5 Machine Learning Techniques Leading to Smart Manufacturing

5 Machine Learning Techniques Leading to Smart Manufacturing

Although artificial intelligence (AI), and its subsets, are beneficial in a lot of fields, it’d be difficult to find one that is more benefiting from them than the manufacturing industry. Machine learning (ML), a key component of manufacturing, is gaining a lot of attention from major companies all over the globe.

With the help of the Industrial Internet of Things, AI is bringing about the dawn of smart manufacturing. It can lower labor costs and reduce downtime as well as increase productivity and speed of production. The numbers speak for themselves; recent estimates predict that he smart manufacturing market is expected to grow at 12.5% annually between now and the next.

It makes perfect sense. Many businesses have already discovered the benefits of ML and are working with QA Testing Services to improve their experience. These are just a few examples of current implementations.

5 Machine Learning Techniques Leading to Smart Manufacturing

1. General process improvement

When you think about ML-based solutions, one of the first things you see is how they can be used to support daily manufacturing processes throughout the entire cycle. This technology allows manufacturers to identify all sorts of problems in their production processes, including bottlenecks and unprofitable lines.

Combining machine learning tools and the Industrial Internet of Things allows companies to take a deeper look at their supply chain management, inventory, assets, and logistics. These insights provide high-value insight that uncovers potential opportunities in both the manufacturing process and in packaging and distribution.

A great example of that was found in the German conglomerate Siemens, It has used neural networks to monitor its steel plants and search for potential problems that could be affecting their efficiency. It uses a combination sensor system in its equipment, and its own smart cloud (called mind sphere), to determine its location. Siemens can monitor, record, and analyze every step involved in the manufacturing process. This dynamic is known as Industry 4.0 by some, a hallmark of smarter manufacturing.

Also read: How to Start A Manufacturing Business

2. Product development

The product development phase is one of the most popular uses of machine learning. This is because product development, including the planning and design of new products and improvements to existing products, requires a lot of information to get the best results.

ML solutions are able to gather consumer data and analyze it in order to understand customer needs, discover hidden requirements, and identify new business opportunities. All of this leads to better products in the existing catalog and new revenue streams. Machine learning is particularly useful in reducing risks associated with developing new products. The insights it provides feed into the planning stage to make more informed decisions.

Coca-Cola is one of the most recognized brands in the world. They use machine learning to develop new products. The company’s use of ML was responsible for the launch of Cherry Sprite. Interactive soda fountain dispensaries were used by the company to allow customers to add different flavors and colors to its base drinks. Coca-Cola used machine learning to determine the most popular combinations and gathered the data. What was the result? The identification of a large enough market for introducing a new beverage to the nation.

3. Quality control

Machine learning, when used well, can increase product quality by up to 35%, particularly in discrete manufacturing industries. This can be done in two ways by machine learning. First, ML can detect anomalies in products and packaging. Companies can prevent defective products from reaching the marketplace by conducting a thorough inspection of manufactured products. Studies show that there is a 90% improvement in defect detection with human inspections.

The quality of the manufacturing process can be improved through IoT devices, ML applications businesses can evaluate the performance and availability of every piece of equipment that is used in manufacturing. This allows predictive maintenance which estimates the best time to attend to a specific piece of equipment in order to prolong its life and prevent costly downtime.

General Electric has been a is one of the biggest investors in quality control. Particularly in all things related to predictive maintenance. It already has its ML-based tools deployed in more than a hundred thousand assets across its business units and customers. This includes the power generation, transportation, and aerospace industries. Its systems detect anomalies in manufacturing lines early and offer prognostications with long-term estimates of behavior and life.

4. Security

All of these machine-learning solutions depend on apps, Operating Systems, Networks, Cloud, and On-Premise Platforms, Security of Mobile Apps, devices, and data are essential for modern manufacturers. Machine learning is able to provide an answer with the Zero Trust Security Framework (ZTS). This technology restricts user access to digital information and valuable digital access.

Machine learning can thus be used to analyze how users access certain information, what applications they use, and how they connect to it. Machine learning can help define a strong perimeter around digital assets. What and who does it not, but you can also spot anomalies that could quickly trigger warnings and actions.

The use of zero-trust frameworks and architectures is not a common practice in the manufacturing sector. Only 60% of respondents to a survey said that they are working on or plan to implement Zero Trust strategies into their digital landscapes.

Also read: Warehouse Automation: What It Is, Types and Benefits

5. Robots

Robots are becoming smarter thanks to machine learning. Robots can perform routine tasks that would be difficult or even dangerous for humans by using artificial intelligence. Because of their ML capabilities, these robots can tackle more complex processes than they were previously able to.

KUKA, a German-owned manufacturing company that is Chinese-owned, has set out to achieve this goal with its industrial robots. Its goal is robots that work with humans and can act as their collaborators. In that spirit, the company has brought its robot LBR Iowa into the fold. The robot’s high-performance sensors allow it to do complex tasks alongside humans and even learn from them how to increase their productivity.

KUKA uses robots in its own factories. However, there are many other major manufacturers who do the same. BMW, the iconic auto manufacturer, is one of its largest customers and one of the companies that have already discovered that robots can reduce human-related errors and increase productivity, as well as add value to the manufacturing chain.

Conclusion

It is obvious that the manufacturing sector is technically advanced. Manufacturers have been pioneers in the adoption of many technologies over the years, including robotics, automation, and digital solutions. It’s not surprising that machine learning solutions are being used by manufacturers all over the globe to improve their processes.

Already, the results of machine learning in manufacturing are evident. Machine learning is proving to be a great tool for manufacturing. It has many benefits, including increased productivity, decreased equipment failures, improved distribution, and the introduction of enhanced products. While we are still far from widespread adoption of these solutions in manufacturing, many companies are already leading the way to smarter ways of making the products we use

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