sizable data sets with a specific goal in mind. This is especially helpful in estimating a machine's remaining useful life and the likelihood that a particular piece of equipment will fail.

On the other hand, unsupervised machine learning is concerned with finding patterns in data sets where the results are still unknown. Engineers and managers, for example, can use machine learning to find strange anomalies and defective parts in the manufacturing process.

This leads us to the real uses of machine learning in a manufacturing environment.

Applications of Machine Learning in Manufacturing

Machine learning has gained popularity recently in a variety of fields as a way to boost effectiveness and productivity.

"The machine learning market is predicted to expand rapidly, from $15.44 billion in 2021 to $209.91 billion by 2029."

All companies and organizations are attempting to use this technology while it is still in its infancy.

Data analytics, quality control, and other manufacturing-related applications have all been addressed by machine learning solutions. The top machine-learning applications in manufacturing operations that are transforming the industry are listed below.

Predictive Maintenance

One of the main applications of Machine Learning in manufacturing is predictive maintenance, which employs algorithms to predict the failure of critical equipment or components. Machine learning can analyze data from previous maintenance cycles to find patterns that can be used to anticipate equipment failures and when additional maintenance will be required. Then, using this information, maintenance appointments can be made before issues arise. As a result, manufacturers may be able to work on particular problems only when necessary—and in a highly focused manner—saving them both time and money.

This helps manufacturers by:

⦁ Reducing planned and unplanned downtime and costs significantly.

⦁ Providing technicians with the targeted tool, inspection, and repair needs.

⦁ Avoiding any secondary damage while performing repairs to increase the machinery's remaining useful life (RUL).

⦁ Reducing the number of technical repair personnel required.

Exploration of New Fields

Finding new fields is one of the most significant issues facing the mining, oil, and gas sectors. This process can be sped up with machine learning. Artificial intelligence creates models that accurately predict where to look for new gas or ore fields based on data from previous areas.

The business receives data from artificial intelligence and machine learning about:

⦁ Elements, chemicals, and mineral composition of the reservoir's components

⦁ 3D rock and void structure

⦁ properties of the reservoir fluid

⦁ the outcomes of computer modeling of reservoir characteristics and additional rock and reservoir fluid physical properties

⦁ Information about the outcomes of laboratory tests

Machine learning techniques analyze reservoir samples in digital labs run by oil companies. Algorithms help build a digital twin of the oil deposit by simulating the environment where the sample is taken from. It aids in evaluating mineral reserves and incorporates a customized approach to development. This enables businesses to look for new mineral deposits and multiply mineral extraction from a given deposit by 1.5 to 2.

For instance, these techniques increase General Electric (GE) production from a well, with the optimization exercise increasing output by 2-5%.

Digital Twins

Manufacturers can perform instant diagnostics, assess production processes, and forecast performance using a digital twin—a real-time digital representation of a physical object or even a process. Further, digital twins can offer complete design, production, and operational customization, which can assist manufacturers in modernizing their engineering practices.

In other words, manufacturing companies can test and optimize a virtual version of their processes and products before their products are built. The advantages of digital twins in manufacturing with ML capabilities include the following:

⦁ substantial cost savings

⦁ improved production line reliability

⦁ improved output and performance

⦁ lowered shop floor risks, better quality, and total customization

⦁ simplifying maintenance

Enhanced Robot Collaboration

With the development of artificial intelligence and machine learning, the era of mindless robots performing repetitive manufacturing tasks is coming to an end. To create agile production processes in the future, humans, and robots will need to collaborate.

In the majority of manufacturing sectors, using powerful mindless robots is still viewed as risky. However, the next generation of intelligent robots will make human workers' jobs much safer and easier to manage. They can be reassigned to any job, which increases their flexibility and effectiveness in factories. Additionally, they will be able to recognize patterns and learn how to adjust their response accordingly.

Cognitive Supply Chain Management

With the spread of IoT technologies, smart supply chains will soon completely transform how manufacturers conduct their operations. The first step on the ladder is automation, but entire supply chains may soon be "cognitive."

They can use AI and machine learning algorithms to automatically analyze datasets such as inbound and outbound shipments, inventory, consumer preferences, market trends, and weather forecasts to predict the best shipping conditions. The following areas will benefit most from cognitive supply chain management:

⦁ Control of the warehouse's inventory is made possible by deep learning-based computer vision systems, allowing for quick supply replenishment.

⦁ Utilizing time series analysis, feature engineering, and NLP techniques, ⦁ demand forecasting analyses consumer behavior and preferences.

⦁ Manufacturing companies can evaluate and assign the best routes for shipping goods using machine learning algorithms.

⦁ Machine and deep learning algorithms optimize transportation solutions to assess the effects on shipments and deliverables.

Predictive Quality and Yield

Process-based losses are getting harder for manufacturers to tolerate as demand rises with population growth. Businesses can protect their bottom line and maintain their competitiveness by identifying the underlying causes of losses related to quality, yield, energy efficiency, and other factors with the help of AI and machine learning. It utilizes machine learning-enabled Root Cause Analysis (RCA) and continuous, multivariate analysis through process-specific ML algorithms.

