Artificial intelligence’s impact on manufacturing can be categorized into five primary sectors:
Maintenance
Instead of performing maintenance on a regularly-intervaled schedule, predictive maintenance utilizes algorithms to predict future component, machine, and/or system failures and signals personnel to conduct focused maintenance procedures to alleviate the issue without causing any unnecessary downtime. By preempting factory failures with machine learning algorithms, systems can continue to operate with fewer interruptions. Additionally, focused repairs, as opposed to general repairs, allow technicians to solve the error with better efficiency. Predictive maintenance also contributes to a longer Remaining Useful Life (RUL) of factory equipment and machinery by preventing tertiary damage and quelling small problems before they become large ones.Quality 4.0
Artificial intelligence allows for constant information and data collection from products and machinery in the field. By amassing and analyzing this data, manufacturers can improve the quality of their output, while simultaneously providing critical information that forms the basis of future product development and business decisions.Human-Robot Collaboration
As industrial robots continue to take over factory jobs, AI will play a critical role in ensuring factory safety. Additionally, AI will allow for real-time data collection from the production floor, thereby providing critical data that can be used to optimize processes.Generative Design
AI can also be used in the design phase of a project in order to explore a variety of different configurations or solutions to a problem. Using generative design software, engineers and designers can make improvements and tweaks to find an optimal solution before investing the time, energy, and money necessary in making the product.Supply Chain / Market Adaptation
Artificial intelligence can use patterns based on a variety of qualifications in order to optimize various aspects of a business such as staffing, inventory control, energy consumption, and making quality financial decisions. These optimizations permit positive changes and adjustments that allow for greater success in factory.Artificial intelligence assists manufacturers in searching for production disturbances in order to quell factory problems. A production disturbance is “any unintended event in the chemical production process that leads to waste, unplanned stoppages, or scrap." By utilizing AI and machine learning techniques to pinpoint potential production disturbances, manufacturers can optimize the production process in order to ensure quality across the product line.
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