Work

Reinventing the Trochoidal Toolpath Pattern by Adaptive Rounding Radius Loop Adjustments for Precision and Performance in End Milling Operations

Year: 2025

Type: article

Abstract: The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. Twenty experimental trials have been performed utilizing a face-centered central composite design through a response surface approach. Artificial Neural Network (ANN) models were built to forecast outcomes, utilizing four distinct learning algorithms: the Batch Back Propagation Algorithm (BBP), Quick Propagation Algorithm (QP), Incremental Back Propagation Algorithm (IBP), and Levenberg–Marquardt Back Propagation Algorithm (LMBP). The efficacy of these models was evaluated using RMSE, revealing that the LMBP model yielded the lowest RMSE for surface roughness (Ra), nose radius wear, and resultant cutting force, hence demonstrating superior predictive capability within the trained dataset. Additionally, a Genetic Algorithm (GA) was employed to ascertain the optimal machining settings, revealing that the ideal parameters include a cutting speed of 85 m/min, a feed rate of 0.07 mm/tooth, and a rounding radius of 7 mm. Moreover, the detachment of the coating layer resulted in alterations to the tooltip cutting edge on the machined surface as the circular loop distance increased. The initial arc radius fluctuated by 33.82% owing to tooltip defects that alter the edge micro-geometry of machining. The measured and expected values of the surface roughness, resultant cutting force, and nose radius wear exhibited discrepancies of 6.49%, 4.26%, and 4.1%, respectively. The morphologies of the machined surfaces exhibited scratches along with laces, and side flow markings. The back surface of the chip structure appears rough and jagged due to the shearing action.

Source: Journal of Manufacturing and Materials Processing

DOI: 10.3390/jmmp9060171

Authors: Santhakumar Jayakumar, Sathish Kannan, Poongavanam Ganeshkumar, U. Mohammed Iqbal

Institutions: SRM Institute of Science and Technology, Amity University

Language: English

Topic: Advanced machining processes and optimization

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funder: SRM Institute of Science and Technology