Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The Inspekto system integrates a distinct electro-optical setup with advanced AI technology to deliver a versatile visual quality inspection solution that is user-friendly, easily adaptable to production line changes, and does not require expertise in machine vision. To get started, only 20 to 30 acceptable samples are necessary, while bad samples are optional rather than compulsory. This system can be seamlessly installed either at the end of the production line or mid-line, and it can be fully integrated with PLC or MES/ERP systems, making it ideal for inspecting incoming goods or conducting final quality assessments of finished products. Inspekto is capable of inspecting a variety of applications right out of the box, including plastic injection molding, metal casting, coating processes, mechanical assembly, surface defects, incoming materials, and packaging and labeling tasks. It efficiently manages parts fed in-line and can work in conjunction with robotic systems. Training the AI of Inspekto for a specific part is a straightforward process; all it requires is marking the inspection areas with a few clicks and adding 20 good samples to complete the setup. This ease of use significantly enhances productivity and assures quality in manufacturing environments.
Description
Mitutoyo has established itself as a global frontrunner in the realm of precision measuring tools and solutions. In our dedication to supporting your goal of producing entirely defect-free products, we have harnessed artificial intelligence to devise an innovative approach to the intricate challenge of defect detection. Historically, the process of visual defect detection has been not only expensive but also labor-intensive. With the advent of AI INSPECT from Mitutoyo, we empower users to craft straightforward yet advanced defect detection systems for visual inspection, thanks to the remarkable capabilities of artificial intelligence and machine learning. This state-of-the-art software employs deep-learning convolutional networks to discern the visual discrepancies between normal and defective pixels across any series of related images. Users can easily upload images of both defects and normal products into the application to establish a project model. They can then utilize intuitive marking tools to identify defects within the images. Furthermore, the software provides a guided training setup process that requires no prior knowledge of artificial intelligence, making it accessible to all users who wish to enhance their inspection capabilities. Ultimately, this transformative tool not only simplifies the defect detection process but also enhances overall product quality.
API Access
Has API
No
API Access
Has API
No
Integrations
NVIDIA DRIVE
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Siemens
Founded
1847
Country
Germany
Website
www.siemens.com/global/en/products/automation/topic-areas/artificial-intelligence-in-industry/usecases/ai-based-quality-inspection.html
Vendor Details
Company Name
Mitutoyo
Country
United States
Website
www.mitutoyo.com/aiinspect/
Product Features
Product Features
Inspection
Appointment Management
No
Customer Database
No
Dispatch Management
No
Equipment Tracking
No
Photos In Reports
No
Print on Site
No
Report Templates
No
Speech Recognition
No
Subcontractor Management
No