Artificial Intelligence is proving to be a game-changer, with a plethora of applications in virtually every field. It is now finding its way into the field of production and manufacturing, allowing it to leverage the power of deep learning and, as a result, provide faster, cheaper, and superior automation. The purpose of this essay is to provide a basic knowledge of automated visual inspection and how a deep learning method may save time and effort.
Automated Visual Inspection (AVI)
It entails analyzing items on the production line for quality control purposes. Visual inspection may also be used to examine the different equipment in a manufacturing plant, such as storage tanks, pressure vessels, pipelines, and other equipment, both internally and externally. It is a procedure that occurs at regular periods of time, such as on a daily basis. Visual inspection has been demonstrated time and time again to uncover the majority of concealed faults during manufacturing.
Areas of Requirement
In non-production contexts, a visual inspection can be used to evaluate whether the traits indicative of a “target” are present and prevent possible negative consequences, similar to how it is used in manufacturing for quality or defect evaluation. There are numerous industries where visual inspection is necessary where it is regarded as a high-priority job owing to the potentially high cost of any errors that may occur during the inspection, such as injury, mortality, loss of expensive equipment, discarded products, rework, or a loss of clients. Nuclear weapons, nuclear power, and airports are examples of industries where a visual examination is prioritized.
Manual to Automated Visual inspection. Why?
Numerous drawbacks to utilizing an old-fashioned examination method. Manual inspection necessitates the presence of a person, an inspector, who assesses the thing in issue and renders a judgment based on training or prior knowledge. Except for the professional inspector’s naked sight, no equipment is necessary. Visual inspection mistakes generally range from 20% to 30%, according to studies (Drury & Fox, 1975). Some flaws are the result of human mistakes, while others are due to space constraints. Certain mistakes can be minimized but not entirely eliminated by training and practice.
In manufacturing, visual inspection mistakes can take two forms: missing an existent fault or wrongly detecting a problem that doesn’t exist (false positive). Missed alarms are far more often than false alarms. False positives can result in extra manufacturing expenses and waste, while misses can result in a loss of quality.
How Auto Visual Inspection is better than Manual Inspection
- Higher optical resolution
- Higher processing speed
- Absolute memory
- Wider Visible spectrum
- Accurate measures and values
- The fast pace and accurate calculation
- Unbiased
- Work without fatigue
- Follow instructions without any question
- Persistent
Cases used by Automated Visual Inspection
- Quality Control: Automated Visual Inspection reduces the risk of getting false-positive and false-negative results.
- Production line automation: Decreasing efforts expected for errors detection through the automation of visual inspection.
Conclusion
From Google search by image to complex industrial systems ensuring product quality— machine vision is making our lives easier. And it’s possible that very soon visual quality inspection tasks will be mostly machine-based, allowing humans to focus on more sophisticated tasks. The efficiency and productivity of global manufacturers will go to a new level because of deep learning and Computer vision.
Our Automated visual inspection (AVI) Solutions are used to detect flaws, defects, or other irregularities in manufactured products. Like most AI-based applications, it aims to exploit the power of deep neural networks that can learn to automatically categorize objects just through processing examples. Let us tell you how we can help improve production.
