Application of Machine Learning to Bending Processes and Material Identification

Daniel J. Cruz, Manuel R. Barbosa, Abel Dias dos Santos, Sara S. Miranda, Rui Amaral

Metals 2021 article cited by 35

Source Open-access PDF More in Engineering & Materials

importance 3/10 ai assisted Metal Forming Simulation Techniques sheet metal forming stage: research

Problem

Manufacturers need to identify material constitutive parameters from bending tests and predict punch displacement to achieve a desired bending angle despite springback in press-brake air bending.

Why it matters

Accurate springback prediction improves efficiency and control in sheet metal forming, reducing waste and rework.

What the AI did

Shallow artificial neural networks were developed to (1) identify constitutive model parameters from force–displacement curves of a standard bending test and (2) predict punch displacement and springback angle in press-brake air bending.

Results

not stated in source

Impact

The work demonstrates ML modeling capability for two manufacturing tasks, but no measured results appear in the abstract.

Record

Venue	Metals
Published	2021
Work type	article
Authors	Daniel J. Cruz, Manuel R. Barbosa, Abel Dias dos Santos, Sara S. Miranda, Rui Amaral
Citations	35 (OpenAlex, at import time)
References	49
Model or method	shallow artificial neural networks (ANN)
Organization	not stated in source
Primary source	https://doi.org/10.3390/met11091418
Open access	gold
Keywords	machine learning, ANN, springback, bending, material identification, manufacturing

Importance rating

3 / 10

incremental / niche

Springback prediction aids efficiency and control in sheet metal forming per abstract; a narrow manufacturing task with no stated scale or impact.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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