Riding the AI ​​wave, innovating product testing with great force

Reinforcement learning algorithms are suitable for natural language processing, such as interactive online chat, chatbots, text or speech translation, language modeling, etc. They are also suitable for applications such as self-driving cars where the surrounding environment is constantly changing dynamically, the healthcare industry that can improve disease diagnosis and treatment, and industrial automation.

Application of AI in actual product testing

Different industries and companies in all regions of the world are using AI technology to innovate complex software and hardware testing methods, using AI technology to improve the efficiency, accuracy, cost-effectiveness and scalability of testing, thereby gaining many benefits. The following are three typical AI use cases:

First: Application of AI in Software Testing

A software company in the banking, financial services and insurance (BFSI) industry must ensure that all of its banking software applications, such as mobile banking, credit cards, e-wallets, trading and investment, and insurance applications, meet all of its performance, functionality, and system integration requirements. In addition, these software applications must meet all compatibility requirements, satisfy business process needs, and meet security regulatory standards.

The company uses AI technology to help automate testing, increasing test coverage by 90%, thereby improving product quality and shortening the time to market for new products by 40%. At the same time, it increases efficiency by more than 40% while ensuring product quality, and minimizes total costs by eliminating risks in the early stages of product development.

Second: Application of AI in machine vision testing

A PCB assembly (PCBA) company needed to streamline its manufacturing process in order to provide competitive PCBA manufacturing services to global customers. The company took a bold step toward this goal by applying AI to all stages of its manufacturing process, including the PCB design stage, where AI performance simulation helps to design integrated circuits more accurately; welding machines, which use AI technology to control fine-pitch component welding; and machine vision inspection based on AI technology, which can effectively capture product defects.

As a result, the company has improved the quality of its PCBA products and reduced manufacturing costs through better design and more efficient, automated machine vision inspection methods.

Third: Application of AI in the field of acoustic and vibration testing of products

A company with heavy industrial machinery needs to ensure that the chances of unexpected downtime in its operations are extremely low to avoid losses. In recent years, the application of acoustic and vibration analysis for early fault detection and diagnosis has made great progress by applying artificial intelligence and machine learning (AI/ML) to fault prediction for industrial machinery.

AI/ML methods, such as unsupervised convolutional neural networks for image segmentation, train algorithms using acoustic and vibration data from the operation of machinery throughout its historical lifecycle. Once trained, the test system is able to perform real-time monitoring online without human supervision, predict early failures, and provide diagnostics to pinpoint the root causes of possible failures. Overall, this early fault detection and diagnostic system with integrated AI technology has saved the company millions of dollars in costs and avoided unexpected downtime and damage to expensive equipment.

Future development trends of product testing driven by AI

The AI ​​era has arrived, and its application in all walks of life will continue to deepen. Solution innovation using AI technology will inevitably make the product testing and development process easier, thus creating good news for the mass market. Test and measurement companies will also launch more easy-to-use, AI-based testing software for specific industry applications. Using AI for product testing can bring many benefits, including improving test efficiency, enhancing accuracy, and improving cost-effectiveness and scalability. Companies can use AI technologies such as supervised learning algorithms, unsupervised learning algorithms, and reinforcement learning algorithms to innovate their testing processes and ensure higher product quality.

AI technology will continue to evolve, and new product testing solutions based on AI technology will become easier to deploy. Many companies have seized this opportunity to gain a leading edge over their competitors in product life cycle management.