Manufacturing quotation platform for turned parts: The PLAT4M project
Antonio SCIPPA, Niccolò GROSSI
Abstract. The increasing digitalization in the manufacturing industry, the development of digital models (Digital Twins) representing products, processes, resources, and systems, as well as the adoption of AI to support classification and automatic reasoning approaches, are paving the way for the raise of platform-based business approaches for manufacturing very shortly. The PLAT4M project aims at defining a general modelling framework, and supporting methodologies, enabling a platform-based quotation approach for mechanical components. Within this framework, customers will be able to submit technical specifications of the requested products and receive a quotation in terms of costs and delivery date. In this work the ongoing activities related to the developing of the AI-based approach for generating process plans and characterizing process steps for turned parts is presented.
Keywords
Manufacturing Platforms, Machine Learning, Turning
Published online 9/10/2025, 8 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Antonio SCIPPA, Niccolò GROSSI, Manufacturing quotation platform for turned parts: The PLAT4M project, Materials Research Proceedings, Vol. 57, pp 647-654, 2025
DOI: https://doi.org/10.21741/9781644903735-76
The article was published as article 76 of the book Italian Manufacturing Association Conference
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] D. Wu, D. W. Rosen, L. Wang, D. Schaefer, Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation, CAD 59 (2015) 1-14. https://doi.org/10.1016/j.cad.2014.07.006
[2] A. Niazi, J. S. Dai, S. Balabani, L. Seneviratne, Product cost estimation: Technique classification and methodology review. J. of Manuf. Sci. E. 128(2) (2006) 563-575. https://doi.org/10.1115/1.2137750
[3] O. Duran, J. Maciel, N. Rodriguez, Comparisons between two types of neural networks for manufacturing cost estimation of piping elements, Expert Syst. Appl. 39(9) (2012) 7788-7795. https://doi.org/10.1016/j.eswa.2012.01.095
[4] F. Ning, Y. Shi, M. Cai, W. Xu, X. Zhang, Manufacturing cost estimation based on a deep-learning method. J. Manuf. Syst., 54 (2020), 186-195. https://doi.org/10.1016/j.jmsy.2019.12.005
[5] Y. Soyoung, K. Namwoo, Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization, Expert Syst. Appl, 183 (2021). https://doi.org/10.1016/j.eswa.2021.115430
[6] M. Behandish, S. Nelaturi, C. S. Verma, M. Allard, Automated process planning for turning: a feature-free approach, Prod. Manuf. Res. 7,1 (2019) 415-432. https://doi.org/10.1080/21693277.2019.1634650
[7] Y. Altintas, Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, second ed., Cambridge University Press, 2012. https://doi.org/10.1017/CBO9780511843723
[8] T.L. Schmitz, B.P. Mann, Closed-form solutions for surface location error in milling, Int. J. Mach. Tools Manuf. 46 (2006), 1369–1377. https://doi.org/10.1016/j.ijmachtools.2005.10.007
[9] E. Budak, Y. Altintas, Analytical Prediction of Chatter Stability in Milling – Part II: Application of the General Formulation to Common Milling Systems, J. Dyn. Sys. Meas. Control, 120-1 (1998), 31-36. https://doi.org/10.1115/1.2801318
[10] G. Totis, P. Albertelli, M. Torta, M. Sortino, M. Monno, Upgraded stability analysis of milling operations by means of advanced modeling of tooling system bending. Int. J. Mach. Tools Manuf. 113 (2017) 19–34. https://doi.org/10.1016/j.ijmachtools.2016.11.005
[11] H. Cherukuri, E. Perez-Bernabeu, M. Selles, T. Schmitz, Machining Chatter Prediction Using a Data Learning Model, J. Manuf. Mater. Process. 3 (2019), 3, 45. https://doi.org/10.3390/jmmp3020045
[12] B. Lutz, D. Kisskalt, D. Regulin, J. Franke, AI-based Approach for Predicting the Machinability under Consideration of Material Batch Deviations in Turning Processes, Procedia CIRP, 93 (2020), 1382-1387. https://doi.org/10.1016/j.procir.2020.04.100
[13] A. Ertürk, H.N. Özgüven, and E. Budak, Analytical modeling of spindle–tool dynamics on machine tools using Timoshenko beam model and receptance coupling for the prediction of tool point FRF, Int. J. Mach. Tools Manuf., 46 (2006), 1901–1912. http://dx.doi.org/10.1016/j.ijmachtools.2006.01.032
[14] N. Grossi, A. Scippa A, G. Campatelli, Diametral error correction in turning slender workpieces: an integrated approach. Int. J. Adv. Manuf. Technol., 130 (3-4) (2024), 1393-1404. http://dx.doi.org/10.1007/s00170-023-12825-9.
