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

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AKKOYUN F., Cevik Z. A., Ozsoy K., Ercetin A., Arpaci I.

Micromachines, cilt.14, sa.7, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 7
  • Basım Tarihi: 2023
  • Doi Numarası: 10.3390/mi14071376
  • Dergi Adı: Micromachines
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Anahtar Kelimeler: additive manufacturing, burr detection, burr formation analysis, classification, computer vision, image analysis
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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.