Artificial Intelligence (AI) has been widely applied across various fields, not only in everyday life but also in manufacturing systems and factory management. Industrial AI helps manufacturers maximize uptime through equipment monitoring and maintenance programs, as well as detect defective products and identify errors.
According to IBM's 2022 Global AI Adoption Index report, 34% of surveyed respondents—approximately 2,550 businesses worldwide—cited a lack of AI expertise as the biggest barrier to implementing this technology. Other challenges included cost (29%), lack of tools/platforms (25%), scalability difficulties (24%), and data complexity (24%).
In this article, we will examine these challenges and address common misconceptions about Artificial Intelligence in manufacturing.
Before exploring AI applications, it is essential to understand its forms, functions, and feasibility. Understanding the terminology and differences between them is the first step in determining whether the technology is suitable for your needs.
A set of instructions and computations that help a computer achieve a goal. Algorithms "learn" uses trial-and-erro & learn-by-example to optimize the manufacturing process without human intervention.
A set of computational techniques that attempt to simulate human decision-making, solving complex tasks for humans by using image recognition, natural language processing, and other technologies.
AI technology automates complex and highly customized applications. Processing occurs through graphics processing units (GPUs), enabling the rapid and efficient analysis of large image datasets to detect subtle defects and differentiate between acceptable and unacceptable anomalies.
AI technology is designed for ease of use. Processing takes place directly on the device using a pre-configured set of algorithms. This technology is simple to install, requires a small image dataset (from 5 to 10 images), and has a shorter setup time compared to traditional deep learning solutions.
Computational processes can improve outcomes without human programming. Machine Learning algorithms use computers to search for successes and avoid failures millions of times to generate learning outcomes.
Rule-based algorithms identify specific characteristics of an object. While machine vision tools operate faster than the human eye, AI technology enhances their accuracy and efficiency.
While some people worry that artificial intelligence will replace humans in the workforce, the reality is that AI is designed to enhance performance, efficiency, and quality. AI enables employees to collaborate, achieve higher productivity, and unlock new possibilities.
AI can reduce tedious and repetitive tasks, allowing employees to focus on more creative or higher-skilled work. For example, in 2018, a charity organization in New York implemented AI for data entry tasks, reducing annual employee turnover from 42% to just 17%.
AI technology is being widely adopted in manufacturing plants to address labor shortages and other challenges. When combined with robotics, AI can perform tasks such as obstacle avoidance and surface mapping for material transportation within facilities. When integrated with machine vision systems, AI can handle repetitive yet essential quality inspection tasks, such as detecting the presence or absence of product components.
One common misconception is that industrial AI requires large amounts of data to function effectively. However, this depends on specific applications. Edge Learning technologies only need 5-10 images and can be deployed by employees with little specialized expertise.
For example, in a visual inspection project, operators provide the system with images of both standard-compliant and non-compliant products. Edge Learning technology then utilizes advanced algorithms to distinguish between acceptable and unacceptable products. Once the system is trained with this data, users can deploy the solution on the production line.
Developing and designing AI requires specialized skills, but modern AI solutions can be deployed by factory staff within minutes. Solutions utilizing smart cameras with AI technology are equipped with integrated lighting, auto-focus lenses, and powerful sensors to ensure a high level of inspection accuracy.
Additionally, since no advanced expertise in machine vision or AI technology is required, production line engineers can easily use and adjust the system to meet the factory’s specific needs.
Artificial intelligence is not just a trend or a niche technology limited to specific markets; it is a vast field that can support the industry in multiple ways. As technology advances, it becomes more user-friendly. Its real-world effectiveness in manufacturing plants and warehouses is optimized, improving product traceability and enabling facilities to detect defective products earlier in the production process.
AI has been used to automate specific tasks by analyzing data and patterns to guide future actions. For example, specialized AI has been applied in manufacturing and logistics operations to inspect parts, verify the presence or absence of specific components, and sort packages.
Source: COGNEX
>>> Read more: The Potential of Artificial Intelligence in Manufacturing
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