Automating Manufacturing Industry with AI

The manufacturing industry has changed fundamentally in the past few years. This change is often referred to as the Fourth Industrial Revolution or Industry 4.0. This new wave of technology is marked by the integration of digital technologies, including digital twin, IoT, and artificial intelligence (AI), which play the leading role. Over the last decade, AI has emerged as a driving force across numerous sectors, and industrial manufacturing has become a leader in adopting this technology. By shifting from traditional practices to AI-enhanced processes, manufacturing companies are improving efficiency and setting new safety standards. A recent study shared that 52% of manufacturing firms will use generative AI in the next year. This blog explores the many benefits of AI in manufacturing.

AI in Manufacturing

Manufacturing aims to produce consistent, high-quality products at the lowest price and fastest speed. AI facilitates this by interpreting vast amounts of data, which can be used in machine learning algorithms to optimize operations. The main applications of AI in manufacturing include preventative maintenance, quality control, supply chain optimization, robotic automation, and worker safety.

Benefits of AI in Manufacturing

The manufacturing industry faces several significant challenges, including labor shortages, quality control issues, and the need for effective maintenance. AI is central to addressing these challenges in the following ways:

Workforce Augmentation

Enhancing human capabilities with workforce augmentation—transitioning to a digital manufacturing environment using AI, digital twins, IoT, and automated dashboards—can address labor shortages. This does not mean replacing humans. Instead, it allows menial tasks to be automated so that they take less time and workers can skill up. A study by Boston Consulting Group found that AI schedulers can cut manual team scheduling in half.

Additionally, AI-enabled robots can undertake tasks that are too dangerous for humans, such as handling toxic substances or operating under extreme conditions, thereby creating a safer manufacturing environment.

In addition, industrial robots are now easier to program and teach thanks to low-code and no-code tools. Workers whose expertise lies in performing manual tasks, such as welding, can now transfer this knowledge to the robot and operate and teach it themselves—a kind of knowledge transfer between humans and robots. In this way, one person can train several robots in their field of knowledge and thus compensate for the shortage of skilled labor on the market with robots.

Production Efficiency and Quality Control

As mentioned earlier, manufacturing aims to produce a high-quality product at the fastest speed and the lowest cost. Data fed into machine learning algorithms can predict machine failures, schedule maintenance, and adjust operations for peak efficiency. Proactive management minimizes manufacturing downtime while maximizing product output. With increased production activity, there is also a need for more accurate quality control. Integrating sensors and cameras allows robots to control the quality of products. Advanced image recognition technologies powered by AI scrutinize components at a level of detail far beyond human capability. This raises the bar for product quality and ensures uniformity across extensive production volumes.

Facilitating Customization and Flexibility

AI allows manufacturing plants to switch between products easily. This adaptability meets the growing consumer demand for customization, thereby maintaining mass production's speed and cost efficiency.

AI also allows for dynamic production planning by analyzing market trends, customer trends, and demand forecasts in real-time, allowing manufacturers to adjust their production schedules and inventory levels proactively.

Adding Predictive Maintenance

A proactive approach to maintenance can provide substantial cost savings down the line. McKinsey Global Institute reports that implementing predictive maintenance throughout manufacturing will result in $240-$627 billion in cost savings across the industry.

Predictive maintenance utilizes a real-time, data-driven approach that collects and analyzes data, enabling it to predict when a machine will likely fail. AI’s power to process massive amounts of data gives it an edge in identifying irregularities to prevent breakdowns.

A great example of visualizing predictive maintenance is digital twin technology. This term refers to a virtual replica of a physical robot, existing within a simulated environment. It gathers real-time data from its physical counterpart to mimic and predict the robot's behavior in a digital setting. By connecting the digital twin with sensor data from the physical robot, AI can analyze the data, find irregularities, and predict potential issues.

Conclusion

The future of manufacturing is undeniably intertwined with advancements in AI technology. Digital manufacturing relies heavily on collecting and interpreting large amounts of data. One of the biggest hurdles in implementing AI is knowing whether you need more or less data and where to focus data-collecting efforts. Extracting data from older machines also presents challenges.

As this field evolves, staying informed and adaptable will be vital in leveraging AI's full potential. Whether you're just starting your journey in AI or looking to deepen your existing practices, the opportunity to transform through technology has never been more accessible.

If you want to learn more about how Wandelbots uses AI or digital twin technology, contact us today.