Speaker
Description
Keywords: Digital Transformation, Real Time, IIoT, Digital Sensor, Industry 4.0
ABSTRACT
In recent years, one of the main challenges in industry has been obtaining 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, i.e., 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. In automotive industry it has not been an exception. These challenges have been associated with the concept of Industry 4.0, specifically with Big Data and IIoT (Industrial Internet of Things) techniques. In this line, a multitude of applications have been proposed and developed, see [1]. The main problem comes during the implementation in the industry since the need to introduce sensors in each one of the machines, to wire it, etc., supposes a very high cost that in some cases minimizes its implementation to critical machines.
To develop this technology in a massive way in one of our previous works [2] a new concept was proposed, using the information available in the PLCs to carry out a low-cost Big Data project. In [2] a standard called Miniterm was developed, which proposes to measure the actuation time of a mechanical component connected to a PLC, as a virtual sensor to measure the deterioration of the components without the need to install the sensors proposed in the literature such as ultrasound. This standard is being adopted by Ford Motor Company where there are currently more than 22,000 Miniterms installed in different Ford Europe factories.
In the press shop where the stamping machines are equipped with many sensors, we have this information available in the PLC. Therefore, a new criterion based on Miniterms called Criterion-360 has been developed. This criterion allows us to obtain working parameters of the presses in each cycle. For each degree of rotation of the main axis of the press, data from sensors is stored in the PLC, then the data block is sent through the industrial network to the office network and stored in a database. Tonnage information carried out by the press, the pressure and position of the cushion, the compensation pressure and the press speed are currently being monitored. This methodology allows us to access the data in each cycle from any device in real time.
With this database at our disposal, many possibilities for the development of new applications and tools are opened towards what has been called “Smart Press Shop 4.0”. In our previous works, we have begun to explore different possibilities, such as being able to predict the failure of the press, [3], energy savings in the plant [4], or beginning to move towards a digital twin of the stamping process [5]. This article aims to define the concept of "Smart Press Shop 4.0" through the Criterion-360 for the development of viable industrial Big Data of the stamping process, exploring new measurables and possible applications.
REFERENCES
[1] Zheng, T., Ardolino, M., Bacchetti, A. and Perona, M. (2021) The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review. International Journal of Production Research, 59:6, pp. 1922-1954.
[2] García, E., Montes, N., Llopis, J. and Lacasa, A (2022). Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era. Sensors MDPI.
[3] Peinado-Asensi, I., Montes, N., and García, E. (2022). In Process Measurement Techniques Based on Available Sensors in the Stamping Machines for the Automotive Industry. In Key Engineering Materials, vol. 926, Trans Tech Publications, Ltd., pp. 853–861.
[4] Peinado-Asensi I., Montes N. and García E. (2022). In Situ Calibration Algorithm to Optimize Energy Consumption in an Automotive Stamping Factory Process. In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, pp. 169–176.
[5] Peinado-Asensi, I., Montes, N., García, E., and Falcó, A. (2022). Towards a Hybrid Twin Model to Obtain the Formability of a Car Body Part in Real Time. In Key Engineering Materials, vol. 926, Trans Tech Publications, Ltd., pp. 2277–2284.