SkyVision Platform: Achieving Precise Detection of Missing Assembly Steps

In industrial manufacturing, the precise execution of assembly processes is a crucial step in ensuring product quality. However, traditional detection methods face numerous challenges when dealing with the issue of missing assembly steps. WeLinkirt's SkyVision platform offers an effective solution to this problem with its advanced technology.

In today's highly competitive industrial manufacturing field, product quality and production efficiency are the key factors for enterprises to gain a foothold in the market. Among them, the assembly process, as an important part of industrial production, its accuracy is directly related to the performance and reliability of products. The product assembly line of a leading industrial manufacturing company is mainly responsible for the assembly process of complex mechanical equipment. These mechanical equipment are composed of multiple components, and each assembly step needs to be strictly carried out in accordance with the standard operating procedure (SOP). Once there is a missing step, the product performance may decline or even fail to work properly. Therefore, ensuring the precise execution of the assembly process is crucial for guaranteeing product quality.

Pain Points: Why It's Difficult

Traditional assembly process inspection mainly relies on manual patrol inspection, but this method has great limitations. In terms of the missed detection rate, the missed detection rate of manual inspection is as high as 12%, and it is difficult to ensure 100% full inspection. This is because in the complex assembly process, some subtle missing steps are easily overlooked. For example, the omission of some small components or small errors in the assembly sequence are difficult for humans to accurately identify in a short time. Moreover, manual inspection is affected by factors such as the experience and attention of inspectors, and different inspectors may get different inspection results, resulting in missed detection situations from time to time.

In terms of the false alarm rate, the false alarm rate of manual inspection is about 15%, which increases the unnecessary re-inspection workload. Due to the strong subjectivity of manual judgment, some normal assembly situations may be misjudged as missing steps, resulting in the need for additional inspection and verification of products, wasting a lot of time and labor costs. In addition, high labor costs are also a major pain point of traditional detection methods. With the continuous increase of labor costs, enterprises need to pay high salaries to hire inspectors. And when changing the production line model, the inspectors need to be retrained, and the model-changing time is as long as 30 minutes, which seriously affects the production efficiency. This is because different product assembly processes may have differences, and inspectors need to re-learn and master new detection standards and methods. This process is not only time-consuming and labor-intensive, but also prone to human errors.

With the development of technologies such as intelligent security, video surveillance, and behavior recognition, traditional manual detection methods can no longer meet the industry's requirements for precise monitoring and compliance of the production process. In modern industrial production, the requirements for product quality are getting higher and higher, and real-time and precise detection of the assembly process is needed. However, traditional manual detection methods cannot achieve this and cannot timely discover and correct missing steps in the assembly process, thus affecting product quality and production efficiency.

Technical Principle

WeLinkirt's SkyVision zero-code video surveillance AI platform uses advanced computer vision algorithms and deep learning technologies. Based on the semantic understanding ability of the DaoAI World model, it can accurately identify and analyze various elements in the assembly scene. The platform obtains real-time video images through the monitoring cameras deployed on - site and uses convolutional neural networks (CNNs) to extract and classify features of the assembly actions and component states in the images.

Specifically, the model can learn the standard features of each assembly step. When there is a situation that does not conform to the standard, such as a component not being installed or an incorrect assembly sequence, the platform can quickly identify and judge it as a missing step event. Compared with traditional methods, this method has higher accuracy and efficiency. Traditional manual detection mainly relies on human vision and experience and is easily affected by subjective factors. The SkyVision platform can objectively and accurately detect the assembly process through computer algorithms and deep learning technologies, greatly improving the detection accuracy. In addition, the edge box of the platform has real-time computing capabilities and can process and analyze video data locally, achieving 100% local data without leaving the factory, which ensures data security and improves detection efficiency. Using the zero-code programming technology, on - site personnel can complete the training of their own models within hours without professional programming knowledge, greatly shortening the model deployment time.

Typical Application Scenarios

• Missing component detection: In the process of mechanical equipment assembly, the omission of some small components may affect the normal use of the product. The SkyVision platform can accurately determine whether there is a missing component situation through the identification and analysis of components in the monitoring image. The difficulty lies in that small components may account for a small proportion in the image, and the platform needs to have high image recognition accuracy.
• Incorrect assembly sequence detection: Each assembly step has its specific sequence. Once the sequence is incorrect, the product may not be assembled normally. The platform can detect the incorrect assembly sequence in time by learning the standard assembly sequence and analyzing the assembly actions in the real-time image. The difficulty lies in accurately distinguishing the order of different assembly actions, which requires in - depth understanding and analysis of the assembly process.
• Bolt tightening degree detection: In the assembly process, the tightening degree of bolts is directly related to the stability and safety of the product. The SkyVision platform can judge whether the bolts are tightened by analyzing the image features of the bolts. The difficulty lies in that the manifestation of the bolt tightening degree in the image may not be obvious, and the platform needs to have strong feature extraction and analysis capabilities.
• Gasket installation detection: Whether the gasket is installed correctly will affect the sealing performance of the product. The platform can detect whether the gasket is installed correctly by identifying features such as the shape and position of the gasket. The difficulty lies in that the shape and color of the gasket may be similar to the surrounding environment, and the platform needs to have good image segmentation and recognition capabilities.

Implementation Case

A large industrial manufacturing enterprise has multiple product assembly lines and a large production scale. Before adopting WeLinkirt's SkyVision platform, the enterprise had been using the traditional manual detection method and faced problems such as high missed detection rate, high false alarm rate, and long model-changing time. During the implementation process, WeLinkirt's technical team first deployed high-definition monitoring cameras at key positions on the production line to ensure that every detail of the assembly process could be fully captured. Then, according to the actual SOP process, on - site engineers used the zero-code function of the SkyVision platform to complete the training of their own detection models within hours.

The application of the SkyVision platform has brought significant benefits to the enterprise, achieving precise detection of missing assembly steps.

Before the implementation, the missed detection rate of the enterprise's assembly process was as high as 12%, the false alarm rate was about 15%, and the model-changing time of the production line was as long as 30 minutes. After the implementation, the detection rate of missing assembly steps reached 98.5%, and the missed detection rate was reduced to <1.5%, greatly improving the stability of product quality. At the same time, the false alarm rate was reduced by -75%, reducing the unnecessary re-inspection workload. In terms of production line model-changing, the model-changing time was shortened from the original 30 minutes to 5 minutes, significantly improving production efficiency.

WeLinkirt's Solution and Products

Centered on the SkyVision zero-code video surveillance AI platform, WeLinkirt provides a complete solution for detecting missing assembly steps. First, high-definition monitoring cameras are deployed at key positions on the production line to ensure that every detail of the assembly process can be fully captured. Then, using the zero-code function of the SkyVision platform, on - site engineers can complete the training of their own detection models within hours according to the actual SOP process. The behavior/event recognition function of the platform can monitor every action in the assembly process in real-time. Once a missing step is detected, the edge box immediately issues a real-time alarm.

At the same time, combined with the semantic understanding ability of the DaoAI World model, the platform can perform cross-scenario generalization for different assembly scenarios and product types, improving the accuracy and universality of detection. In addition, WeLinkirt's DaoAI AI AOI software system and DaoAI 2D / 3D AI AOI equipment can be used as auxiliary tools to further improve the detection accuracy of components.

Quantitative Results

After using the SkyVision platform, the detection rate of missing assembly steps reached 98.5%, and the missed detection rate was reduced to <1.5%, greatly improving the stability of product quality. At the same time, the false alarm rate was reduced by -75%, reducing the unnecessary re-inspection workload. In terms of production line model-changing, the model-changing time was shortened from the original 30 minutes to 5 minutes, significantly improving production efficiency.

This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.