Optimization of Turn-milling Processes

Kara, Mehmet Emre; Budak, Erhan

doi:10.1016/j.procir.2015.06.057

Cited by 19 publications

(4 citation statements)

References 15 publications

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“…Cutting temperature, therefore, reduces which in turn decreases tool wear [1]. Due to these special aspects and several advantages offer by turnmilling, it is widely used in industries with large-sized workpieces [2].…”

Section: Introductionmentioning

confidence: 99%

Vibration Analysis for Turning-Milling Process Condition Monitoring using Short-Time Fourier Transform Enhanced by Empirical Mode Decomposition

Susanto

2023

JMESI

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Turn-milling process is widely applied in industries that provides advantages for machining large-diameter mechanical parts with high speed, reducing cutting temperature, which in turn decreases tool wear. However, it needs to monitor the turn-milling process for preventing the onset of chatter vibration during operation and the chatter induces negative effects. Vibration analysis is one of the ways for monitoring it. However, acquired vibrations should be denoised from noises, wherein the conventional signal filter may have defiance to do that. This paper presents the utilization of the empirical mode decomposition (EMD) method as an efficient and adaptive noise filter. The Short-Time Fourier Transform (STFT) improvement using EMD is then used for monitoring turn-milling process conditions in the energy-time-frequency domain. The results showed that the reconstructed signal was quite impressive compared to the raw signal and the oscillation of the filtered signal was clearer than the raw signal. The improvement of the filtered signals was proved by the kurtosis index and spectral kurtosis. The improved STFT using EMD showed a significant spectrum with high resolution compared to conventional STFT. The energy density could be observed clearly in the machining characteristic frequencies with an improvement of about 10-100 times larger. The proposed method is therefore effectively applied to monitor the turn-milling condition.

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Section: Introductionmentioning

confidence: 99%

Vibration Analysis for Turning-Milling Process Condition Monitoring using Short-Time Fourier Transform Enhanced by Empirical Mode Decomposition

Susanto

2023

JMESI

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“…The method of turn-milling, a promising cutting process combining the two conventional machining operations turning and milling, is already successfully used in order to create microstructures on tribologically stressed surfaces [2]. It not only offers a more sustainable and environmentally friendly way to mill, it is also a technique with lower cutting temperatures which helps to increase the tool's life [3]. Due to nonlinear effects, like vibration excitations caused by interrupted cutting and their repercussions on the system, this manufacturing method needs to be further investigated to ensure a stable process and a reproducible surface quality.…”

Section: Introductionmentioning

confidence: 99%

Modelling the dynamic contact forces during orthogonal turn‐milling

Knape

Nitzschke

Woschke

2021

Proc Appl Math & Mech

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The aim of this work is to numerically calculate the effective cutting forces during orthogonal turn-milling using the wellknown analytical approach introduced by Victor/Kienzle in connection with a multi-body system. Starting with a routine that numerically computes the mill's infeed with the implemented dexel model, it is possible to reconstruct and follow the tool's outline and track caused by its rotation. Hence, the acting cutting forces can be determined.

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“…2 Machining parameter optimisation is a typical multi-objective optimisation problem (MOP) when two or more objectives are considered synchronously. Material removal rate, machining cost, tool wear (Padhi et al, 2016), surface quality (Bhavsar et al, 2015), cutting forces (Kara and Budak, 2015), cutting fluid consumption (Jiang et al, 2015), carbon emissions (Lin et al, 2016) and energy consumption (Albertelli et al, 2016), etc., should be considered concurrently. It is impossible to optimise all objectives most at the same time (Padhi et al, 2016), because some conflicts may exist among these objectives (Tiwari et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective machining parameter optimisation for residual stress based on quantum cat swarm

Zhang¹,

Sun²

2017

IJSCOM

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Residual stresses greatly affect parts' performances, lives, fatigue strengths, corrosion resistance, etc. Due to the lack of analytical models, machining parameter optimisation for better residual stresses is still a problem. In this paper, a multi-objective machining parameter optimisation method is proposed. Based on the support vector machine, the machining parameters' nonlinear relationships with the surface roughness and the residual stress are investigated. The cutting time consumption, surface roughness and absolute residual stress are the objectives, while the cutting speed, feed rate, axial cutting depth and the radial cutting deep are variables. The cutting power and cutting torque are constraints. The multi-objective cat swarm optimisation is designed to solve the problem, while the quantum computation is used to improve its performance. An experimental study is presented to verify the method. Some Pareto solutions are obtained with good convergence and diversity. Compared with the empirical machining parameters, the material removal rate, surface roughness and residual stress are optimised greatly. Compared with non-dominated sorting genetic algorithm II, the algorithm's precision and effectiveness are also verified.

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Optimization of Turn-milling Processes

Cited by 19 publications

References 15 publications

Vibration Analysis for Turning-Milling Process Condition Monitoring using Short-Time Fourier Transform Enhanced by Empirical Mode Decomposition

Vibration Analysis for Turning-Milling Process Condition Monitoring using Short-Time Fourier Transform Enhanced by Empirical Mode Decomposition

Modelling the dynamic contact forces during orthogonal turn‐milling

Multi-objective machining parameter optimisation for residual stress based on quantum cat swarm

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