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AI Vision
Prefactor’s AI vision solution integrates advanced edge computing and deep learning technologies, specifically designed to meet industrial automation needs. Through precise image recognition and high-speed data processing provided by the AI vision system, it can detect and mark product defects in real-time, conduct high-precision measurements, and classify items, significantly enhancing the level of automation and production efficiency in the manufacturing process. The application of edge computing allows image data to be processed directly on the device, reducing transmission time and alleviating the load on data centers. This not only improves operational efficiency but also effectively reduces costs, making it particularly suitable for industrial environments with high demands for timeliness and data security. From electronics assembly and automotive parts inspection to semiconductor precision manufacturing, our AI vision system is applied across multiple fields, helping clients quickly identify defects and adjust processes in real-time to achieve stable and high-quality production lines. |
With edge computing technology, image analysis can be performed directly on the device, reducing latency and ensuring data security, making real-time detection possible. |
Application of AI Vision Systems: The Future of Manufacturing Automation
AI vision systems have become a key technology for enhancing automation efficiency and quality control, especially in the manufacturing industry. These systems utilize advanced image recognition and deep learning algorithms to provide production lines with high-precision, real-time product inspection, classification, and defect detection. Below are the application areas of AI vision systems across major industries:
Automobile
Defect detection and dimensional measurement of automotive parts are crucial, as the quality of these parts directly impacts driving safety. By combining AI vision systems with deep learning, it is possible to automatically detect scratches, cracks, or other defects on part surfaces. Additionally, the system’s high-precision measurement capabilities can accurately determine whether each part meets specifications, ensuring quality. The application of edge computing makes detection more real-time and significantly reduces data transmission costs. |
Food and Packaging
In food packaging and label inspection, AI vision systems can quickly identify whether the packaging is intact and if the labels are correctly applied or printed. The system can check key information such as production batch and expiration dates, ensuring product compliance and standardization. It can also directly assess the shape and size of the food items. |
Assembly
The assembly process often involves hundreds of parts, where even the smallest error can lead to product malfunction and impact the efficiency of the entire production line. AI vision technology, utilizing advanced image recognition and deep learning algorithms, can monitor the placement, angle, and alignment accuracy of each part in real-time, quickly detecting errors and removing defective parts. |
Metal
AI vision technology offers multiple benefits in metal processing inspection, enhancing the efficiency of quality checks while significantly reducing the costs associated with manual inspection. Traditional metal processing inspections typically rely on human labor, which is time-consuming and prone to errors caused by fatigue or judgment lapses, often leading to undetected defects. In contrast, AI vision technology can quickly and accurately detect various flaws, such as surface scratches, cracks, and deformations, ensuring that each product meets quality standards. |
Energy
AI vision systems can automatically detect defects in capacitor assembly, such as poor welding, misaligned components, and surface defects, using high-speed detection to identify issues in real time and ensure each capacitor meets quality standards. This technology not only significantly reduces the risk of defective products reaching subsequent production stages but also lowers the costs of manual inspection, effectively improving the yield and stability of the production line. |
Food and Beverage
AI vision systems can accurately recognize the plating, portion size, and appearance of dishes, comparing them with standard reference images. If an item error or plating discrepancy is detected, the system immediately alerts kitchen staff to make adjustments, helping to prevent customer dissatisfaction due to order mistakes. Additionally, AI vision assists kitchen staff in verifying the completeness of each dish, ensuring that main courses, sides, and garnishes are presented accurately and correctly. |
Server
Through machine learning, AI vision systems can quickly identify various assembly defects, such as missing or deformed parts, ensuring each product meets assembly standards. This technology enables high-precision inspection and comprehensive automated detection, boosting production efficiency and reducing labor demands. |
ICT
In the manufacturing process of ICT (Information and Communication Technology) products, components are often small, and assembly precision is critical; even the slightest error can cause the final product to malfunction. The high-speed image recognition technology of AI vision systems can detect issues such as misalignment and missing parts in real time, ensuring precision throughout the assembly process. |
Medical Equipment
In the production of contact lenses, even the smallest defect can impact product safety and customer experience. AI vision technology brings highly automated quality inspection to contact lens manufacturing, allowing factories to quickly and accurately detect issues such as missing parts, lack of hydration, dirt, contaminants, or insufficient liquid. This ensures that each contact lens meets strict quality standards. |
Three-step process for AI vision model development
The development of an AI vision model can be completed through three key steps: Building, Training, and Evaluation.
Data Annotation
Before training the AI model. Data preparation is critical.✔ Data Collection: Gather high-quality images containing the target objects, ensuring diversity in the dataset by covering various scenarios, lighting conditions, and angles.
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Model Training/Fine-Tuning
Import the annotated data into the AI model for training.✔ Model Selection: Choose a suitable model architecture (e.g., YOLO, Faster R-CNN, SSD) based on the required balance between accuracy and speed.
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Model Evaluation
The model must ensure it meets the expected performance.✔ Validation Data Testing: Use a separate validation dataset that was not part of the training process to evaluate the model's accuracy with metrics such as Precision, Recall, and F1 Score.
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AI Vision Project Workflow
Requirement
Conduct interviews regarding the application of AI vision in product, process, and quality contexts to identify customer needs and determine the return on investment (ROI) period.
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Collection
Collect and categorize relevant data based on the project context. If data is insufficient, use synthetic data generation as a substitute.
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Envaulation
Use the three-step AI vision model process to conduct preliminary model training and evaluation, ensuring feasibility.
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Dealing
Provide a quotation, then review the specifications and pricing again to finalize the transaction terms, ensuring the order is clearly confirmed.
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Manufacture
Focus on AI vision to carry out the design, machining, manufacturing, assembly, and testing of equipment, followed by delivery.
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