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Case Study

Industrial Vision System

Real-time monitoring system for industrial production processes detecting material deviations, foreign particles and process irregularities during operation.

Projektumfang
  • Machine Vision
  • Zeilenkameras
  • HTML
  • CSS
  • JavaScript
  • C#
  • Golang
Location
Germany, USA, China
Electrical Engineering

Project Description

An integrated vision system was developed for a continuous winding process, monitoring material flows in real time and detecting defects directly during production. The goal was to replace manual quality controls and enable reliable, automated monitoring without interrupting the production process.

The Initial Situation

Our customer processes a material over several meters in a continuous winding process. During this process, ideally no material damage, contamination or foreign particles should occur.

The Challenge

Manual quality control by employees is practically impossible at these lengths and speeds. Defects such as small particles, holes or irregularities can easily be overlooked – with direct impact on product quality and possible complaints.

The Solution

Real-time inspection with machine vision

To safeguard this critical process, we developed a high-performance vision system and integrated it directly into the machine environment. Using high-resolution camera technology and intelligent image processing, the system continuously monitors the material during ongoing operation. The captured image data is analyzed and automatically evaluated in real time.

As soon as a defect is detected, the system responds immediately:

  • Automatic stop of the machine
  • Acoustic signal for the operator
  • Display of an image section including defect position
  • Storage of the defect image for further analysis

This ensures problems can be identified and resolved immediately, before they affect larger areas of material.

Machine Vision
Line Scan Cameras
HTML
CSS
JavaScript
Golang
C#

Result

With the vision system, a previously barely controllable process has been fully automated and digitally safeguarded.

The combination of modern camera technology, intelligent image processing and machine learning enables reliable real-time quality control – creating a sustainable foundation for higher product quality and more efficient processes.

Solution Overview

System design and key capabilities

Intelligent defect detection through machine learning

A central element of the solution is the self-learning defect analysis. The system uses machine learning algorithms to recognize typical defect patterns such as foreign particles or material defects. The system is continuously trained with new examples, so detection accuracy improves steadily during operation.

Defects that have already been detected are documented and made available for quality assurance (QA). This creates a transparent data basis for analyses, optimizations and possible complaints toward suppliers.

User-friendly operation

Operation takes place via a web-based user interface ("Operator View"), which is flexibly accessible across the machine network.

Employees see there:

  • Live images from the process
  • Overview of all detected defects
  • Access to stored defect images
  • Control options for the system

This ensures control is maintained at all times – without disrupting the production flow.

Vision System

Hardware & Integration

For reliable inspection, a specifically tailored hardware solution was implemented:

  • High-resolution line scan cameras for continuous image capture
  • Powerful image processing computers for real-time analysis
  • Dedicated lighting systems (light bars) for optimal defect detection
  • Direct connection to the PLC for synchronization with the production process

The system is seamlessly integrated into the existing machine environment and works fully automated in the background.

Value for the customer

Using the vision system delivers clear benefits:

  • Significantly reduced product failures through early defect detection
  • Automated quality control without additional staffing effort
  • Transparent defect documentation for quality assurance and complaints
  • Relief for employees, who can focus on value-adding activities
  • Continuous improvement through self-learning algorithms

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