Sonae Arauco manufactures wood-based panels through a continuous process involving the feeding of raw materials (wood and resins from external suppliers), their processing (through heat and pressure) and finishing of the panels (sanding and cutting) and/or further processing (such as surfacing with decorative papers from external suppliers). Panels are then supplied to industrial clients from every size to satisfy a wide variety of end uses.
The Sonae Arauco’s use case will focus precisely on the process of melamine surfaced wood panels at the Linares, Spain production site towards a zero-defect manufacturing approach.
In a nutshell, the melamine process consists in coating a raw board (particle board or medium density fiberboard) with melamine impregnated papers. Melamine papers are then pressed to the raw board surface using a combination of pressure and temperature.
Detailing the process, the production flow process starts with the production of unsanded particle boards in the press. The unsanded boards are then, in the most normal flow, sanded in the sanding line. The sanded boards are then stored in the crane warehouse to be either dispatched as finished goods, to be cutted or to be melamined. The melamine production is done in 2 melamine lines which cover the clients’ demand. The melamine production plan triggers the requirements for the impregnation line and for the sanded particle boards (or MDF boards purchased to Valladolid, Spain and Mangualde, Portugal). The impregnation production transforms the dry paper rolls in pallets of impregnated paper. The paper is either transported immediately to the melamine presses or stored in the systraplan warehouse. The produced melamine boards, combining the paper and boards and finishing type – given by the melamine plates – are then stored in the finished goods warehouse. The melamine boards can be cutted either in the melamine line or in next operation in the Giben saw.
| Web resources: | https://www.openzdm.eu/pilots/sonae-arauco/ |
The vision of the future process with the contribution of the openZDM solutions targets an improved decision-making at the plant level to reduce defects towards a zero defects paradigm, that will facilitate defect prediction and recommendation of production recipes to adjust machine parameters according to predicted defects. In turn, improved products can be developed using the data-driven knowledge acquired, resulting in less wasted material. Towards this direction, the openZDM project performed an LCA at the beginning ot he project to compare it with the final one and also it supports integration through its platform to an LCA tool for on-demand LCA.
OPENZDM solutions for AI quality assessment and decision support aim for data driven defect prediction and estimation of machine components degradation through drifts in data distribution. The main goal is to predict defects such as broken paper, dust or glued paper so that changes in machinery/parameters can be performed and the defect avoided and also to estimate degradation based on slight and smooth changes in the process start occurring due to component wear-out / degradation and, if data is representative enough, it may be different data distributions in time.
OPENZDM is expected to contribute to the SONAE Arauco use case in the aspects of proactive quality control towards zero-defect manufacturing through the digital twin enabled machine learning approaches for data analytics and quality assessment.
- Not specified
In the demonstrator of SONAE Arauco ES, the openZDM digital tools and platform will be used to achieve the following:- improvement of data collection and in-situ monitoring, namely in what concerns time-related annotation
- improvement of inspection of quality issues through artificial intelligence and digital-twin technology.
- further advances in predictive quality based on data-driven and model-driven approaches for defect prediction and quality assessment.
- improvement in manufacturing decision making for ZDM strategies and process adaptation. This will also include recommendation models for new products, an explainable AI approach for a better understanding of Machine Learning decisions and a graphical user interface through UX/UI strategies.
thus effectively support productivity through the following aspects:
1. Improved decision-making at the plant level to reduce defects towards a zero defects paradigm
2. Improved product development (faster and with less waste generated)
3. Improved evaluation of machinery components