AI Quality Inspection Market size worth US$438.705 million by 2029
The AI Quality Inspection market is estimated to grow at a CAGR of 28.40%, attaining US$438.705 million by 2029 from US$179.806 million in 2024.
AI quality inspection helps collect and analyze variances through software-driven artificial intelligence and vision technologies in the production of products such as semiconductors, pharmaceuticals, textiles, and automobiles among others. With their precise and less time-consumed features, quality check applications that are owned by AI are thus permeating more in the semiconductor industries and the medical, clothing, automotive industries, etc.
Moreover, many advanced manufacturing firms in the world implement artificial intelligence-based quality control techniques because they are more accurate compared to the traditional approach of controlling quality manually. Therefore, it can be said that several reasons will enhance the demand for AI-based quality control systems other than the existing need for AI-based products, it is almost obvious that the business of such systems will increase within the forecast period.
The rise in operating expenses for manufacturing firms due to the production of subpar goods is the cause of the growth. In large batches, the human eye’s manual quality control may not always be able to identify these failures. Leading manufacturers around the world are aggressively investing in AI-based quality inspection software to get around this restriction, find faulty products sooner, and avoid further costs.
Furthermore, unrealized opportunities in developing regions seem to present an opportunity for the vendors of AI visual inspection systems to widen their customer base and establish themselves in fast-industrializing and technology-savvy areas. Developing countries often have a growing manufacturing industry and a transcending economy, making them ideal candidates for advanced inspection technologies. The providers of AI visual inspection systems have the unique opportunity of availing their cutting-edge solutions in these regions due to the lack of developed infrastructure and processes that usually serve the people’s needs in such places.
The AI quality inspection market, by type, is divided into two types- pre-trained and deep learning. Examples of artificial intelligence are deep learning models, which are modelled on the multi-layered neural structure of the human brain. Because such models are fed with a good amount of data, they can also understand non-visual features and patterns in the images. Visual inspection systems deploy deep networks to understand images and videos by locating imperfections, defects or specific features with accuracy.
On the other hand, pre-trained models are offered as artificial intelligence models trained before on wider image identification and object recognition using very large datasets. These models are then modified or transformed to meet the very demanding visual inspection applications. Pre-trained these models facilitate faster deployment and reduce the training periods because all the knowledge gained is put into use from previous learning stages.
The AI quality inspection market, by end-user, is divided into five types: Semiconductor, pharmaceutical, automotive, textile, and others. Due to the growing use of IoT technologies, smart devices, and networked systems, product failures have a substantial effect on user confidence, data security, and brand equity. The global trend of regulatory bodies tightening controls to impose stricter laws and regulations on the quality and safety of products exacerbates this. Besides being a legal requirement, it is also a strategic option to comply with these rules since any deviations from them can lead to huge fines, legal issues, or tarnishing the image of the concerned brand.
Moreover, the rising importance placed on quality control in many sectors also emphasizes the need to be able to detect and take corrective actions for all potential quality problems in the shortest possible time, across the entire product life cycle. Strong quality assurance can help businesses minimize risks and maximize performance.
During the projected timeframe, it is anticipated that the market for AI quality inspections will experience remarkable growth within North America. North America has been making significant investments to broaden the use and scope of AI software, including AI quality control and inspection, as a powerful technological evolution force in the global artificial intelligence market. The leading software companies are developing and competing with one another to expand their portfolio of AI products and services. For example, Spyglass Visual Inspection, a virtual AI quality inspection tool from Microsoft, combines technology services to find any flaws in products.
Moreover, using a federated learning model, IBM has unveiled its most recent AI quality inspection product. In addition to these well-known businesses, several American startups are focusing their product lines on developing new models and techniques to enhance AI-assisted quality inspection. For example, IHI Corporation, one of the world’s top manufacturers, has adopted the AI-based quality control application developed by Neurala Inc., a Boston startup. As a result, it is reasonable to assume that the North American AI quality inspection market will grow during the forecast period given current market trends and recent advancements in AI quality inspection products in the USA.
The research includes several key players from the AI quality inspection market, such as Intel Corp, Kitov Systems, Mitutoyo America Corporation, Landing AI, NEC Corporation, tunic AG, device GmbH, craftworks GmbH, Pleora Technologies Inc.
View a sample of the report or purchase the complete study at: https://www.knowledge-sourcing.com/report/ai-quality-inspection-market
The analytics report categorizes the AI quality inspection market using the following criteria:
- By Type
- Pre-trained
- Deep learning
- By End-Users
- Semiconductor
- Pharmaceutical
- Automotive
- Textile
- Others
- By Geography
- North America
- USA
- Canada
- Mexico
- South America
- Brazil
- Argentina
- Others
- Europe
- United Kingdom
- Germany
- France
- Italy
- Spain
- Others
- Middle East and Africa
- Saudi Arabia
- UAE
- Others
- Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Singapore
- Indonesia
- Others
- North America
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