Evidence map›Paper›PMID 37241331›Full record

ArticleMaterials (Basel, Switzerland)2023

An Adaptive Algorithm and Additively Manufactured Punch Used to Form Aluminum Sheet Metal Parts.

Vlad Andrei Ciubotariu, Cosmin Constantin Grigoras, Valentin Zichil, Ana-Maria Rosu

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Vlad Andrei CiubotariuDepartment of Industrial Systems Engineering and Management, "Vasile Alecsandri" University of Bacău, 157 Calea Mărăşeşti, 600115 Bacău, Romania.ORCID 0000-0002-9060-593X
Cosmin Constantin GrigorasDepartment of Engineering and Management, Mechatronics, "Vasile Alecsandri" University of Bacău, 157 Calea Mărăşeşti, 600115 Bacău, Romania.ORCID 0000-0003-0621-1258
Valentin ZichilDepartment of Engineering and Management, Mechatronics, "Vasile Alecsandri" University of Bacău, 157 Calea Mărăşeşti, 600115 Bacău, Romania.
Ana-Maria RosuDepartment of Chemical and Food Engineering, "Vasile Alecsandri" University of Bacău, 157 Calea Mărăşeşti, 600115 Bacău, Romania.ORCID 0000-0001-5551-4752

Funding

National Council for the Financing of Higher Education, Romania CNFIS-FDI -F-2023-0085
6 · The paper itself

Abstract

Self-adaptive mechanisms are gaining momentum in industrial processes. It is understandable that as the complexity increases, the human work must be augmented. Considering this, the authors have developed one such solution for the punch-forming process, using additive manufacturing, i.e., a 3D-printed punch, to draw into shape 6061-T6 aluminum sheets. This paper aims to highlight the topological study used to optimize the punch form shape, the methodology of the 3D printing process, and the material used. For the adaptive algorithm, a complex Python-to-C++ bridge was created. It was necessary as the script has computer vision (used for calculating stroke and speed), punch force, and hydraulic pressure measurement capabilities. The algorithm uses the input data to control its subsequent actions. Two approaches are used in this experimental paper, a pre-programmed direction and an adaptive one, for comparison purposes. The results, namely the drawing radius and flange angle, were statistically analyzed using the ANOVA methodology for significance. The results indicate significant improvements when using the adaptive algorithm.

Indexed as

3D-printed punchadaptive algorithmcomputer visionPython-to-C++ bridgetopological study

Identifiers

PMID37241331
PMCPMC10224241

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.