Why you should integrate Machine Vision in your production Process?

Machine Vision Technology in Today Market

According to VDMA Organization, In German and Europe, The machine vision in industry sales records more than doubled in the years 2005 – 2015. Applying Machine Vision leads to improved quality, greater reliability, increased safety and cost-effectiveness.

Machine vision is a technology that the enterprises are using for automating and integrating a wide range of processes. Today Machine vision technology is giving Industry 4.0 a true economic significance.

How does Machine Vision Technology work?

Step 1: Image Capturing

The system can automatically capture images through one /multiple instance, ultraviolet or infrared cameras during the production process.

Step 2: Processing

Analyzing the captured image by using customized algorithms.
The system can extract, verify and identify some information about the object such as gauging, counting, classification, segmentation and sorting based on the standard applied by the client or the industry.

Step 3: Action

The system takes an action based on the analyzing step.

Machine Vision Applications

The field of machine vision may be best understood by considering different types of applications. Many of these applications involve tasks that require either work in a hostile environment, a high rate of processing, access and use of large databases of information, or are tedious for people to perform. Machine vision systems are used in many and various types of environments – from manufacturing plants, to hospital surgical suits, and to the surface of Mars Traditionally, visual inspection and quality control are performed by human experts.

How can machine Vision support Human workers in industry?

Although humans can do the job better than machines in many cases, they are slower than the machines and get tired quickly.

Moreover, human experts are difficult to find or maintain in an industry, require training and their skills may take time to develop. There are also cases which inspection tends to be tedious or difficult, even for the best-trained experts. In certain applications, precise information must be quickly or repetitively extracted and used. In some environments (e.g., underwater inspection, nuclear industry, chemical industry etc.) inspection may be difficult or dangerous. Machine vision may effectively replace human inspection in such demanding cases Many key tasks in the manufacture of products, including inspection, orientation, identification, and assembly, require the use of visual techniques. Using machine vision can go far in increasing accuracy and saving time by automating factory operation.

Deep learning Technology

Introduction to Deep learning Technology

Deep learning concentrates on a subset of machine-learning techniques, with the term “deep” generally referring to the number of hidden layers in the deep neural network. While a conventional neural network CNN may contain a few hidden layers, a deep network may have tens or hundreds of layers. In deep learning, a computer model learns to perform classification tasks directly from text, sound or image data. In the case of images, deep learning requires substantial computing power and involves feeding large amounts of labeled data through a multi-layer neural network architecture to create a model that can classify the objects contained within the image.

Adding Deep learning to machine Vision solutions

The value of deep learning in machine-vision applications stems from its ability to make human-like judgments of part quality and other example-based decisions. Verifying the presence of bolts, brackets, foam pads and straps on car seat assemblies, for example, can challenge traditional machine vision systems if sub-components come from a variety of suppliers with variations in color and texture. In such applications, deep learning helps machine-vision systems cope with the range of acceptable part appearances.

Source: Vision System Design

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