In how many ways has AI transformed the manufacturing industry 39526 1 - In how many ways has AI transformed the manufacturing industry?
February 26, 2022

The manufacturing industry is being driven by artificial intelligence and machine learning at a rapid pace nowadays.

Over 60% of manufacturing organizations have already embraced AI technology to boost operational efficiency, decrease downtime, and provide high-quality goods that suit distinct consumer expectations.

So it’s safe to say that industry leaders are smart to invest in AI, as it is transforming manufacturing in profound ways. AI will have a significant influence on manufacturing in the future, as can be seen by looking at well-known Industry 4.0 models. However, don’t take our word for it, let the numbers in the following paragraph speak.

The Future of AI in the Manufacturing Industry

By 2026, the market for artificial intelligence in manufacturing is expected to reach $16.7 billion, with a CAGR of 57.2 percent.

Artificial intelligence has numerous and potentially game-changing uses in the manufacturing industry. It has revolutionized the way goods are created and manufactured, providing actionable insights into every stage of the design and production process.

In this way, problems may be found and fixed quickly, resulting in high-quality final goods.

In the following parts of this article, let’s explore the ways AI will be influencing the manufacturing industry.

Smart Maintenance and Industry 4.0

Equipment and machinery maintenance poses a significant cost in manufacturing. It also has a direct influence on the financial health of any organization that relies heavily on its assets. Studies suggest that unexpected downtime costs manufacturers an estimated $50 billion per year, and that asset failure is responsible for 42 percent.

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The ability to forecast when a part, equipment, or system will break is a valuable asset for manufacturers, which is why predictive maintenance has become a must-have option.

To make predictions about when a piece of equipment would fail, predictive maintenance makes use of cutting-edge AI technologies such as machine learning and artificial neural networks.

This reduces unexpected downtime and extends the Remaining Useful Life (RUL) of manufacturing machinery and equipment.

Even if maintenance is inevitable, professionals are prepared in advance by being given information on which parts need to be inspected, as well as the equipment and procedures they should employ.

 

The Rising of Quality 4.0

Today’s short time to market requirements and rising product complexity are making it increasingly difficult for manufacturing organizations to maintain high quality and meet quality rules and standards.

On the other hand, buyers now want flawless products, which is driving manufacturers to improve their quality standards. They must do so while also realizing the harm that high defect rates and product recalls can cause to a company’s reputation.

Artificial intelligence algorithms are being used in Quality 4.0 to alert manufacturing teams of new production flaws that are likely to result in product quality problems. These include variances from recipes, machine anomalies, changes in raw materials, and more.

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Quality can be maintained by addressing these concerns early on.

Quality 4.0 also gives producers the ability to gather information on how their goods are being used and how well they are performing in the field. Product development teams can use this information to make both strategic and tactical engineering choices.

Collaboration Between Humans and Robots

According to the International Federation of Robotics, more than 1.3 million industrial robots will be employed in industries throughout the globe by the end of 2018. Robots are expected to take over more and more employment shortly, thus employees will be taught to take on more complex roles in design and programming.

As industrial robots increasingly coexist with human employees on the factory floor, the human-robot collaboration will need to be effective while also ensuring worker safety.

Robots will be able to do more cognitive activities and make autonomous judgments based on real-time environmental data as a result of advancements in AI, which will further optimize operations.

Using Generative Design To Create Better Goods

Moreover, AI is transforming the way we create goods. One approach is to provide an AI algorithm with a precise brief.

Information on limitations and parameters, such as material kinds, production processes, budget restraints, and deadline restrictions, might be included in the brief. Before zeroing in on a small subset of the best options, the algorithm investigates every conceivable arrangement.

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Additional insights into which designs function best may be gained by testing the offered solutions using machine learning. The procedure can be done as many times as necessary to come up with the best possible design.

Artificial Intelligence (AI) algorithms are entirely impartial since they don’t start from a human designer’s “logical” assumptions. Every assumption is validated against a wide range of production situations and circumstances, so nothing is assumed at face value.

 

Keeping Up With an Ever-Changing Market

Artificial intelligence in manufacturing is a key component in the Industry 4.0 revolution, and its applications are not restricted to the factory floor. AI algorithms may also be used to optimize supply chains, helping organizations predict changes in the market. This shifts management from a reactive/response attitude to a strategic one, which is a tremendous advantage.

Artificial intelligence (AI) algorithms create market demand estimates by searching for patterns that relate to geographic location, socioeconomic and macroeconomic aspects, weather patterns, political status, and consumer behavior, among other things.

This data is critical to manufacturers because it enables them to better manage their workforce, inventory, energy use, and raw material supply.

AI Fused With Digital Twins

Essentially, a digital twin is a replica of a physical item. Using AI and digital twins, manufacturers could gain a better knowledge of their products and experiment with future actions that could improve asset performance. Manufacturers can employ digital twins before producing the physical version of a product. Businesses could use this program to collect data from the virtual twin and use that data to improve the actual product.

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Additionally, manufacturers can use digital twins to create a variety of product configurations. Customers may now choose a product based on its performance, rather than on the design’s aesthetics. Furthermore, the use of digital twins to monitor and evaluate the manufacturing process might help uncover potential quality concerns or areas where the product’s performance falls short of expectations.

Finally, digital twins give producers a comprehensive perspective of the resources they use and the ability to automate the restocking process, which is a huge benefit.

 

Image by Wizata (Screenshot)

The Manufacturing Industry Will Continue To Be Transformed by Industrial AI

Using artificial intelligence in manufacturing is a natural progression for technology. AI is having a big impact on Industry 4.0, even if the revolution has only begun. AI has the potential to revolutionize the manufacturing and processing of products and materials.

Author bio

Rick Seidl is a digital marketing specialist with a bachelor’s degree in Digital Media and

Communications, based in Portland, Oregon. With a burning passion for digital marketing, social media, small business development, and establishing its presence in a digital world, he is currently quenching his thirst through writing about digital marketing and business strategies for Find Digital Agency.

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