In sectors like general and automotive manufacturing, AI has already revolutionized processes and efficiency. This article explores how AI is used in manufacturing factories worldwide.
Predictive maintenance using AI
In any application with machinery, predictive maintenance utilizes AI algorithms to predict when and how a machine may fail. This implementation of AI enables proactive fixing of machinery before it breaks to minimize cost, optimize maintenance schedules, and reduce system downtime. For predictive maintenance applications, AI analyzes historical data from sensor inputs, equipment performance over time, and forecasted demand to predict potential failures.
For predictive maintenance to work, machinery must include an array of purpose-built sensors, IoT connectivity devices, and on-system data processing units. Machine learning algorithms can run on integrated data processing units, local servers, or the cloud. Predictive maintenance technology can be found in nearly every industry, whether implemented on a simple hydronic cooling system pump or state-of-the-art 12-axis CNC.
Supply chain optimization using AI
In manufacturing, supply chain logistics can drastically influence throughput for the better or worse. AI-powered optimization of manufacturing supply chains can forecast assembly-line asset demand, optimize inventory levels based on manufacturing velocity, and suggest efficient storage and transportation routes throughout a factory. All combined, AI used in manufacturing supply chain applications can significantly minimize cost, increase throughput, and minimize delays.
For example, Boeing’s Everett facility, the largest building on the planet with over 98 acres of interior space, manufactures four different aircraft models. A single model, the Boeing 777, contains around 3 million parts from over 500 suppliers. Boeing uses FRID tags, GPS trackers, warehouse automation systems, automated vehicles, conveyor systems, and advanced robotics to manage the vast supply chain complexity within the facility. The streamlining of supply chain logistics heavily relies on AI-drive logistics software to provide super-human management of not just a single plane’s manufacturing but the entire factory.
Energy optimization in factory operations
A factory’s productivity and efficiency are at the core of its performance metrics. Reducing energy consumption can increase the cost efficiency of a factory, but if not effectively managed, it can reduce its overall productivity. Factory energy optimization using AI involves analyzing real-time data from an array of device and environmental sensors, machine operations, energy usage patterns, and cost patterns to identify inefficiencies and suggest adjustments for optimal energy utilization.
For example, Siemens utilizes AI algorithms to create digital twins of factories, monitor equipment performance, optimize production schedules, and alter energy usage patterns across a variety of factory types. Through AI-based predictive analytics, Siemens has proven they can help decrease energy-related emissions by 50% while maintaining production output by reducing energy waste during idle periods and optimizing machinery operation times. Devices like smart energy meters, energy monitoring systems, IoT-enabled sensors, and AI-based energy management platforms must be employed. Even given their high upfront cost, their use can reduce the overall lifetime cost of the factory.
AI-powered robotics in the automotive industry
Additionally, robotic movements themselves can be optimized using AI. For example, if a robotic arm uses five independent motors to control a claw apparatus at the end of the arm, AI can define the most efficient way to independently control the motors to improve accuracy, reduce energy use, and reduce time to completion of a task. This capability is particularly beneficial for welding, assembly, and painting applications such as those used in automotive manufacturing facilities, as it can ensure high-quality and consistent welds, while also minimizing total energy consumption and manufacturing time. These robots often utilize computer vision sensors, computational systems, AI-enabled control units, and various motion sensors.
AI-automated quality control
Vehicles contain hundreds of thousands of components, most subject to failure. As automation increasingly takes over the assembly process, there is a growing need for stringent, high-quality assurance. AI assists in identifying defects or deviations from quality standards by analyzing images, video, and sensor data to flag faulty construction or individual components.
For example, Porsche’s automotive painting facility features a four-mile-long conveyor belt that includes a completely robotic-powered painting process and a final inspection tunnel. In the inspection tunnel, employees and high-resolution cameras examine the painted finishes. The video feeds from these cameras are analyzed by an AI that can detect minor imperfections that can be immediately remedied.
Constant process improvement
BMW has employed AI in their painting process in a different way: overall process analysis for their painting assembly line. They use AI to predict an increase in dust levels in their facility, which can negatively impact their paint quality based on temperature and season patterns. Based on this prediction, BMW can precisely time filter replacement in their HVAC systems to effectively minimize harmful effects.
Broadly speaking, AI can accept massive data series using a variety of data inputs, identifying patterns and irregularities in that data to monitor or predict data outcomes. BMW’s data input was a variety of historical and current weather dates, and the output is an expected increase in dust particles and the subsequent actioning of filter changes.
Manufacturing optimization using AI
AI can amalgamate diverse data inputs, identify patterns, facilitate in-depth analysis, and provide application-tailored recommendations to increase factory and manufacturing improvements in nearly any sector. Humans can better understand complex systems through AI implementation, enabling real-world problem-solving. Whether it is enforcing a new era of quality control on Porsche’s painting inspection line, helping manage the largest manufacturing facility on the planet at Boeing, or monitoring a simple hydronic pump’s performance, AI can optimize industrial manufacturing efficiency, maximize performance, and reduce energy consumption.