Evidence map›Paper›PMID 37512686›Full record

ArticleMicromachines2023

Image Processing Approach to Investigate the Correlation between Machining Parameters and Burr Formation in Micro-Milling Processes of Selective-Laser-Melted AISI 316L.

Fatih Akkoyun, Zihni Alp Cevik, Koray Ozsoy, Ali Ercetin, Ibrahim Arpaci

Abstract read
In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Fatih AkkoyunDepartment of Mechanical and Metal Technologies, Trabzon Vocational School, Karadeniz Technical University, Trabzon 61300, Turkey.ORCID 0000-0002-1432-8926
Zihni Alp CevikDepartment of Electronic and Automation, Besni Ali Erdemoğlu Vocational School, Adıyaman University, Adıyaman 02302, Turkey.ORCID 0000-0003-3141-0160
Koray OzsoyDepartment of Machine and Metal Technologies, Isparta OSB Vocational School, Isparta University of Applied Sciences, Isparta 32092, Turkey.ORCID 0000-0001-8663-4466
Ali ErcetinDepartment of Naval Architecture and Marine Engineering, Maritime Faculty, Bandırma Onyedi Eylül University, Bandırma 10200, Turkey.ORCID 0000-0002-7631-1361
Ibrahim ArpaciDepartment of Software Engineering, Faculty of Engineering and Natural Sciences, Bandırma Onyedi Eylül University, Bandırma 10200, Turkey.ORCID 0000-0001-6513-4569

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In industrial manufacturing and research laboratories, precise machining of work materials is crucial to meet the demand for fast assembly and sustainable high-quality production. Precise machining procedures play a vital role in manufacturing compatible parts that meet the production requirements. This study investigates the impact of different parameters on burr formations and slot dimensions during the micro-milling of AISI 316 material. A careful examination was conducted using scanning electron microscopy (SEM) images under varying conditions. The variables considered include cutting speed, feed rate, and depth of cut. The main finding revealed that the feed rate and depth of cut significantly influence burr formation, with lower rates and depths resulting in noticeable reductions. A higher feed rate was associated with more pronounced burr formation. Moreover, burr widths on the down-milling sides were typically wider than those on the up-milling sides due to continuous chip formation and compressive forces during down-milling. Utilizing image processing, the study further quantified burr and slot widths with high accuracy, offering a reliable method to characterize burr formation. This research presents potential ways to minimize burr formation during micro-milling processes by effectively managing machining parameters.

Indexed as

additive manufacturingburr detectionburr formation analysisclassificationcomputer visionimage analysis

Identifiers

PMID37512686
PMCPMC10385879

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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.