I. Organizational Structure
Machine vision application projects encompass a variety of technologies, including optics, mechanics, electronics, hydraulics, pneumatics, software, networking, and databases. Project management is complex and meticulous. A typical commercial machine vision project requires a team, including a project manager, a research and development team, a document management team, and an installation and testing team. The project manager must be highly professional and familiar with machine vision systems, responsible for the overall project design, task allocation, schedule planning, and necessary development work. The research and development team focuses on optical system design, mechanical installation structure design, signal and electrical design, software functional design, and code development, and is also responsible for on-site installation and maintenance. The document management team is responsible for creating and revising all relevant documents and writing user manuals, working concurrently with the research and development team. The installation and testing team is primarily responsible for testing and improving software functions, providing software training, and tracking on-site usage, and can collaborate with the research and development team.
II. Evaluation Indicators for the Visual System
Machine vision systems are systems that replace human eyes in inspection functions. Their application can significantly save labor, improve product quality, reduce scrap rates, save raw materials, and establish a good quality management image for enterprises. Only through these product quality advantages can users be convinced.
Machine vision is characterized by automation, objectivity, non-contact operation, and high precision. Compared to general image processing systems, machine vision emphasizes accuracy, speed, and reliability in industrial environments. Therefore, detection accuracy, detection speed, and system reliability can all serve as evaluation metrics for vision systems.
The accuracy of the inspection varies from system to system. For example, in a dimensional measurement machine vision system, the measurement accuracy can reach 0.01 mm; in a surface defect inspection vision system, the defect area can reach 0.1 mm * 0.1 mm.
The inspection speed of machine vision needs to be matched with the production speed or the production cycle time on the production line. For example, vision systems are capable of online inspection speeds of 10,000 pieces/min; on high-speed printing production lines, inspection speeds can reach 300 meters/minute.
Mean Time Between Failures (MTBF) is a metric for measuring the reliability of a product, especially electrical products. It is measured in hours. It reflects the product's time quality, demonstrating its ability to maintain functionality within a specified timeframe. Specifically, it refers to the average operating time between two consecutive failures, also known as the mean time between failures. It applies to repairable products.
Meanwhile, advancements in machine vision system application technology have made them extremely convenient to use. It is worth noting that when assessing the feasibility of installing machine vision systems on production lines, manufacturers are increasingly considering ease of installation and maintenance as a key factor.
III. Document Management
Documentation for machine vision application projects should include:
System overview and performance description;
Description of the object being inspected: physical variables such as size, color, and quantity;
Performance requirements for vision systems: speed, accuracy, reliability
Inspection process: How the component enters the camera's field of view, how to design external triggers, etc.;
Optical: Scene size, distance, etc.;
Mechanical requirements: size limitations, mounting method, packaging method;
Attachments required: power supply, etc.
Environmental requirements: ambient light, temperature, humidity, dust, dirt, cleaning method, etc.
Device interfaces: network, etc.;
User interface: screen, controller, access permissions, etc.;
Technical support: training, maintenance, upgrades, etc.
IV. Product Testing
For machine vision projects to operate stably and easily in the production environment for a long period, the following issues need to be addressed during the testing phase:
1. User interface: Do the controller and screen display conform to human factors engineering?
2. Detection accuracy: accuracy and repeatability;
3. Testing efficiency: Can it meet the requirements of production capacity?
4. Sensitivity: Will minor changes in the environment and system cause performance changes?
5. Maintainability: How easy is it to replace parts and perform optical calibration?
6. Stability during long-term operation.
V. Cost Accounting
Economic indicators are one of the important aspects of evaluating machine vision systems. The cost of a machine vision system includes initial costs and operating costs.
Initial costs include:
1. Equipment procurement costs; 2. System development and integration costs; 3. Transportation and installation costs; 4. Training costs; 5. Project management costs.
Operating costs include:
1. Regular maintenance; 2. Retraining; 3. System upgrades
