Abstract

Surface machining using hard turning is an intricate operation due to the influence of multiple machining parameters, their non-linear interactions, and the inherent variability introduced by different experimental trials. This study proposes a Linear Mixed Model (LMM) for predicting surface roughness, effectively addressing the challenges in traditional linear models, posed by the influencing factors, non-linearity, and interactions. The LMM incorporates variability from both fixed effects, such as cutting parameters (feed rate, depth of cut, and cutting speed), and random effects arising from tool wear across experimental runs. As a result, it provides a more comprehensive understanding of how these factors interact to impact surface roughness. The model’s performance is evaluated using the Akaike Information Criterion (AIC), with results demonstrating that the LMM outperforms conventional linear models by achieving lower AIC values, indicating better fit and predictive capability. Cross-Validation (CV) is employed to assess the model’s reliability. The findings highlight the importance of accounting for random effects due to multiple machining trials and complex interactions in surface roughness prediction, offering a framework for more accurate and reliable modeling in hard-turning operations.

Meeting Name

IISE Annual Conference & Expo 2025

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Turning, Machining, Statistical Inference, Linear Mixed Model, Random Effect

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Publication Date

31 May, 2025

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