In the era of Industry 4.0, manufacturing companies are relying more on digital technologies like Big Data analytics, Artificial Intelligence (AI), and the Internet of Things (IoT). To remain competitive in today’s complex business environment, manufacturing companies recognize that they must have business agility, flexibility, and resilience.

Essentially, in Industry 4.0, digital technologies improve industrial productivity and elevate the quality of delivered products through connected and smart systems. With the advancement of digital technologies, companies can also implement predictive maintenance in their facilities. As a result, quality management in Industry 4.0 is a critical aspect of any manufacturing firm. 

Loss of optimum quality can seriously damage the company’s brand value and reputation. On the positive side, quality improvements can enhance profitability and differentiate the company from its closest competitors. Despite the benefits, only 16% of organizations were previously reported to have started implementing quality management in their Industry 4.0 initiatives.

In the next section, let’s discuss how quality management facilitates predictive maintenance in manufacturing companies.

How Quality Management Facilitates Predictive Maintenance in Industry 4.0

In simple language, predictive maintenance is about leveraging ongoing manufacturing data to predict and prevent equipment failures. Also known as condition-based maintenance, predictive maintenance is a proactive mode of addressing maintenance work instead of following a strict schedule.

Predictive insights can also elevate product quality. Before the advent of Industry 4.0, quality management was associated mainly with the production phase of manufacturing. Now, its focus has shifted to the entire product development phase. With this focus shift, manufacturers can easily reduce the number of defective products. 

Embedded with Industry 4.0 sensors, connected products facilitate communication between the customer and the product designers. Similarly, IoT-enabled sensors (attached to products and equipment) can generate large volumes of real-time data about their quality. When compared to product design data, this collected data can proactively predict the failure rate of the finished product.

To minimize production losses caused due to machine downtime, predictive maintenance is a valuable asset for manufacturers to build smart factories in the future. Here are some successful case studies of companies leveraging predictive maintenance in the industry 4.0 era:

1. Rolls Royce

This aircraft engine manufacturer leverages Big Data processes as part of their product design, manufacturing, and after-sales. For instance, the company has been deploying smart nanobots to execute predictive maintenance operations and inspections. The company also collects and analyzes data from its engine design using connected IoT sensors.

2. Hyundai Motors

Among the largest automobile manufacturers, Hyundai Motors has developed an AI-powered car diagnosis system that leverages AI technology to detect any faults in the vehicle. The system utilizes data related to over 800 engine faults to improve quality through predictive maintenance.

Additionally, the company has designed a Knock Sensor Detection System to determine any abnormality in car engines based on their vibrations.

Next, let’s discuss some of the applications of Computer Vision (CV) technology in achieving quality management in Industry 4.0.

4 Applications of Computer Vision in Quality Management in Industry 4.0  

In the era of Industry 4.0, Computer Vision or CV technology is the “eye” of the smart manufacturing process. It is now an integral part of every automated manufacturing environment.

Here are four common applications of CV technology in quality management:

1. Product Assembly

As part of their Industry 4.0 initiative, production facilities can use CV applications to fully automate their product assemblies and management. Based on approved product design, computer vision can guide assembly workers and industrial robots to work according to product specifications. 

Effectively, through continuous monitoring of the assembly line, CV-enabled systems play a significant role in improving product quality and eliminating product defects.

2. Product Defects 

Through manual or human intervention, manufacturing companies are unable to create 100% defect-free products. This requires them to monitor product defects at a microscopic level. Once products are released to the customers, defects can cause customer dissatisfaction, besides increasing the cost of product recalls and production.

CV-powered applications are more cost-effective than the expenses incurred due to product defects. They can collect real-time data from connected cameras and machine learning algorithms. With real-time data, organizations can also trace and rectify any faults in the production line.

2. Predictive Maintenance of Equipment

Most production in manufacturing facilities happens at extremely high temperatures and in a corrosive environment. This is bound to create constant wear-and-tear of machines and other equipment. Predictive maintenance can ensure the health of equipment for any defects and notify maintenance engineers to perform maintenance work on time.

Computer vision can constantly monitor production equipment based on predetermined metrics and automatically report any deviations to the maintenance team.

4. Efficient Packaging 

As part of quality management, manufacturers must also adhere to high-quality packaging standards — for example, counting and packaging the right number of products into a container. However, with human vision and intervention, manufacturers cannot guarantee adherence to packaging standards. For example, human operators can overlook printing defects like missing quality stamps or production dates on the packaged products.

CV-enabled systems can accurately count the number of products being packaged or if all packaging standards are being followed in the manufacturing line. Another use case of CV applications is to detect any damaged packaging, which can lead to product damage before delivery to the customer.

Conclusion

As an Industry 4.0 technology, Computer Vision can significantly improve quality management in manufacturing companies. Along with quality assurance, CV-enabled systems can drive benefits like improving predictive maintenance, reducing product defects, and improving packaging standards. 

With its CV-based automation platform, KamerAI leverages the power of connected cameras in the production facility to inspect product quality. In the manufacturing domain, KamerAI solutions help in the following:

• Maintaining safety standards
• Conducting product inspections to meet quality standards
• Proactively performing predictive maintenance.
• Identifying and eliminating product defects.
• Preventing any trespassing in manufacturing facilities.

Explore how our Computer Vision solution can nurture quality management in your enterprise. Get in touch with our KamerAI consultant!