While the general population may have an archaic understanding of the inner workings of a factory, the truth is that the smart factory revolution, also known as Industry 4.0, has taken leaps and bounds over the past several decades. The rise of automation and smart technologies are at the forefront of this revolution. However, in recent years, artificial intelligence (AI) has become a mainstay in the future of Industry 4.0. While AI continues to drive automation, manufacturers are now looking at it to promote efficiency in a variety of new avenues.
Artificial intelligence’s impact on manufacturing can be categorized into five primary sectors:
MaintenanceInstead 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 4.0Artificial 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 CollaborationAs 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 DesignAI 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 AdaptationArtificial 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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