MARATTO

article · Heliyon

RETRACTED: Optimization and prediction of CBN tool life sustainability during AA1100 CNC turning by response surface methodology

202322 citationsOpen accessDebre Tabor University

In plain language

This research investigates methods to extend the operational life of cubic boron nitride coated cutting tools during the computer numerical control turning of aluminium alloy AA1100. This material is commonly used in automotive flexible shaft couplings because of its strength, corrosion resistance, thermal performance, and machinability. Using an experimental design framework combined with analysis of variance and response surface methodology, the study tested variations across spindle speeds, feed rates, and depths of cut. Spindle speeds ranged from 900 to 1500 rpm, feed rates from 0.1 to 0.25 mm per revolution, and depths of cut from 0.1 to 0.4 mm. The statistical optimisation revealed that specific operating settings, namely lower depths of cut paired with higher spindle speeds and feed rates, prolonged tool life beyond twenty minutes, thereby reducing interruptions during the machining process.

Key takeaways

  • Cubic boron nitride coated insert tools were evaluated to enhance tool longevity when machining aluminium alloy AA1100.
  • Experimental parameters included four levels each for spindle speed, feed rate, and depth of cut evaluated via response surface methodology.
  • Optimal turning settings were identified as a depth of cut of 0.1 mm, feed rates between 0.2 and 0.25 mm per revolution, and spindle speeds between 1300 and 1500 rpm.
  • The identified parameter combination enabled an extended cutting tool life exceeding 20 minutes.

Why it matters

In manufacturing, worn cutting tools cause production halts, higher costs, and defects in finished components. By identifying the exact machine settings that minimise tool wear on automotive-grade aluminium alloys, manufacturing facilities can cut downtime and improve productivity without needing new machinery.

Commercialisation angle

The findings are directly applicable to manufacturing and automotive component workshops machining AA1100 flexible shaft couplings. As an applied and tested optimisation study on standard computer numerical control machinery, these operational parameters could be implemented immediately by machine operators and process engineers to improve cutting tool durability, though the retraction of the underlying publication warrants independent validation prior to adoption.

AI-generated from the published abstract. Always read the original work before citing.

Abstract

The aluminium alloy (AA1100) was familiar with automotive flexible shaft coupling applications due to its high strength, good machinability, and superior thermal and resistance to corrosion characteristics. Machining tool life drives the prominent role for deciding the product quality (machining) act aims to productivity target with zero interruptions. The novelty of this present investigation is the focus on increasing tool life during the complexity of CNC turning operation for AA1100 alloy by using CBN coated insert tool with varied input parameters of spindle speed (SS), feed rate (f), and depth of cut (DOC). Design of experiment (L16), analysis of variance (ANOVA) statistical system adopted with response surface methodology (RSM) is implemented for experimental analysis. The turning input parameters of SS, f and DOC are considered as factors and its SS (900, 1100, 1300, and 1500 rpm), f (0.1, 0.15, 0.2, and 0.25), and DOC (0.1, 0.2, 0.3, and 0.4 mm) values are treated as levels. The investigational analysis was made with the ANOVA technique and the desirability of high tool life with input turning parameters was optimized by RSM, and sample no 11/16 was predicted as high tool life and performed with extended working hours compared to other samples. The RSM optimized best turning parameter combinations are 0.1 mm DOC, 0.2mm/rev to 0.25mm/rev f, and 1300 rpm-1500 rpm SS, facilitating a higher tool life of more than 20min.

Research topics

  • Advanced machining processes and optimization
  • Advanced Machining and Optimization Techniques
  • Metallurgy and Material Forming

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.1016/j.heliyon.2023.e18807

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.