There is no doubt that in the Industrial Revolution 4.0 manufacturing sector is leading in the application of artificial intelligence technologies. From significant reductions in unplanned downtime to better-designed products, manufacturers are turning to artificial intelligence-based analytics on data for employee safety.
Smart Maintenance
In production, the ongoing maintenance of the machines and systems on the production line represents a considerable effort, which has a decisive effect on the end result of every system-dependent production company. In addition, studies show that unplanned downtime is estimated to cost manufacturers $ 50 billion annually and that asset downtime accounts for 42 percent of this unplanned downtime.
For this reason, predictive maintenance has become an indispensable solution for manufacturers who have a lot to gain from being able to predict the next failure of a part, machine, or system in the form of machine learning and artificial neural networks to make predictions about plant malfunctions, drastically reduce costly unplanned downtime and extend the Remaining Useful Life (RUL) of production machines and plants.
The Quality Factor
Due to today’s very short marketing deadlines and increasing product complexity, it is becoming more and more difficult for manufacturing companies to maintain a high level of quality and to comply with quality regulations and standards. On the other hand, customers expect perfect products, which drives manufacturers to improve their quality game while understanding the damage that high failure rates and product recalls can do to a company and its brand.
Quality errors can include formulation deviations, subtle anomalies in machine behavior, changes in raw materials, etc. By addressing these problems early on, a high level of quality can be maintained. In addition, Quality 4.0 enables manufacturers to collect data on the use and performance of their products in the field. This information can be useful for product development teams to make both strategic and tactical technical decisions.
Human-Machine Collab
The International Federation of Robotics predicts that by the end of 2021, more than 1.3 million industrial robots will be in use in factories around the world. Branch offices in design, maintenance, and programming. In this intermediate phase, human-robot collaboration must be efficient and safe, as more and more industrial robots are entering production alongside human workers. Advances in AI will be crucial for this development, as robots can cope with more cognitive tasks and make autonomous decisions based on real-time environmental data to further optimize processes.
Design Generation
Artificial intelligence is also changing the way we develop products. One method is to enter a detailed summary, defined by designers and engineers, as input into an AI algorithm (in this case called “generative design software”). The summary can contain data describing constraints and various parameters such as material types, available production methods, budget constraints, and time constraints. The algorithm examines all possible configurations before focusing on a set of best solutions.
The solution suggestions can be tested using machine learning and provides more information on which designs work best. The process can be repeated until an optimal design solution is achieved. One of the main advantages of this approach is that an AI algorithm is completely objective, and not by default based on what a human designer would consider a “logical” starting point. No assumptions are made and everything is tested against actual performance against a variety of m scenarios and manufacturing conditions.
Adaptation
Artificial intelligence is a central element of the Industrial revolution 4.0 and is not limited to use cases on the shop floor; AI algorithms can also be used to optimize supply chains in production and help companies anticipate market changes. Through a reactionary/strategic mindset AI algorithms formulate estimates of market demand and look for patterns related to location, socioeconomic and macroeconomic factors, weather patterns, political status, consumer behavior, and more. This information is invaluable to manufacturers as it enables them to optimize staffing, inventory control, energy consumption, and raw material supply.
Industrial AI will Continue to Transform
The manufacturing industry is perfect for the use of artificial intelligence. Although the Industrial revolution 4.0 is still in its infancy, we are already seeing clear advantages of AI. From the design process to the manufacturing area to the supply chain and management, AI is designed to forever change the way we make products and process materials.
