Automated visual quality inspection using AI/ML in manufacturing industries

Introduction:

Whether a company manufactures automobiles, semiconductor chips, smartphones, or food and beverages, quality inspection is the industry’s most critical performance metric, which was traditionally performed by humans. However, inspecting the same product hundreds of times a day by humans can result in fatigue and unintentional complacency. The best option for industries is to use an automated visual quality inspection model powered by AI/ML.

Artificial Intelligence (AI) and Machine Learning (ML) are designed to reduce human efforts and increase task efficiency. The AI visual quality inspection can save your scrap/rework costs by providing a better outlier detection system.

Additionally, the company’s financial costs are also affected due to multiple reworks or remodeling of the entire product. The rework costs include generated scrap, reduced yield, increased work in process inventory, post-sale recalls, warranty claims, and repairs. The AI model guarantees you minimal errors in the quality detection of your product and thereby protects you from all the extra costs.

The time required for quality detection is also critical, as the traditional method can take up to a week to complete the task. However, AI models can provide results in milliseconds. In a matter of seconds,

AI-enabled models can process complex images and make accurate predictions about what you’re about to discover, this is possible due to accurate image labeling and incorporating computer vision services provided by firms like BAJS AI(need to add a hyperlink for BAJS here)

Evolution of Quality Inspection Methods:

Quality assurance techniques have evolved significantly over the last few years. Software development and testing methodologies are constantly evolving to meet the user’s changing needs. The waterfall model and other traditional models are now being replaced by Agile development methods. It helps you perform quality assurance tasks speedily and efficiently. Agile development includes AI-based testing that reduces the time as well as the efforts of the employee and the organization. They are efficient and provide many advantages, including reduced effort, faster prediction, and better execution. However, certain aspects of the AI models developed to date are lacking:

  • Large-scale tests are conducted.
  • The end-to-end requirements are ambiguous.
  • Unquestioning faith in the model.

The above challenges can be easily solved using autonomous testing in quality assurance tasks.

What is visual inspection powered by artificial intelligence and why is it superior?


When it comes to describing AI-based defect detection solutions, visual inspection technology based on deep learning and computer vision is frequently mentioned. In AI-based visual inspection, machine learning is used to automatically assess product quality by evaluating unstructured image and video data. Manufacturers can automate the identification of product defects using AI and computer vision technology, saving time and money while also improving quality control. Several distinct advantages of combining traditional inspection methods with AI and machine learning techniques include the following:

  • Defect detection is improved by freeing up mental resources for manual inspectors.
  • Automatically adapting to product changes without the need for extra programming.
  • Inspection of dozens or even hundreds of product regions in real-time.

Artificial intelligence can also be used to conduct internal and external assessments of production facility equipment, such as storage tanks, hydraulic hoses, and pipes. During production, AI-based visual inspection enables the detection of concealed issues to be more thorough and efficient. Additionally,

no-code AI application development solutions enable manufacturers to leverage this rapidly evolving technology without the need for technical expertise or a significant investment of time and money.

In terms of speed, accuracy, and repeatability, visual inspection AI outperforms human operators. Machine vision systems can inspect items with characteristics that are invisible to the naked eye more quickly and accurately than humans alone. On a production line, visual inspection powered by AI can scan hundreds or thousands of items. On an assembly line, AI-based visual inspection can scan hundreds or thousands of parts per minute in a consistent and repetitive manner, far exceeding the capabilities of human employees.

Industrial Use Cases

Currently, industries are rapidly adopting AI to reduce their efforts and time. The industries like automotive industry, health care industry, building industries, and manufacturing industries are constantly using AI-based visual inspection models to perform various tasks efficiently:

Automotive

Semiconductors

Building Materials

Electronics

Medical

Food

Benefits of Using AI in QA


The AI-based Visual inspection helps you to switch from traditional quality maintenance to automated quality assurance. It helps the industries to reduce their manpower and time for quality inspection. The key benefits of using AI visual inspection are as follows:

Reduce Test Planning time

Testing a product for quality assurance is a very time-consuming task if the planning is not done correctly. Moreover, most time the best quality assurance testers also go in designing the test cases for testing a product or software. The AI-enabled QA models allow you to reduce your efforts and time in planning by crawling through the applications or website and generating the needed test cases for you. It also executes the generated test cases on the required scan and thereby generates the testing report.

Enhanced Defect discovery

The AI-enabled debuggers can detect all the covered bugs that can cause severe problems. It also instructs the software development team about the repeated points of failure on which they need to concentrate.

Well researched build release

The AI-based models allow the organizations to track the performance of their previous build versions of the application. It helps the QA tester to realize the points of failure software is facing and correct the bugs in the next build. The AI in QA allows the companies to track the performance of their applications

 

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