Analisi dei dati IoT: tecniche e strumenti per insights in tempo reale
Gianluca Mazzi
14 November 2023
Smart predictive manifacturing
The project was carried out with the support of POR – Objective "Increase in business innovation activity" Part ERDF European regional development fund 2014-2020.
Design, development and implementation of Digital Manufacturing solutions for Quality Prediction and Intelligent Maintenance.
Project description
The development of high efficiency production systems that allow to minimize production costs, improve productivity and product quality is universally recognized as one of the central themes of Smart Manufacturing, particularly in the vision of Industry 4.0.
High production efficiency is a necessary condition for the competitiveness of all companies, which must achieve an improvement in performance, and achieve an element of differentiation from low-cost countries through the creation of high quality products, which is particularly significant for the production system in Veneto region. Moreover, systems with high application flexibility allow the company to maintain its efficiency even in the face of extreme variability in demand, and at the same time to achieve a reduction in waste (also in terms of environmental sustainability) and energy consumption resulting from inefficient processes (energy efficiency)
In this context, it is necessary to develop integrated methods, technologies and tools for maintenance, quality control and production logistics, in line with what is specified in the Technological Trajectory #10: Solutions for advanced maintenance, quality and logistics management and decision support in complex environments.
At the equipment level, modelling and forecasting approaches must be developed for the machine’s state of degradation starting from data acquired from the field through process and product sensors. These models allow to define condition-based maintenance solutions able to predict deviations and avoid defects, without interfering with system performance.
At the system level, it is necessary to build models and methods to predict the impact of a defect on subsequent stages of production and to identify solutions to eliminate defects, including in-line rework and repair, preventing defects from being identified only by end-of-line inspection, with the associated cost in terms of scrap.
The study and implementation of this type of forecasting model is at the heart of the PreMANI project, and is a particularly innovative part of it, given the methodologies that will be used for the purpose and that will be applied to a wide range of industrial use cases by leveraging ICT technologies (Information & Communication Technologies), which constitute the enabling technology (as an expression of the Micro/nano electronic) of reference for the project, along with advanced production systems.
Purpose
The project aims to demonstrate the ability of these techniques to penetrate heterogeneous fields of application, characterized by very different needs, using the methodological aspects of a general nature. The project intends to develop techniques that can address the issue of the prediction of the operating characteristics of machines and plants, combining the analysis of quality (of the product) with that of efficiency (of the plants), in a context that is then described as Predictive Manufacturing.
The solutions developed belong to the Digital Manufacturing field, involving the creation of advanced tools for decision support, and components at the hardware level (dedicated sensor architectures, low-cost embedded systems for the real-time use of complex forecasting models), infrastructural level (cloud-based IT platforms), and algorithmic level (with particular emphasis on the use of machine learning techniques).
Expected results
From the point of view of the activities carried out in the project, it should be noted that PreMANI’s main objective is the creation of innovative solutions for production systems, product quality and “intelligent” maintenance. Some partners will also develop new products and machinery that incorporate the functionalities expressed by innovative solutions.
In this vision, the project has a predominant component of Industrial Research activities, as the main objective of the partners is to acquire new knowledge and skills to innovate both their production processes and their products.
For the realization of such solutions, it is essential to have data of adequate number and quality related to the use of machines and production systems. In order to carry out this data collection in a controlled environment, some partners will develop laboratory prototypes and create pilot lines. With a view to bringing the project’s innovations to the market, a secondary Experimental Development activity is also planned in which the products and services produced will be validated in operating condition that reproduce the real ones, to make further technical improvements to solutions that are not substantially final.
Partecipating subjects: CIELLE Srl, EDALab Srl, Electolux Italia SpA, Enginsoft SpA, Fonderia Corrà SpA, Galdi Srl, Improvenet, I.R.S. Srl, M3E Erl, M31 Italia Srl, RDS Moulding Technology SpA, Simnumerica Srl, Smit Srl, Sordato Srl, Statwolf Data Science Srl, T2I – Technology Tranfer and Scarl Innovation, University of Padua, University of Verona.
TOTAL EXPECTED EXPENDITURE: € 5.396.160,33
ESTIMATED TOTAL CONTRIBUTION: € 2.999.858,64
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