The use of artificial intelligence (AI) has grown and expanded into near every nook and cranny of everyday life in the modern world. Look no further than the smartphone as exhibit “A”, and, just beyond that, to the search engines that customize news and optimize online shopping. AI regulates smart technology in homes, offices & businesses, and it operates nearly all car functions and even irrigates crops on farms and monitors the food consumption of livestock. The list of how thoroughly integrated AI is into everyday life is exhaustive.
AI's Role in Manufacturing
In manufacturing, words such as ‘simulation’ and ‘mimic’ are often used to describe the technology we refer to as industrial AI - the ability of systems and machines to adapt human intelligence in performing repetitive tasks while simultaneously, through iterative programming, continually improves upon its performance with the data it collects during the iterative process. In other words, AI has the capability to learn and reason through programming, not self-awareness. For manufacturers, the development of industrial artificial intelligence (IAI) in manufacturing processes has greatly enhanced production capabilities.
Labels such as “smart factory,” “smart manufacturing,” or simply “Industry 4.0” are often applied to describe IAI processes. By any name, it offers many advantages for manufacturers. And though IAI in manufacturing still has challenges, it has also resolved tasks once thought incapable of being automated. Prime among these is IAI capability in performing tedious, time-consuming, repetitive tasks with greater efficiency and output or those tasks too difficult or dangerous for humans to perform. Special equipment and tools, software and algorithms are engineered for specific environments and target specific problems unique to a particular manufacturing environment.
Rule-Based vs Machine Learning Systems
Although IAI applications have been integrated into nearly every niche of manufacturing operations, there are still only two categories of IAI in manufacturing. IAI processes operate through either a rules-based system or machine learning system, and sometimes a combination of each.
Rules-based systems rely on predefined instructions - programs - that literally tells the machine what to do. Easily operated and controlled by humans, rules-based IAI can still be complex and sophisticated systems, its limitations related to modeling or anticipating outcomes in that system’s design. Nonetheless, the production applications that rules-based IAI systems are programmed for are basic decision-making processes designed for specific outcomes.
If there is a general conception of what the term AI is in manufacturing - the imagery of robotic automated processes without human interaction - then machine learning IAIs is it. The performance of any machine learning process is based on the algorithms that “teach” it to adapt and differentiate between good and bad responses. In effect, these production systems learn by recognizing patterns in equipment behavior. Many equipment monitoring systems utilize machine learning algorithms to alert operators when patterns change.
Industrial Artificial Intelligence Applications
IAI has optimized whole manufacturing processes - automating production environments, managing facility operations, implementing predictive maintenance schedules, and increasing production output, with increased efficiency. One area of note is the electronics manufacturing industry. At the forefront of AI-enabled electronics manufacturing processes, IAI electronics manufacturing processes are not only used to produce AI-powered electronic applications and devices, but are also being used to improve intelligent workflows for the rapid development of new products.
In R&D, IAI accelerates product development and prototyping by ensuring correct material selection, identifying likely prototype part failure, and rapidly compiling product data. The complexity of designing circuit boards is being reduced from weeks to a matter of hours with AI. Algorithms can anticipate likely prototype failures by analyzing mounds of performance data, and cameras powered by computer vision algorithms can identify defective products immediately and trace root causes of failure.
Today’s manufacturers are benefiting from the combination of automation and machine learning that has evolved over the past decades. IAI solutions offer cost-effective solutions for whole manufacturing processes. Advancements in IAI have optimized productivity and quality control, eliminated time-consuming manual tasks, and standardized products at higher quality, higher output, and efficiency.