Active Member
|
[China]
Address: 168 TangKou Road,Taohua Industrial Park,Hefei,Anhui
Contact name:Amy Zheng
Anhui Keye Information & Technology Co., Ltd. |
|
|
Equipment features:
| 1.AI algorithm: high stability, adapting to the environment and background disturbance; different defect samples can be automatically identified after training |
| 2.Dataization: Independent database, save multiple samples, analyze non-good products, and retain history |
| 3.Multi-orientation: 360 ° comprehensive inside and outside the samples |
| 4.High precision: detection accuracy can be high |
| 5.Modularization, can flexibly increase or decrease the detection function according to customer actual needs |
| 6.Easy to operate: It is easy to operate and easy to maintain |
| 7.Safety: Medical grade material manufacturing, fully compliant with medical supplies production environment |
Inspection principle
The inspection of wheel hub surface defects is an important part of the wheel hub production process. Through preliminary research in the market, defect inspection and sorting are mainly carried out manually at present. However, due to the complex structure of the automobile wheel hub, the workload of manual inspection is large, and the work efficiency is low. The manual inspection method has been difficult to meet the requirements of fast, accurate and stable in the process of wheel hub production.
In order to realize the automation and intelligence of production and logistics equipment, improve the efficiency of wheel hub manufacturing, and reduce labor costs. KEYE TECH has introduced a solution for inspecting defects on the surface of wheels hub based on deep learning technology.
1. Process design of defect inspection system for wheel hub production line:
This system includes image acquisition, image preprocessing, wheel hub image defect inspection and so on.
2.Establish a database of wheel hub surface defects.
First, at the production site, an industrial camera is used to collect the defect images of the wheel hub surface, and then the data is processed, and then the defect images are marked by professional marking software, and finally a defect image database with accurate markings is obtained.
3.Identify fuzzy images.
The fuzzy elimination algorithm is realized by the generative confrontation network of deep learning, so the operation speed is slow. However, only a part of the collected images is fuzzed. To improve the efficiency of the defect inspection algorithm system, Before the image fuzzy is eliminated, the image will be recognized by the algorithm. If the image is recognized as a fuzzy image, it will be sent to the deep learning to eliminate the image fuzzy network. If the image is recognized as a clear image, the defect inspection of the wheel hub image will be carried out directly.
4.Eliminate fuzzy of the wheel hub surface image
Before detecting the defect image of the wheel hub, it is necessary to preprocess the defect image to meet the algorithm requirements. Since the image acquisition stage of the defect detection system proposed in this paper is carried out while the wheel is being transported on the production line, the image of the acquired wheel will have motion blur. Aiming at this problem, the author proposed a deep adversarial learning algorithm to eliminate motion blur without requiring accurate motion information.
5.Wheel hub image defect inspection.
The inspection system will give the defect location and defect type
of the wheel hub.
After-sale service
The company has a complete technical service team and rapid response mechanism, and has dedicated service specialists for each customer, who can receive technical consultation and fault reports from customers at any time. And to ensure rapid response to customer emergencies, to ensure that customers receive satisfactory service.During the epidemic or due to special reasons, when after-sales engineers are unable to reach the site, the service center can remotely adjust customer equipment for troubleshooting and technical consultation.
After the equipment arrives at the customer site, the after-sales engineer arrives in time to carry out equipment installation, commissioning, and operation training. The product quality of the whole machine is traceable, and the quality warranty period is 1 year from the date of acceptance. In the event of non-human faults during the warranty period, after-sales engineers will quickly arrive at the site or provide remote guidance for free maintenance.
