The goal of MONDRAGON Corporation roadmap for the coming years, as a global business group, is to change the structure of the businesses, leading their evolutions towards higher added value and developing new activities in leading sectors. Machinery Building for Capital Goods is a main industrial division within the Corporation providing high quality –high performance solutions based on smart technology in a wide variety of sectors (automotive, aerospace, energy, railway, oil &gas, capital goods, white goods, etc.). Danobat Group, Fagor Arrasate, and other leading corporate brands represent a benchmark in machines, solutions and advanced services in the area of Machine Tools.
Given the strategic importance of the Machine Tools industry, and in the context of QU4LITY, MONDRAGON proposes two process pilots in the Machinery Building for Capital Goods scenario; two realities that can be complementary in many customers’ value chains:
Use Case MONDR1:
Multi stage zero defect manufacturing railway axles production line: Manufacturing Processes with Cutting/Grinding Machinery, leaded by DANOBAT (DAN). The objective of MONDR1 pilot is to reach zero defects in the production line of axles that includes forging, heat treatment, machining (roughing and finishing), finishing stages (painting and protecting), as well as in-process and final inspection and verification operations. During this multi-stage process, several deviations or process variability may cause geometry and quality defects that cause both extensive rework operations and part scrap.
The technologies and the control loops provided by QU4LITY will make it possible to achieve observability of the product, process and resource states, throughout the system stages.
Use Case MONDR2:
Zero-Defects Manufacturing digital Hot Stamping process: Manufacturing Processes with Hot Stamping Machinery, leaded by FAGOR. The objective of this MONDR2 pilot is to reach zero defects in the hot stamping cooling temperatures, transfer speed, loss of temperature in the transfer or settings of press and identifying exactly the process developed in the manufacturing of the parts.
There are two IoT platforms included in 2 manufacturaing lines for Automotive and Railway sector for MONDRAGON pilot. IoT platforms monitor 3 grinding machines from railway sector as well as press machine, stacker, Owen and Transfer from Automiotive customer.
The variables monitored allow machine tool buider to know that the process exectuion is under treshold defined as well as enabling predictions about possible faillures. As a consequence there will be an increase of the quality production and the optimisation of the production.
The interoperability layer between two IoT Platfroms has been achieved considering OPC-UA and AAS.
Collaboration with international partners such as ATLANTIS, VTT and FHG has allowed us to include IA algorithms, specific data analitics and data sharing connectors.In spte of the fact that IoT platfrom and interopoerability has been oriented to real time optimisation the analitycs and the IA algorithms have been carryed out off-line
The variables monitored in real time throght IoT platfroms for 2 manufacturing lines enables the Zero Defect Manufuactiring goal. Multiple industrial assets monitored force to have an strategic in terms of enchacement processes.
The implementation of modular architecture interconected involving Cloud and Edge Systems, Data Modelling and Learning Service and Iot Hub produce top quality production. The introduction of Interoperability layer for gathering data from two different manufacturing lines together with OPC-UA and AAS is key for the goal.
MONDRAGON pilot is being developed considering 2 IoT platfrom and interoperability layer developed by MGEP together with OPC-UA and AAS. Real time process optimisation enables Autonomous quality outcomes and Zero Defect Manufacturing for Automotve (Fagor Arrasate )and railway (Danobat) manufacturing lines. The FA-LINK platfrom monitored industrial assets for Fagor Arrasate and SAVVY IoT platfrom for DANOBAT.
The introduction of IA algorithms by ATLANTIS and VTT are developed offline achieving high top optimisation production. On the other hand, the approach of Machine Learning approach should be further developed. The interaction of the operators, maintenance workers and R&D staff are stil crucial for Top high level Autonomous Manufacturing process Optmisation