The Impact of AI and Machine Learning in Manufacturing

The manufacturing industry is a vast, innovative space, continually optimizing to meet ever-increasing demands.

To address these challenges, companies around the globe are turning to Artificial Intelligence (AI) for assistance. Everything from computer vision for quality control to artificially-controlled metal-forming machinery is benefiting from the implementation of AI and machine learning algorithms.

The rise of AI in manufacturing is often cited as an integral part of the Fourth Industrial Revolution, which is characterized by the increasing use of automation, data exchange, and artificial intelligence on the factory floor. Like all disruptive technologies, the integration of AI comes with its own unique challenges. High costs of implementation, the need for specialized skill sets, and workforce displacement are all hurdles companies must overcome.

In this article, we’ll take a closer look at how AI is being implemented in various sectors of the manufacturing industry, the impact it’s having, and how manufacturing businesses can start implementing their own AI tactics.

A Brief History of AI in Manufacturing

While the widespread adoption of AI in manufacturing is currently underway, it has been slowly progressing behind the scenes for decades.

Going back to the 1960s and 1970s, the first applications of AI in manufacturing emerged with rule-based expert systems used for fault detection, diagnostics, and basic predictive maintenance. These early AI systems relied on if-then rules to mimic human decision-making. A rudimentary task for any modern software engineer, it nevertheless laid the groundwork for modern AI systems.

The 1980s saw the rise of robotics in production lines and marked a steady period of growth for AI in manufacturing. Robots powered by early AI algorithms began taking over repetitive tasks like welding and assembly. This decade also saw progression in computer vision with algorithms being introduced for machines to detect edges, corners, curves, and similar basic shapes.

The 1990s saw a continued steady progression in machine learning algorithms enabling improved predictive maintenance, optimized production scheduling, and efficient supply chain management. AI was beginning to play a part in streamlining the entire manufacturing life cycle, from raw materials to finished products.

With the computational power of the 21st century, AI is rapidly becoming widely accessible for manufacturing businesses. The Fourth Industrial Revolution concept has become synonymous with integrating intelligent autonomous systems on the factory floor. We have now reached a point where the impact of AI on the manufacturing industry is coming into the spotlight.

The Benefits of AI in Manufacturing

People are constantly finding new ways to apply and make money with AI and it can be applied to almost every aspect of manufacturing from design and conceptualization to management, delivery, and everything in between.

Here are some of the ways in which AI benefits the manufacturing lifecycle:

Improved Design

Generative design uses AI technology to generate designs based on constraints provided by the designer or engineer. Generative design can significantly reduce time and effort in the design and analysis phase, which is often an iterative process. Adding AI to the design phase not only reduces design time, it provides considerable cost savings by designing lighter, cheaper, more efficient structures.

Many Computer-Aided Design (CAD) companies have started to incorporate generative design into their software in various ways. Solidworks have incorporated an AI assistant which can anticipate the designer’s next moves and optimize the workflow. Autodesk have released a number of papers focused on the incorporation of deep learning into computer-aided design allowing things like text-to-shape generation, reverse engineering of 3D objects, and the automation of part pairing in assemblies. And Siemens NX has incorporated a number of AI features allowing predictive behavior and improved analysis.

Rapid Prototyping

The integration of AI and robotics into conventional manufacturing processes is enabling rapid prototyping and production. In some cases, parts that may have taken weeks or months are now able to be manufactured in days. This is made possible by the use of AI algorithms in programming and smart physical manufacturing processes.

Machina Labs has built a robotic craftsman which features 7-axis robots, autonomous loading and fixturing, and advanced AI process models and closed-loop controls which reduce some manufacturing processes to the click of a button. Machina labs’ innovative approach to metal forming allows complex-part prototyping that would have historically been prohibitively time consuming and expensive.

Image: Machina Labs – https://machinalabs.ai/

Alt text: Image showcasing Machina Labs’ “robotic craftsman” 7-axis forming robot creating a complex part from sheet metal.

Supply Chain Optimization

AI can analyze complex data from various sources to optimize supply chain operations. It can predict demand, manage inventory, and streamline logistics.

According to Mckinsey, successful implementation of AI-enabled supply-chain management has allowed early adopters to reduce logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent.

Quality Control

AI enables real-time quality control by quickly analyzing visual data from cameras and sensors on the production line. AI can also be implemented for checking parts are within tolerance, which can save significant man-hours when compared to traditional inspection protocols.

