An ensemble approach to maximize metal removal rate for chatter free milling

Mishra, Rohit; Singh, Bhagat

doi:10.1016/j.jocs.2022.101567

Cited by 12 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of adaptive variational mode decomposition for chatter detection has been lately reported in [142] and [198]. Mishra and Singh [87,[340][341][342][343]] investigated a spline-based local mean decomposition technique, while Zhang et al [137] used a morphological empirical wavelet transform (EWT). Ren and Ding employed an adaptive Hankel low-rank decomposition to adaptively separate the chatter-related components from the observations [101].…”

Section: Time-frequency Domain Analysismentioning

confidence: 99%

“…Criterion selection usually emerges from multiple experiments under several cutting parameters in both stable and unstable conditions. Hence, many authors aimed to eliminate cutting condition influences by defining a normalized or a dimensionless threshold, while some recent studies in milling dynamics [341,[349][350][351] have suggested a different approach, the use of a quantitative value to represent chatter stability, instead of a qualitative designation (e.g. stable, transition, chatter).…”

Section: Feature Generationmentioning

confidence: 99%

See 1 more Smart Citation

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

show abstract

Section: Time-frequency Domain Analysismentioning

confidence: 99%

Section: Feature Generationmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Yan and Li [26] presented a multi-objective optimization approach based on weighted grey relational analysis and response surface method (RSM), which is applied to optimize the cutting parameters in milling. Mishra and Singh [27] adopted multi-objective particle swarm optimization (MOPSO) to optimize these regression models for obtaining the best possible range of input parameters for minimal chatter and maximum Metal Removal Rate; Beudaert et al [28] fully considering the dynamic performance of the machine tool, a velocity profile optimization (VPOP) algorithm is proposed to optimize the feed rate curve, which improves the efficiency reduction caused by the traditional control of the motion parameters (speed, acceleration) of each axis; Nguyen et al [29] proposed a new polynomial control algorithm, which can select the third-order, fourth-order, and fifth-order orders for the polynomial according to different processing conditions; Erkorkmaz and Altintas [30] proposed a parallel speed based on linear programming after segmenting the path. The programming method transforms the convex programming problem into a linear programming problem and improves computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

An optimization method of acceleration and deceleration time of feed system based on load inertia

Zhou,

Yang,

Guo

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The acceleration and deceleration time is usually a constant value in the process of computer numerical control (CNC) machine tool processing, which cannot adapt to the change of external load and greatly affects processing efficiency. This paper proposes an optimization method for the acceleration and deceleration time of the feed system based on load inertia, which provides the basis for the adaptive adjustment of the acceleration and deceleration time of the feed system. Firstly, by establishing the dynamic model of the servo system, the acceleration and deceleration method is used to identify the external load inertia under different working conditions. The prediction model of the current variance based on load inertia and acceleration and deceleration time parameters is established by using the response surface method, and then the multi-objective particle swarm optimization algorithm is used to build the acceleration and deceleration time optimization model based on the load inertia. At the same time, the inertia identification part is compared with the model reference adaptive system method and the empirical formula estimation method based on current and velocity, the simulation and cutting experiment results show that the inertia identification method based on acceleration and deceleration optimizes the other method. Finally, the machining experiments are carried out on three-axis and five-axis machine tools with the same machine tool type, and by adding different counterweight blocks to change the external load. The test proves that the acceleration and deceleration adaptive adjustment strategy based on load inertia can effectively improve processing efficiency and reduce the fluctuation of the processing load.

show abstract

“…The results revealed that surface roughness is primarily affected by the spindle speed and cutting depth. Mishra et al [15][16][17] proposed several new integrated methods to determine stable regions during milling. One is a milling stability analysis method based on spline local mean decomposition (SB-LMD) and ANN.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the micro-texture and cutting parameters of ball-end milling cutters to achieve milling stability

Tong

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

The machining of titanium alloy can lead to serious tool wear, with this resulting in a lack of milling system stability and chatter. This paper looks at the relationship between milling stability and the use of micro-texturing on cutter surfaces, which aims to reduce tool wear. A stability lobe diagram is first drawn that is based on a dynamic model of the milling of titanium alloy by a ball-end milling cutter. This provides the cutting parameters for a stable domain. A micro-texture distribution model is then established for different cutting depths and the influence of micro-texture parameters on the milling performance of cutting tools according to various cutting parameters studied. The parameters are then optimized using an artificial bee colony algorithm, with the milling force, machined surface quality, and tool wear being adopted as the principal evaluation indicators. This provides a theoretical basis for the future selection of optimal cutting and micro-texture parameters.

show abstract

An ensemble approach to maximize metal removal rate for chatter free milling

Cited by 12 publications

References 18 publications

Chatter detection in milling processes—a review on signal processing and condition classification

Chatter detection in milling processes—a review on signal processing and condition classification

An optimization method of acceleration and deceleration time of feed system based on load inertia

Optimizing the micro-texture and cutting parameters of ball-end milling cutters to achieve milling stability

Contact Info

Product

Resources

About