The electronics industry, especially the electronics manufacturing industry, has always been in the forefront of innovation. Technology-powered improvements in the industry have not only disrupted electronics for the better but they have also been the reason for the major transformations in all other industries as well.
With the advancement of artificial intelligence (AI), the electronics manufacturing industry is set for another major revolution that will send ripples to the commercial, industrial, and consumer markets, transforming them.
It’s also best for you to keep up with the latest standards in the electronics manufacturing industry with the help of an authorized IPC training center.
As early as 2019, electronic manufacturers have been showcasing consumer electronics including baby monitors, television, and translator devices equipped with AI-powered electrical components. Intel, Apple, Google, Samsung, and many other companies are also said to have embraced AI-enabled electronics manufacturing.
In fact, in a 2017 study, a majority of organizations in the manufacturing and high-tech sector put boosting productivity through automation and machine learning high on their agenda.
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What is Artificial Intelligence?
Artificial intelligence utilizes computer systems and machines to simulate the capabilities of the human mind in terms of solving problems and making decisions. It generally works by consuming and assessing large amounts of labeled training data, identifying correlations and patterns, and applying these patterns to predict future conditions.
AI programming typically focuses on three cognitive skills that will make it useful in any industry that it’s applied in. These are:
- The learning process is the aspect of programming that ensures the AI can acquire data and process it to store actionable information. Algorithms are established to provide step-by-step guides for the computing devices to follow in order to complete specific tasks.
- The reasoning process is the part that allows the computer systems to select the appropriate set of algorithms in order to obtain the most suitable outcome.
- The self-correction process allows the computer systems to constantly adjust and fine-tune the algorithms in order to ensure making the most accurate results in the future.
AI in Electronics Manufacturing
The manufacturing process typically generates tons of analytical data from the sensors of machineries and equipment. A large amount of data, coupled with the hundreds of variables to consider, can be hard for humans to analyze. Computing systems powered by artificial intelligence, however, have the capability to quickly and easily assess patterns and predict the effects of individual variables even in complicated conditions.
Here are some areas of manufacturing where AI shines:
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Process optimization
With the help of AI programming in their process mining tools, manufacturers can identify and rectify bottlenecks and other related problems within their production process. Even when a company has several plants in different regions, an AI-powered process mining tool allows the manufacturer to ensure that operations are streamlined and are operating consistently to achieve their goals.
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Predictive maintenance
AI systems, with the help of sensor data, can be programmed to identify potential downtime and accidents involving machineries, allowing manufacturers to schedule maintenance and repairs before major disruptions can happen. In this way, manufacturers can increase efficiency while reducing the costs of equipment breakdowns.
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Robotics
Since the late 1970s, manufacturing robots, also known as industrial robots, have been utilized in manufacturing operations. They automate repetitive and mundane tasks, allowing humans to free their time in order to focus on the more productive aspects of the operation. They also eliminate human error in production or at least reduce the error to a negligible rate.
With the advancement of AI, manufacturing robots now have the capability to monitor their performance and accuracy while making self-corrections for improvements. Some robots can also be equipped with machine vision, which uses cameras, sensors and computing power to capture and assess images and precisely complete industrial tasks even in random and complex situations.
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Raw material price prediction
The prices of raw materials are generally unstable and this is nothing new for manufacturers, who need to adapt to the extreme price volatility to remain competitive in the market. AI programming can be designed to predict material prices much more precisely than any person. The AI can also adjust its algorithm based on its self-correcting process in order to make a more accurate forecast in the future.
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Inventory management
Just as AIs can predict the prices of raw materials more precisely than humans, they are also good at forecasting demand and managing supplies for more accurate inventory management. In this way, manufacturers can ensure that operations will not be disrupted due to out-of-stock scenarios.
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Generative design
Manufacturers can quickly and conveniently create multiple options for the design of a single product with the help of AI technology. A generative design computer system can be provided with design parameters (e.g. cost limitations, weight, size, materials, etc.), which the AI can utilize to create thousands of possible designs based on the restrictions provided.
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Edge analytics
Edge analytics is the collection, processing, and analysis of data at some non-central point of the network (i.e. at the “edge” of the network) instead of within another centralized system or on the cloud. The analytics usually occur near or at a sensor, peripheral node, network switch, and other connected devices on equipment and machinery.
The bulk of data is analyzed at the edge, thus, lightening the load of the network. Only the important information is uploaded to the network for a more thorough analysis. This results in faster and more accurate business analysis.
Be in the Know about the Electronics Manufacturing Standards
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