Parametric chip thickness model based cutting forces estimation considering cutter runout of five-axis general end milling

Zhu, Zhi‐Biao; Yan, Rong; Peng, Fangyu; Duan, Xianyin; Zhou, Lin; Song, Kang; Guo, Chaoyong

doi:10.1016/j.ijmachtools.2015.11.001

Cited by 65 publications

(16 citation statements)

References 30 publications

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“…Figure 6d illustrates that Bi-containing alloy produced small segmented chips due to having low melting point with lamellar silicon shape along the shear zones [2,27]. Chip formation depends on material characteristic and cutting tool geometry and it is better to have fragmented [28]. As can be seen in Fig.…”

Section: Optical Microscopymentioning

confidence: 95%

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

et al. 2021

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This study investigated the predicted cutting force model of a turning operation for Al–Si–Cu cast alloy modified with modifiers based on adaptive neuro-fuzzy inference system (ANFIS) approach. Feed rate, cutting speed and Silicon spacing were considered as the input parameters. A series of turning experiments were conducted at various feed rates and cutting speeds. The prediction result showed that the ANFIS model successfully predicted the cutting force value in terms of cutting speed, feed rate and Si spacing. A mathematical model was proposed to describe the cutting force changes during the machining of Al–Si–Cu cast alloy. Moreover, the addition of Bismuth into the base alloy decreased the cutting force compared to other refinement elements.

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Section: Optical Microscopymentioning

confidence: 95%

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

et al. 2021

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“…Cutting force determination on five-axis machining processes can be difficult, due to the changes in cutter orientation during the machining of complex parts. In the paper presented by Zhu et al [37], the authors propose a cutting force prediction model by applying a new method, the cutting-edge element moving (CEEM) method, to calculate instantaneous undeformed chip thickness (IUCT) to effectively simulate five-axis machining cutting forces. Still regarding cutting force prediction in five-axis milling, in the study carried out by Olvera et al [38], the authors propose a method of predicting forces in the milling of Al 7075-T6 alloy, using barrel-shaped cutters.…”

Section: Cutting Force Prediction Methodsmentioning

confidence: 99%

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

Sousa

Silva

Fecheira

et al. 2020

Sensors

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Machining processes remain an unavoidable technique in the production of high-precision parts. Tool behavior is of the utmost importance in machining productivity and costs. Tool performance can be assessed by the roughness left on the machined surfaces, as well as of the forces developed during the process. There are various techniques to determine these cutting forces, such as cutting force prediction or measurement, using dynamometers and other sensor systems. This technique has often been used by numerous researchers in this area. This paper aims to give a review of the different techniques and devices for measuring the forces developed for machining processes, allowing a quick perception of the advantages and limitations of each technique, through the literature research carried out, using recently published works.

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“…Figure 8 shows a section of tool displacement signal. The dynamic parameters and displacement signals of the tool are substituted into Equations (8) and (10). After the influence of the tool system is superimposed, the measured cutting force is compared with the predicted cutting force calculated by proposed method.…”

Section: Comparison and Verificationmentioning

confidence: 99%

“…Taner et al [ 9 ] proposed a widely used cutting force model which can predict cutting force by identifying cutting force coefficients. Zhu et al [ 10 , 11 ] proposed a method to analyze tool positioning changes and tool runout by calculating the undeformed cutting thickness and improved the model in subsequent research. Sun et al [ 12 ] proposed a dynamic milling parameter prediction model considering the influence of deformation.…”

Section: Introductionmentioning

confidence: 99%

Cutting Force Transition Model Considering the Influence of Tool System by Using Standard Test Table

Chen

Zhang

Wang

2021

Sensors

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The cutting force prediction model usually uses the classical oblique transformation method, which introduces the orthogonal cutting parameters into the oblique milling edge shape, and combines the geometric parameters of the tool to convert the orthogonal cutting force into the actual cutting force, thereby predicting the cutting force. However, this cutting force prediction method ignores the impact of tool vibration in actual machining, resulting in a large difference between the prediction model and the actual measurement. This paper proposes a cutting force conversion model considering the influence of the tool system. The proposed model fully considers the impact of tool vibration on the cutting force. On the basis of the orthogonal model, superimposing the additional cutting force generated by tool vibration makes the predicted value of the model closer to the actual cutting force. The results of milling experiments show that the conversion model can obtain higher prediction accuracy. Moreover, compared with the original conversion model, the accuracy of the proposed model is significantly improved.

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Parametric chip thickness model based cutting forces estimation considering cutter runout of five-axis general end milling

Cited by 65 publications

References 30 publications

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

Cutting Forces Assessment in CNC Machining Processes: A Critical Review

Cutting Force Transition Model Considering the Influence of Tool System by Using Standard Test Table

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