Critical Synthesis of CNC Wood Machining Research in the Industry 5.0 Era

This paper reviews computer numerical control wood machining research published between 2020 and 2025, exploring how to choose cutting parameters so that surface quality, energy use, tool wear, and environmental impact are balanced. Wood’s anisotropy, hygroscopicity, and biological variability make it so different, even for the same species or board. Two clear shifts have emerged. Optimization work is moving from one-factor-at-a-time experiments toward multi-objective methods built on neural networks, genetic algorithms, and hybrid artificial intelligence schemes. In parallel, edge computing and predictive maintenance are bringing real-time decision-making down to the machine itself. Two gaps also stand out: few studies test sensors and artificial intelligence models under dusty, high-vibration conditions, and existing digital twins remain static and ignore the internal stresses, knots, and grain irregularities revealed during cutting. The paper closes by arguing for a total-process view of sustainability, optimizing surface quality, dust emissions, auxiliary energy use, and tool life simultaneously.