A technology story goes there is town located almost 90 minutes from the west of Tokyo. A small and beautiful town located in the shadow of Mount Fuji, the Oshino, Japan is home to almost 9,000 people. A FANUC plant that produces close to 5,000 banana colored robots per month that completely outnumbers the population of the town by almost seven to one. So how does the factory achieve such a mind-blowing foot of efficiency, there is a group of robots that build themselves, test themselves and inspect themselves sounding absurd. Almost a decade ago many of the innovators or business leaders would have considered that such things as science fiction or centuries away old story. The FANUC factory is on the world’s first industry that can have 24/7 operation with the reality-driven approach and intelligent robots. This is one of the prime examples so if we decide to develop a complete manufacturing plant using just AI and robots we are just one step away from implementing the solution.
Engineers working at the Advanced Manufacturing Research Center Factory 2050 in Sheffield, UK have started using Artificial Intelligence on the workshop floor to understand how the manufacturing plant actually reacts on the workshop floor. The aim of the research plus factory is to understand how the robots will react to an operational floor and how the system can be made more efficient? The aim is to build something much more autonomous system that can manufacture anything from a simple electronic toy to modern jumbo jet making the industry 4.0 accessible, revolutionizing the shop floor productivity. First, the demonstration will be provided with an emerging AI strategy and harnessing the innovative work that is being done to develop the AI and Machine Learning technique across the Advanced Manufacturing Research Center (AMRC) and provide real-time use cases for the industrial environment.
Making AI Smarter
In manufacturing, the maintenance and associated downtime represent a huge expense for the industries. They all have a crucial impact on the bottom line in asset reliant operation with more focus on industrial production. A study was recently conducted estimated around $50 billion manufacturing industry losses with an unplanned downtime costs and the asset failure close to 42 percent of the time cause this failure. The predictive analysis combined with predictive maintenance has become much more important as a solution for the manufacturer, they want to be ready if the failure occurs and where it could occur. Surprise failure can ruin the delivery time making the manufacturing process inefficient.
Predictive maintenance can add an extra layer of solution to improve the manufacturing industry, which wants to gain from the innovative technology to gain improved manufacturing efficiency. Predictive analysis is combined effort of machine learning and an artificial neural network that can be able to formulate the predictions before the malfunction occurs. The manufacturing industry works on different components and process types, with each unplanned downtime the enterprise’s losses the potential manufacturing capacity and loss in the revenue.
What We Should Look Out for?
Modern markets are being developed with zero downtime and quicker reactionary time as the deadlines are coming closer to the order date. The market is volatile and demand for faster production is just another requirement to be competitive in the market. It’s also making the manufacturers find new methods of manufacturing to improve or maintain the already set quality and comply with all the regulations and standards. Customers expect faultless products that use manufacturing to the threshold level of quality within the stipulated time. Quality 4.0 involves the use of AI algorithms that will notify the manufacturing teams about the emerging production faults that will likely cause product quality failure issues. The faults can include various abnormalities, changes in raw material in quality and production defects.
Manufacturers have to maintain a high level of quality in production; with the quality 4.0 enable the manufacturer to collect the data from the usage and performance of the product. It’s quite imperative that they maintain the required quality with the information that can improve the product development teams for both the strategic and identical cases in engineering decisions.
The international federation for robotics recently predicted that by the end of 2020 we will have close to 20 million industrial robots working in factories all over the world. As more and more manual labor and menial jobs are taken by the robots, workers will now have to be trained for the more advanced profession of design, maintain and programming. It’s imperative that the human-machine collaboration should be more efficient to make the manufacturing phase of robotic deployment safer. Robots and Human need to collaborate if we want to deliver efficient results. Advances in the AI will initially be combined with collaborations as a central idea. Making the robots handle more of cognitive tasks and make decisions in the real-time environment will be the next step.
Improving the Design for AI
Artificial Intelligence is also changing the way manufacturers design the production workflow. Safety and efficiency are prime pillars that will be implanting the design factors; designers and engineers need to understand the AI algorithm before starting the designing. The brief should include data describing the restrictions and various parameters that can be implemented with the material type, available production methods combined with revenue restrictions and time constraints. The designers will have an advantage of leveraging an objective process that will have a step-by-step production statement. The design will widely depend on the product and testing should be delivered with actual performance with various manufacturing scenarios and conditions.
The industry revolution 4.0 will be led by the Artificial Intelligence (AI), it will not be restricted towards the production floors, it will be able to optimize the supply chain capacity and assist the companies to optimize the manufacturing for the supply chains. AI can be able to apply the predictive model to different factors that will affect the supply of raw materials and output by demand, competitions, and geopolitics. AI will not just be technology but much closer to decision analysts.
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