Multi-objective optimisation of drilling process variables of LM5 alloy using grey relational analysis


Juliyana S. J., Prakash J. U., Rubi C. S., SALUNKHE S. S., Cep R., Nasr E. A.

Discover Mechanical Engineering, cilt.4, sa.1, 2025 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 4 Sayı: 1
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1007/s44245-025-00148-w
  • Dergi Adı: Discover Mechanical Engineering
  • Derginin Tarandığı İndeksler: Scopus
  • Anahtar Kelimeler: Drilling, Grey relational analysis, LM5 alloy, Orthogonal array, Surface roughness and burr height, Taguchi technique, Thrust force
  • Gazi Üniversitesi Adresli: Evet

Özet

LM5 alloy, with its excellent durability against corrosion, is ideal for marine and aesthetic applications. This investigation aims to improve the drilling process variables of the LM5 aluminium alloy to fulfil the specific needs of the naval industries, including naval construction, offshore buildings, and undersea vehicles. In order to gain a greater insight into how drilling process parameters like feed rate (FR), spindle speed (SS), and type of drill material (DM) affect responses such as burr height (BH), surface roughness (SR), and thrust force (TF) while drilling. The LM5 aluminium alloy plate was made using a stir-casting method. Drilling studies were executed utilising a design-of-experiment (DOE) technique L9 orthogonal array (OA). Thrust force was measured with a piezo-electric dynamometer. The surface roughness was assessed with an SR tester, and the BH was determined with a vision measurement instrument. GRA simultaneously determines the ideal process variables for machining LM5 alloy with the lowest TF, SR, and BH. It is observed from the ANOVA table that spindle Speed is the most effective control factor for maximum GRG (58.31%), feed rate (29.9%) and Drill material (9.84%). As a result of GRA, the Spindle Speed was identified as the most critical control element. The statistical models are created using the multiple linear regression method and employed to determine response parameters due to multiple control factors. The model’s accuracy was successfully validated using confirmation tests.