Artificial Intelligence and Industrial Applications

Today, factories and production centers around the world must turn to automation, machine learning, social networks and other AI (Artificial Intelligence) applications to meet rising demand in an efficient and competitive manner. Artificial intelligence applications are demonstrating their impact in industry. Let us briefly examine a few of the industrial applications of artificial intelligence.

Adaptive Manufacturing

With the Industrial Revolution, factories began producing rapidly and cheaply thanks to machines. However, these machines are optimized to produce only a few products, depending on each factory's needs. This is why in traditional industrial automation, redesigning production for new products is a lengthy, complicated and costly process. Especially in high-technology production lines, robots are often used for basic automatic systems. Classically, these robots are perfect for repetitive tasks but need to be reprogrammed for even the slightest change in operation. However, thanks to artificial intelligence applications, robots are now beginning to reach a level where the system can measure and adapt to the process in real time. In this way, next-generation robots with artificial intelligence will be able to adapt to changes in production logic after short training, just as a human would.

Predictive Maintenance

The purpose of predictive maintenance is, first and foremost, to predict when equipment failure may occur and perform maintenance beforehand to prevent the failure from happening. Predicting future failures enables maintenance to be planned before failures occur. Ideally, predictive maintenance allows maintenance frequency to be as low as possible without incurring costs associated with excessive planned and repetitive maintenance. Predictive maintenance uses condition monitoring equipment to assess an asset's performance in real time. A key element in this process is the Internet of Things (IoT). IoT enables different assets and systems to connect with one another, work together and share, analyze and act on data. IoT relies on predictive maintenance sensors to capture information, understand it and identify areas that require attention. Some examples of using predictive maintenance and predictive maintenance sensors include vibration analysis, oil analysis, thermal imaging and equipment observation. Maintenance is performed only when necessary, that is, just before failure occurs and only on the machines that need it. Predictive maintenance provides very significant advantages such as minimizing the time equipment spends in maintenance, reducing production hours lost due to maintenance, and lowering spare parts and consumables costs. AI-led cognitive automation solutions (Intelligent Automation) combine the best automation approaches with AI and deliver superior results. Today, organizations have processing of very large amounts of data that enable better machine learning algorithms and better runtime decisions. For example, during a software update, machine learning algorithms can scan code to detect significant changes in functionality and map them to requirements to define test cases. This helps optimize testing and prevents decisions that could lead to failure. The future lies in solutions that use deep learning foundations to create a truly autonomous approach to testing. Like self-driving cars, autonomous technologies will begin to create their own scenarios to learn the system and test autonomously.

Automated Quality Control

In a developed inspection system, a special light wall projects a zebra pattern onto the object to be examined. A camera captures the reflected black and white light reflections on the surface of an automobile or aircraft. Images are analyzed by software to identify and classify defects in terms of 12 mathematical parameters. In addition to being more efficient than visual inspection, the mathematics-based technology used by this system is also an advancement over traditional automatic inspection models. Traditional inspection systems take data from CAD (computer-aided design) files and vehicle geometry, among other information, so they can detect defects by comparison to a standard. The system also saves considerable time in the preparation phase. In traditional applications, preparing an inspection template that searches for defects by comparing a product to an ideal standard takes an extremely long time. However, with the new generation pre-prepared mathematical model, this time is significantly shortened. Prepared by: B. Serhat Cengiz Sources • Artificial Intelligence: How Advance Machine Learning Will Shape The Future Of Our World, Christina Ahmet. ScreenMagic Publishing via PublishDrive, Nov 20, 2018. • Artificial Intelligence: A Systems Approach: A Systems Approach, M. Tim Jones. Jones & Bartlett Learning, Jun 16, 2015 • https://avr-aerospace.com/what-is-adaptive-manufacturing-exactly/ • https://www.fiixsoftware.com/maintenance-strategies/predictive-maintenance/ • http://pesquisaparainovacao.fapesp.br/the_automotive_industry_uses_artificial_intelligence_in_vehicle_inspection/865