For the following reasons, ML and AI-driven RCA is a potent tool for combating process-based wastage and is far more effective than manual RCA.

⦁ Automated RCA uses machine learning algorithms to recognize patterns in new data and predict potential loss locations, anticipating problems before they arise.

⦁ Compared to manual RCA, this approach is entirely data-driven and unbiased.

⦁ The focus is solely on process optimization because it is unclouded by daily administrative tasks and other manual tasks carried out by process experts.

Benefits of Machine Learning in Manufacturing

Manufacturers stand to gain significantly from implementing machine learning in their production operations due to the various use cases. Among the most notable advantages of machine learning are the following:

Better Supply Chain Management

Manufacturers ensure that the factory has enough materials by properly deploying machine learning. This increases customer satisfaction because orders are completed and delivered on time.

Improved Quality Control

As previously stated, manufacturers use machine learning for predictive quality and maintenance. This keeps the production line equipment in perfect working order, lowering the likelihood of defective products coming off the line.

Better Employee Safety

Predictive maintenance ensures that employees are not harmed or endangered by operating faulty machinery. Furthermore, optimal space utilization and layout ensure that workers only work in safe zones.

Improved Product Design

Machine learning enables manufacturers to create in-demand products by considering their customers' needs. Businesses can offer market-ready products if they are confident that their products will solve a specific market pain point.

Lower Production Costs

Using machine learning in previously described use cases results in lower production costs. As a result, the business spends less on space utilization, utility costs, and broken equipment.

Top Companies Using Machine Learning in Manufacturing

Here are five innovative companies that are utilizing AI and machine learning to enhance manufacturing in the age of Industry 4.0.

Siemens

Location: Washington, DC

Siemens maximizes productivity in the manufacturing and industrial sectors by investigating AI and machine learning's potential. The business has focused on data analytics and industrial automation as two fields where AI can flourish. Companies now rely on Siemens' technologies to lower the cost of machinery, increase consistency through automation, and provide instant data for swiftly modifying workflows.

IBM

Location: Newyork, Newyork

IBM provides cloud, AI, and machine learning tools to help businesses reduce production times and costs. IBM Watson offers a variety of AI solutions, but its predictive intelligence and automation features have proven particularly popular with manufacturers. Businesses can now gather rich data insights to track market trends, foresee potential risks, and decide how to best implement automation technology.

Intel Corporation

Location: Santa Clara, California

By fusing information, operational, and AI technologies, Intel gives businesses a smart advantage. The business has used Machine learning in manufacturing to produce real-time data that enables teams to adjust workflows quickly. In addition, as Intel pushes for Industry 4.0, advancements in manufacturing, simulation, machine vision, and predictive maintenance have grown in importance.

Rockwell Automation

Location: Milwaukee, Wisconsin

By utilizing AI to strengthen systems, cybersecurity protocols, and other aspects of the manufacturing process, Rockwell Automation promotes "Smart Manufacturing." FactoryTalk Analytics LogixAI, a company product, employs predictive capabilities to anticipate potential setbacks, encourage proactive measures, and maintain product quality. The module combines machine learning with an effective modeling engine to foresee problems, forecast results, and streamline operations.

Mythic

Location: Austin, Texas

Mythic develops intelligent processing units for various gadgets, some of which are used in the manufacturing sector. To enable conversational robot and drone communication and real-time response and reaction, the company wants its IPUs to be powerful enough. According to Mythic, its IPUs support all widely used deep learning architectures, have practically limitless processing power and consume less energy than comparable products while offering more power.

Future of Machine Learning in Manufacturing

The manufacturing sector has a bright future for AI and machine learning. The manufacturing sector can grow and scale its businesses with the aid of AI and ML. The "Smart Manufacturing" revolution has already made it simpler than ever for companies to achieve this goal.

Many experts predict that machine learning and artificial intelligence will significantly impact factories and manufacturing.

"The market for artificial intelligence in manufacturing is expected to reach USD 16.3 billion by 2027, growing at a CAGR of 47.9% between 2022 and 2027, from its current value of USD 2.3 billion in 2022."

Data has become a valuable asset, and big data analytics has made it easier to collect and store.

The potential of AI and ML in the manufacturing sector is promising. To smoothly streamline the implementation process, businesses need the appropriate AI technology stack, tools, and a digital partner.

Globally significant businesses are already investing in leveraging AI and machine learning in manufacturing to improve every aspect of their manufacturing processes. The industry will see a substantial rise in smart manufacturing, which combines industrial AI and IoT, over the next three to five years.

Conclusion

Until this point, it has probably been evident that the manufacturing industry is technologically advanced. Manufacturers have long been among the first industries to adopt a variety of technologies, including automation, robotics, and sophisticated digital solutions. Therefore, it is no surprise that businesses are already investing in machine learning solutions to improve their operations.

Machine learning can assist factories in streamlining their entire production process and minimizing waste by utilizing the power of data. As machine learning advances and becomes more sophisticated, it will eventually play a more significant role in the manufacturing sector in the future.

Ready to revolutionize your manufacturing business with AI and ML? Tell us in the comments.