Industrial Internet of Things and Big Data Techniques for the Smart Press Shop 4.0 Development in Automotive Industry

Abstract

Abstract In recent years, one of the main challenges in industry has been to obtain work parameters in real time for later analysis to understand the process better. The purpose is to obtain substantial information about the process for better decision-making and to find out what happens during manufacturing to know both the state of the equipment and the product, looking for savings in maintenance costs and quality controls among others. This has not been an exception for the automotive industry. These challenges have been associated with the concept of Industry 4.0, specifically with Big Data and IIoT (Industrial Internet of Things) techniques. Following this concept, many of the companies and research teams develop their algorithms and ideas based on the installation of new sensors without taking into account the implications that this proposal has in real factories. The cost of the sensor, the installation setup, maintenance, machine modifications and replication for hundreds of machines make many of the ideas fail when innovation is implemented in the companies. For that reason, in our previous work we have introduced a novel concept in Industry 4.0, the development of algorithms by using exclusively the information that is available in the equipment. Machines, as well as presses and general automated systems, include sensors for their automated normal production. These sensors are installed and the information they provide is almost free. Following this philosophy, the present paper proposes the Criterion-360 for real press shops. With this criterion, it is possible to measure a lot of variables with no cost, not only the tonnage, but also, many variables such as the press speed, Counterbalance and overload pressure, Cushion Pressure & Position, lubrication, etc., and therefore with this amount of information many possibilities arise. As the new developed system does not need new sensors and additional installation setup, it is easy and cheap to replicate it in other presses and plants. This article presents two tools that have been developed from the information obtained with this criterion, these tools are currently in operation and have been used to modify work processes and get the press to work in optimal conditions.