Neuro-fuzzy assessment of machined wood fibre–reinforced magnesium oxide composite

ABSTRACT

High quality processing key to improving product quality and enterprise benefits. In this work, an adaptive network–based fuzzy inference system (ANFIS) was combined with milling experiments to understand the effects of tool geometry and milling parameters on the surface quality of wood fibre–reinforced magnesium oxide composite (WRMC). Specifically, changes in surface roughness (Ra) and damage of WRMC at different milling conditions were assessed using ANFIS and micro-analysis methods. Development of ANFIS models were confirmed to be reliable for predicting surface roughness. Changes in surface roughness at different milling conditions were determined, and the lowest surface roughness was obtained at the highest rake angle, highest cutting speed, and smallest milling depth. Furthermore, pitting-type damage irregularly distributed on the machined surface is attributed to the pulling out and debonding of wood fibres. Overall, high cutting speed, shallow cutting depth, and high rake angle is recommended for fine machining of WRMC where a smooth surface is desired. This study showcases how neuro-fuzzy models can be combined with conventional micro-analysis to optimize milling parameters for WRMC to minimize surface damage, and paves the way for future studies to optimize cutting tool life and energy consumption.

KEYWORDS:

• Cutting quality
• fuzzy logic
• neural networks
• surface damage
• WRMC

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors gratefully acknowledge the considerable support of the CT WOOD at Luleå University of Technology. This work was supported by the National Natural Science Foundation of China [31971594], the Natural Science Foundation of the Jiangsu Higher Education Institutions of China [21KJB220009], the Technology Innovation Alliance of Wood/Bamboo Industry [TIAWBI2021-08], the Self-Made Experimental and Teaching Instruments of Nanjing Forestry University in 2021 [nlzzyq202101], the Nanjing Forestry University Undergraduate Innovation Project [202110298158h], the Qin Lan Project, and the International Cooperation Joint Laboratory for Production, Education, Research and Application of Ecological Health Care on Home Furnishing.