Understanding Machine Vision and Exploring It’s Practical Applications

Machine vision has proven itself to be a superior technology to be part of today’s production floor technological development in automated product inspection applications. Machine vision plays a vital role in high-speed production within pharmaceutical, automotive, and product assembly line sectors providing real time automated inspection capabilities, wherein human eye manual interventions are non-existent or close to impossible.

Implementation of machine vision within a control system requires system integrators with knowledge and experience interfacing vision system hardware and software to existing control system, most modern machine vision operates on computer-based hardware and software which requires to communicate to PLC and HMI controllers which performs overall control system.

Typical Applications of Machine Vision in today’s production floor:

Flaw detection

Flaw Detection

Flaw detection is one of the most fundamental quality control tasks in manufacturing industries and the most utilized function of machine vision systems. In flaw detection, the machine vision searches for defects such as cracks, scratches, blemishes, gaps, contaminants, discoloration, and other irregularities present on the part’s surface, which can affect the product functionality and reliability.

Practical Positioning

Positioning is the process of comparing the location and orientation of the part to a specified spatial tolerance. Machine vision positioning systems offer more accuracy and speed than manual inspection, alignment, and positioning. Practical Positioning applications find itself most useful with robotics applications.

Parts Counting

Counting applications is a common application, where a high-speed ROI – regions of interest parts required counts confirmation, example counting target tablets count on strips.

Distance Measurement

Accurate distance measurement provides large scale applications, example determine dimensions of a package traveling on a conveyor line, perform certain functions while monitoring distance to target within a production control system.

Parts Identification

Objects identification could be implemented static or while in-motion over a conveyor line, by capturing barcode image and performing OCR algorithms to identify parts in real time. ControlSoft Canada has implemented such an application with automotive industry here: In Motion Engine Block Laser Marking System Design (controlsoft.ca)

Parts Counting​
Distance Measurement​

Major Component – Typical Vision System

Major hardware and software component consist of Cameras, lenses, illumination, and image processing equipment make up its systems. Each component is chosen based on the application:

Camera: Picture sensors in cameras that transform light into digital image data for transmission to the controller. Camera selection is based on number of pixel requirements of an output image. At the most basic level, a camera sensor is a solid-state device that absorbs particles of light (photons) through millions of light-sensitive pixels and converts them into electrical signals. These electrical signals are then interpreted by a computer chip, which uses them to produce a digital image. While there are several different types of camera sensors, by far the most prevalent is the complementary metal-oxide semiconductor (CMOS) sensor, which can be found inside most modern digital cameras.

Lens: Lenses are used to concentrate light onto the picture sensor.

Light: Any machine vision setup requires careful light selection; a system can’t investigate what the camera can’t see. The form, size, and color of illumination and the distance and angle from which it is installed may all be tuned to highlight the things being examined while avoiding any impacts from the surrounding environment.

Unit for Image Processing: Picture processing units, also known as controllers, process image input and extract crucial information using predefined algorithms.


Vision systems increase product quality by reducing human error and ensuring quality checks on all goods passing through the production line. It has a cascade effect, decreasing the overall production cost in terms of both time and money, as fewer defects and faulty items emerge and never make it to the next stage, incurring time delays. This helps prevent defective items from reaching the end customer and producing unfavorable publicity, which some firms have not avoided.

Optical Characters Recognition – OCR is one application of image processing algorithm that has taken off in automated documents processing applications.

ControlSoft Canada Control Engineering Team has Successfully Completed Machine Vision Projects:

  • In an engine oil filling can application to confirm using vision to make sure engine oil is filled to the rim of the cans, and as the engine oil can travels along production line vision image is captured to read type of engine oil and print batch code and serial number using laser printer on to the filled cans.
Vision to detect required FILL levels, presence of bottle caps
Vision to detect required FILL levels, presence of bottle caps

Vision to Detect Required FILL Levels, Presence of Bottle Caps

  • In automated laser marking production lines, machine vision is used to read laser marked contents to verify content is accurate and the 3D matrix is readable.
  • Machine vision is used to guide robots on parts locations, by transmitting 3D co-ordinates to robot axis controller.
  • ControlSoft expertise ranges with different machine vision vendors, which includes working very closely with Cognex Vision system teams both on hardware and software integration.

ControlSoft Canada, Machine Vision & Computer Vision is focused on industrial applications side within the production floor assembly lines, as a system integrator.

Advantages and Benefits of Machine Vision Systems

Machine vision systems provide several direct benefits to manufacturers and production line processes. The benefits of using machine vision technology include:

  • Reduction in the number of defects, hence less wastage and saving in material cost.
  • Increase in production yield by tightening control levels.
  • Reduction in production line downtime, making sure correct parts in correct orientation proceed to next stage of production.
  • Improved ability to track and trace parts and products in a production process.
  • Facilitation with compliance to regulations that apply to specific product classes.

Reduced defects translate into a fewer number of product recalls from issues such as mislabeling of products, where the label applied to the product does not match the product contents. These situations cost manufacturers money to recall and replace mislabeled stock. The consequences of these recalls include damage to the perceived reputation of the manufacturer’s brand and can also involve issues impacting the safety of consumers, such as in the case of mislabeled pharmaceuticals or products that contain ingredients to which some consumers may have allergies.

Improved yield is a direct result of having an improved ability to catch incorrect or bad parts before they are built into larger assemblies. The sooner that defective items can be detected and removed from a production process, the less waste in the process, which allows more of the raw material used in the process to be turned into completed goods that can be sold. Scrapped material and reworked assemblies cost time and money and show up as lower yield in production processes.

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