Computer vision AI is the technology that allows for autonomous inspection of parts for defects or tolerance failures and pattern recognition tools help detect anomalies that might get overlooked by a human inspector. These AI inspection systems allow manufacturers to address quality issues in real-time, reducing costs from faulty products and recalls.

Product Optimization

AI-powered product optimization combines traditional customer feedback with data-driven insights to close the design loop and allow for faster, more effective optimizations.

Sensors embedded in products collect usage data on how the product actually functions. AI analyzes this sensor data to identify weaknesses, defects, excessive wear, and opportunities for improvement. Customer feedback provides additional real-world insights. Combining sensor analytics and customer input, AI can automatically generate optimized redesigns or suggest improvements to a human designer.

This closed-loop of generating new designs based on real product usage creates a feedback cycle for constant product improvement.

Competitive Advantage

Implementing AI throughout the manufacturing lifecycle enables companies to achieve greater speed, efficiency, and quality. This allows them to rapidly innovate, meet demands, and exceed customer expectations.

Companies leveraging AI can design, prototype, and deliver high-quality customized products faster than competitors. AI-powered analytics and optimization drive intelligent decision making, improved yields, and reduced costs across operations.

The end result is optimized products to market quicker, the ability to respond rapidly to trends, and the ability to provide better service when compared to rivals not utilizing AI.

Challenges and Limitations

While the integration of AI into manufacturing can see many benefits, like all disruptive technologies, it also comes with certain challenges and limitations:

• Cost: The upfront costs of implementing AI and automation technologies is still quite prohibitive for most small and medium-sized businesses. This is expected to improve with time as the technology becomes more accessible.
• Skills Gap: A major barrier to AI implementation in manufacturing is the need for skilled workers that can work with AI and machine learning tools. This requires significant investment in training and upskilling of the workforce, or the hiring of external expertise.
• Security: With increased adoption of AI and machine learning technologies comes an increased vulnerability to cyber attacks.

Factories must ensure that their AI systems are secure, which can be complex and costly.

• Displacement: The rise of AI and automation could potentially lead to significant job displacement within the manufacturing sector. This raises important societal and ethical considerations.
• Interoperability: Ensuring seamless integration and interoperability between different AI systems and existing manufacturing infrastructure can be a challenging endeavor.
• Reliability: Ensuring consistent and reliable performance of AI systems is a key challenge in a manufacturing environment where errors can have costly implications.
• Accountability: When AI systems fail, determining the root cause can be a difficult task as oftentimes decisions made by AI systems are hard to clarify.

Implementation of AI in Manufacturing

Now is a great time for companies and businesses looking to implement AI to get a headstart on their competitors. Here are some ways businesses can get started with AI in manufacturing:

• CAD Software: Perhaps the easiest way to implement AI in your manufacturing process is by leveraging some of the readily available AI CAD tools. Check out your CAD suppliers’ website and see what’s available.
• Supply Chain: Implementing AI in your supply chain can help predict demand, manage inventory, and streamline logistics. Actually implementing AI into your supply chain can be a massive undertaking so careful planning is needed.
• Quality Control: Computer vision technology is becoming much more accessible and is a great tool for quality control on production lines and for finished parts. There are even off the shelf computer vision software packages like object detection pro that allow basic out of the box object detection.
• AI CNC Programming: Implementing AI for CNC programming is a relatively simple way to reduce programming time, optimize toolpaths, and minimize scrap rates.

To effectively implement AI in manufacturing, businesses must clearly identify the target problem or objective. Following this, they should assess, gather, and analyze relevant data, assess appropriate AI solutions, consider a pilot project for validation, and continually monitor and adjust the deployed AI system.

For successful implementation of AI into your manufacturing process, it must be well planned and methodical.

Moving Forward

As the accessibility of AI continues to increase, more businesses will start to benefit from increased efficiency, optimized development, and other enhanced capabilities. Despite the challenges involved, the rewards of AI implementation are worth the time and effort invested in their deployment.

For manufacturing businesses to remain competitive they must start looking to implement these tools in various areas of their company. By clearly defining objectives, assessing and gathering quality data, and closely monitoring deployed AI systems, businesses can expect to compete in the Fourth Industrial Revolution.

AI’s role in sustainable and competitive manufacturing must not be overlooked.