Examples of application solutions for appearance defect detection of precision parts

Program overview

Examples of application solutions for appearance defect detection of precision parts are examples of public industry application solutions in the direction of industrial vision inspection. For the detection of scratches, bruises, dirt, missing parts and character anomalies on metal parts, injection molded parts and electronic parts, it presents a visual quality inspection delivery path from sample evaluation to production line joint debugging. This page is geared towards project evaluation and solution matching. It does not disclose customer names, production line scales, business data or unconfirmed operating indicators. It focuses on what similar computer vision projects need to be evaluated, how to organize the technical route, how to confirm the delivery content, and what information should be prepared for early communication.

Project background

Appearance inspection of precision parts usually occurs in machining, injection molding, stamping, electronic assembly and incoming material re-inspection. The surface materials of the inspection objects are obviously different, and the defect forms include obvious scratches, but also minor indentations, contamination, edge gaps and abnormal characters. In project communication, it is first necessary to clarify the detection objects, work station locations, sample differences, rhythm requirements, installation space, existing equipment interfaces and acceptance methods. For scenarios such as precision metal parts, injection molded parts, electronic components, and assembly re-inspection, the same visual concept may require different camera angles, light source methods, lens focal lengths, trigger methods, and judgment rules at different sites. Therefore, JIVISION usually starts with sample analysis and imaging verification, rather than directly applying a fixed template.

Customer needs and assessment points

Typical requirements for this type of project include scratches and bruises, dirt and foreign objects, missing parts, characters and traceability codes. In addition to the identification itself, it is also necessary to evaluate whether the test results need to retain pictures, whether the formula needs to be switched according to product model, whether it needs to be connected with PLC, robot, MES, WMS or host computer system, and whether batch reports need to be generated. The main pain points include: manual inspection standards are inconsistent, and small defects and edge defects are easy to miss; changes in product batches, surface reflections, and incoming material status will affect imaging stability; customers need inspection results to be traceable and to complete OK/NG linkage with production line equipment. These issues need to be confirmed through sample testing and field condition analysis, and are not suitable to be replaced by unverified universal indicators.

Technical solution

The technical solution focuses on appearance defects, dimensional review, and OCR identification, and is implemented in conjunction with the project delivery process. JIVISION will first evaluate the imaging stability, and then design the algorithm link and system interface. The plan usually includes: completing the evaluation of camera, lens and light source combinations based on defect size, material and installation space; establishing a multi-strategy judgment process by combining traditional image processing, deep learning detection and OCR recognition; configuring inspection records, picture retention, alarm output, parameter formulas and data export modules. In actual projects, traditional image processing, deep learning detection, OCR recognition, instance segmentation, point cloud processing or rule review processes can be combined according to object characteristics, so that the system can both identify targets and explain the basis for judgment.

System architecture

• Multi-angle imaging station
• Defect detection model
• OCR identification module
• OK/NG linkage output

Implementation process

The implementation process generally includes requirement confirmation, sample collection, imaging experiments, solution verification, algorithm training or rule development, interface definition, on-site deployment, acceptance testing, and operation and maintenance handover. At each stage, sample conditions, parameter versions, judgment rules, and abnormal sample records will be precipitated to facilitate subsequent review. When customers subsequently add product models, defect categories or on-site workstations, they can continue to iterate based on the original data and interfaces.

Delivery content

• Visual inspection software
• Imaging Hardware Selection Plan
• Defect sample evaluation report
• On-site joint debugging and acceptance support

Acceptance and iteration

The acceptance indicators should be determined based on customer samples, on-site test results and testing standards confirmed by both parties. Common evaluation dimensions include recognition accuracy, risk of missed detection, false alarm handling, processing speed, stability under illumination changes, data traceability and maintenance convenience. The solution example description on the public page is used to illustrate the technical route and delivery method. The final project indicators should be based on the customer's real samples, deployment environment and contract agreements.

Applicable scenarios

This solution example is suitable for precision metal parts, injection molded parts, electronic components, assembly re-inspection and similar needs. It is also suitable for reference by customers who are evaluating machine vision inspection, computer vision algorithms, edge AI vision, 3D visual measurement, robot vision or visual data training. For projects with uncertain technical routes, sample evaluation, imaging testing, and small-scale solution verification can be conducted first to confirm feasibility before proceeding to system development and on-site deployment.

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