Politecnico di Bari, Università degli studi di Napoli Federico II, Università degli Studi di Padova, Brembo S.p.A, Camozzi Group S.p.A., Prima Additive S.R.L., Leonardo S.p.A., Thales Alenia Space Italia S.p.A.
The primary objective of this project is to create an advanced autonomous and intelligent Additive Manufacturing (AM) platform that utilizes innovative sensing solutions, big data mining, self-learning, and adaptive control to produce defect-free 3D printed parts. This will involve the implementation of new defect prevention, detection, correction, and postprocessing solutions, as well as the design of complex shapes and defect-tolerant geometries.
The project will be based on five main research pillars: design for zero-waste, inspection and measurement, modeling, control, and improvement. The project will specifically focus on addressing complex challenges in AM, such as lattice structures and support-free parts, to achieve first-time-right AM capabilities and improve geometrical conformity and roughness. The system will possess autonomous learning, prediction, self-adaptation, and self-correction capabilities, making it a novel and advanced type of AM system.
Task 6.4.1 New solutions for AM sensing (POLIMI, POLIBA, CAMOZZI, PRIMA, UNINA) New solutions for measuring process signature in-situ and in-line
Task 6.4.2 In-situ and ex-situ correlation of defect for inline qualification (POLIMI, UNIPD)
Approaches to link the in-situ signature with the defects observed on the printed products
Task 6.4.3 Big data mining (POLIMI, POLIBA, UNINA) – Use of machine learning, data fusion, artificial intelligence (AI) and digital twin per zero-waste production in AM
Task 6.4.4 Feedforward e feedback control in metal AM (POLIMI, POLIBA, UNINA) – new solutions for predicting and healing defects insitu and inline
Task 6.4.5 Process chains for improved surface finish and structural integrity of highperformance mechanical parts (Brembo, UNIPD). To improve the structural durability of highperformance mechanical parts made of Al and Ti alloys, the entire AM-based process chain needs to be optimised, including thermal treatments, and finishing operations. State-of-the-art fatigue modelling and testing will be included. Sustainable finishing operations based on cryogenic cooling have the potential to increase fatigue and corrosion resistance and will be investigated.
Task 6.4.6 Design and manufacturing of defect-tolerant lattice structure (POLIMI Leonardo) – new solutions for lightweight lattice structures via defect-tolerant design and in-situ data mining
Task 6.4.7 Hybrid extrusion-based AM (POLIBA) smart sensing as a support to combine
additive and subtractive solutions for net-shape zero-defect products
Task 6.4.8 New solutions of space AM (POLIMI, THALES) smart sensing for first-time Am in unknown, unexpected conditions.
We expect to design a new generation of Additive Manufacturing (AM) solutions that minimize resource waste by reducing defects, supports, and enabling first-time-right production, even for non-expert users in small and medium-sized enterprises (SMEs).
The expected results include:
Overall, the project is expected to result in significant advancements in AM technology, enabling the production of green and sustainable products with improved reliability and reduced waste.
WASTELESS project KPI’s: