Dongqian Wang

TL;DR: In this paper, the acceleration signals acquired by sensor were decomposed into a series of intrinsic mode functions (IMFs) by the adaptive analysis method named ensemble empirical mode decomposition (EEMD), and the IMFs which contain the feature information of milling process were selected as the analyzed signals.

TL;DR: The results of milling experiment show that the proposed chatter identification method can recognize early milling chatter effectively and a support vector machine chatter identification model is obtained based on the multi-indicators.

TL;DR: Based on third-order Newton interpolation polynomial and direct integration scheme (DIS), a method to generate stability lobe diagram in milling process is proposed and proved to be an accurate and efficient method by the comparison results.

TL;DR: In this paper, the authors considered the process damping determined by the indentation volume between flank face of milling tool and machined surface, and used multi-mode model to describe this behavior.

TL;DR: This paper presents a brief introduction to competition-driven digital transformation in the machining sector using a basic digital twin structure and addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties.