AI isn’t just for generating images or processing large datasets – it has tangible, real-world applications for the manufacturing industry.

By optimising supply chains, enhancing quality control, and automating complex tasks, AI is making the marketplace more competitive and tackling some of the industry’s biggest pain points in transformative ways. The integration of AI technologies such as computer vision, machine learning, and collaborative robots (cobots) brings new levels of efficiency and precision. This not only benefits manufacturers but also leads to higher quality products and faster delivery times for consumers. In the sections that follow, we’ll look at some real-world examples of how manufacturing is utilising AI today.

1. Predictive Maintenance in CNC Machines

Predictive maintenance can leverage AI to improve the reliability and efficiency of CNC (Computer Numerical Control) machines, which are critical to precision manufacturing. By forecasting and preempting machine failures, manufacturers can sidestep expensive downtime and maintain seamless, uninterrupted operations.

CNC machines are outfitted with an array of sensors that continuously track ‘health’ parameters such as vibration, temperature, and acoustic signals. These intelligent sensors gather an immense volume of data, transmitting it to an AI-driven analytics platform for real-time processing and analysis.

Once the models detect these anomalies, they generate alerts, allowing maintenance teams to intervene before a breakdown occurs. The AI system not only predicts when a failure is likely to happen but also provides insights into the specific component or subsystem that may be at risk. This level of detail enables targeted maintenance, reducing the need for extensive inspections and repairs.

Predictive maintenance

2. Quality Control in Electronics Manufacturing

Quality control (QC) is the final inspection step in the electronics manufacturing process, where the assembled products are thoroughly checked for defects before packaging and shipment.

If defects slip through the quality control process, they can lead to product failures, customer dissatisfaction, increased returns and warranty claims, and significant financial losses.

Computer vision systems have been around for decades to aid with QC and inspect printed circuit boards (PCBs) for defects such as soldering issues, component misplacements, and trace breaks. However, with the integration of AI, their capabilities have expanded. A specialised convolutional neural network (CNN) processes the images in real time, having been trained to recognise the distinctive characteristics of both defective and non-defective products.

AI reduces the reliance on costly manual inspections, speeds up the quality control process, and allows only products meeting stringent quality standards to ship to customers.

CNN process

3. Injection Moulding Process Optimization

Injection moulding is a widely used manufacturing process for producing plastic parts. However, achieving consistent quality can be challenging because of numerous variables, such as temperature, pressure, and cooling time.

AI proves particularly useful during the initial setup of the moulding process for new parts, where production efficiency can vary based on the skill of the process engineer. LS Mtron’s AI-based Smart Solution 4.0 reduces initial process stabilisation time by an average of 23%. This system mimics the expertise of skilled engineers to recommend optimal settings, thereby reducing reliance on highly skilled personnel. This facilitates the transfer of best practices across company plants without the need for onsite experts.

Smart Solution 4.0 also includes an AI Weight Control System that maintains consistent part weight by automatically adjusting process parameters in response to variations caused by material batch differences or environmental changes. Users can choose to store processing data locally or share it on the cloud to leverage collective AI insights.

Machine learning models analyse historical and real-time process data to identify the optimal settings that minimise defects like warping. By dynamically adjusting these parameters during production, AI ensures consistent product quality and reduces waste, leading to more efficient and cost-effective manufacturing.

Injection moulding process optimisation flow chart

4. Supply Chain Optimisation in Automotive Manufacturing

Effective supply chain management is the backbone of the automotive industry, where timely vehicle production and delivery are necessary to meet market demands and maintain competitiveness. More and more companies are starting to use AI for logistics to enhance their supply chain operations.

Predictive analytics models process vast amounts of data from various sources, such as market trends, sales data, and supplier performance. This allows manufacturers to adjust procurement and production schedules proactively, to make sure the right parts are always available when needed, and to reduce the risk of overstocking or stockouts.

For example, AI can calculate the optimal inventory levels for critical components such as batteries and electric motors. It uses machine learning algorithms to predict lead times and adjust reorder points dynamically, while real-time supplier performance monitoring helps identify the most reliable suppliers, reducing the risk of delays and defects.

5. Robotic Process Automation (RPA) for Assembly Lines

Robotic Process Automation (RPA) powered by AI is transforming assembly lines by automating repetitive and labour-intensive tasks. AI-driven robotic arms can perform complex tasks such as welding, painting, palletising, and part assembly with high precision.

For example, COREMATIC’s Collaborative Robots (cobots) manufactured by OMRON are designed to increase throughput for a wide variety of applications across numerous industries. The TM Series cobots can improve throughput and consistency in repetitive or complex assembly tasks, including part joining, insertion, and tool changing, all while safely working alongside human operators thanks to LiDAR curtains that detect obstacles and prevent collisions.

In packaging and pick-and-place operations, COREMATIC palletising cobots equipped with built-in vision, lighting, and customisable grippers can inspect, sort, and pick up products from conveyor belts before placing them onto pallets. This automation eliminates the need for workers to handle the exhausting task of palletising, allowing them to focus on tasks that require human skills and attention. The ability to palletise 24/7 also reduces backup that can limit production, streamlining end-of-line processes.

While investing in AI-powered cobots can be costly upfront, the long-term savings are impressive. Robots can work around the clock without breaks – boosting production output and efficiency. With COREMATIC’s OMRON cobots, companies often see a return on investment within 18 months.

A diagram of a collaborative robot arm

6. Traceability and Safety in Food Processing

Product safety and quality in food processing rely on effective traceability from farm to table. In fact, the FDA requires companies to maintain detailed records of food items as they move through the supply chain. Meeting these standards is easier than ever, thanks to AI and IoT sensors.

These sensors, attached to containers, pallets, and even individual products, collect crucial information such as temperature, humidity, and location throughout the supply chain. AI processes this data in real-time, identifying patterns and anomalies that could indicate potential issues. For example, if a refrigerated container’s temperature rises above the safe threshold of 40°F (4°C), the AI system can immediately alert the relevant personnel, prompting corrective action to prevent spoilage or have the product pulled.

AI can also be used within the plant to monitor for contaminants. Advanced machine learning algorithms analyse data from various sensors and inspection systems to detect foreign materials, like metal, glass, or plastic, and even microbial contamination. Continuous monitoring of production lines allows for contaminated batches to be identified and isolated quickly. A single recall can cost a company upwards of $10 million, making early detection and isolation of contaminants not just a safety measure but a significant cost-saving strategy.

A close up of a basket of cucumbers

A Future of Endless Possibilities

While AI in manufacturing is still in its infancy, its potential is immense and constantly growing. As these technologies become more sophisticated and deeply integrated into production processes, we can anticipate even greater strides in efficiency, precision, and reliability. The future holds exciting possibilities where AI transforms manufacturing practices and paves the way for innovative production methods.

Check out COREMATIC today to explore how AI can solve your manufacturing pain points and drive your business forward.