Development and testing of a wireless smart toolholder with multi-sensor fusion

doi:10.1007/s11465-023-0774-y

Frontiers of Mechanical Engineering

2023, Vol. 18

Issue (4): 55 https://doi.org/10.1007/s11465-023-0774-y

本期目录

Development and testing of a wireless smart toolholder with multi-sensor fusion

Jin ZHANG^1,², Xinzhen KANG^1,², Zhengmao YE³, Lei LIU^1,², Guibao TAO^1,², Huajun CAO^1,²(

)

¹. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
². State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
³. Aerospace Research Institute of Materials & Processing Technology, Beijing 100076, China

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Abstract：

The smart toolholder is the core component in the development of intelligent and precise manufacturing. It enables in situ monitoring of cutting data and machining accuracy evolution and has become a focal point in academic research and industrial applications. However, current table and rotational dynamometers for milling force, vibration, and temperature testing suffer from cumbersome installation and provide only a single acquisition signal, which limits their use in laboratory settings. In this study, we propose a wireless smart toolholder with multi-sensor fusion for simultaneous sensing of milling force, vibration, and temperature signals. We select force, vibration, and temperature sensors suitable for smart toolholder fusion to adapt to the cutting environment. Thereafter, structural design, circular runout, dynamic balancing, static stiffness, and dynamic inherent frequency tests are conducted to assess its dynamic and static performance. Finally, the smart toolholder is tested for accuracy and repeatability in terms of force, vibration, and temperature. Experimental results demonstrate that the smart toolholder accurately captures machining data with a relative deviation of less than 1.5% compared with existing force gauges and provides high repeatability of milling temperature and vibration signals. Therefore, it is a smart solution for machining condition monitoring.

Key words： wireless smart toolholder multi-sensor fusion circular runout dynamic balancing static stiffness dynamic inherent frequency

收稿日期: 2023-07-18 出版日期: 2023-12-28

Corresponding Author(s): Huajun CAO

引用本文:

. [J]. Frontiers of Mechanical Engineering, 2023, 18(4): 55.
Jin ZHANG, Xinzhen KANG, Zhengmao YE, Lei LIU, Guibao TAO, Huajun CAO. Development and testing of a wireless smart toolholder with multi-sensor fusion. Front. Mech. Eng., 2023, 18(4): 55.

链接本文:

https://academic.hep.com.cn/fme/CN/10.1007/s11465-023-0774-y
https://academic.hep.com.cn/fme/CN/Y2023/V18/I4/55

Fig.1

Fig.2

Characteristic quantity	Expression form	Value
Force range	F_x, F_y	±2800 N
Force range	F_z	±6800 N
Torque range	T_x, T_y	±120 N?m
Torque range	T_z	±120 N?m
Stiffness index	K_x, K_y	2.5 × 10⁸ N/m
Stiffness index	K_z	3.7 × 10⁸ N/m
Torque stiffness index	K_tx, K_ty	1.1 × 10⁵ N?m/rad
Torque stiffness index	K_tz	2.0 × 10⁵ N?m/rad
Force measurement error	$E F x$ , $E F y$	≤ 2.25%, ≥ −2.25%
Force measurement error	$E F z$	≤ 2.75%, ≥ −2.75%
Torque measurement error	$E T x$ , $E T y$	≤ 1.25%, ≥ −1.25%
Torque measurement error	$E T z$	≤ 2.25%, ≥ −2.25%

Tab.1

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Tab.2

Fig.10

Fig.11

Fig.12

Tab.3

Fig.13

Fig.14

Fig.15

Fig.16

Test	$M x ¯$ /(N?m)	$M y ¯$ /(N?m)	$F z ¯$ /N
1	3.00	2.95	31.00
2	2.94	2.86	30.50
3	3.10	2.90	30.00

Tab.4

Test	$A x ¯$ /(m·s^?2)	$A y ¯$ /(m·s^?2)	$A z ¯$ /(m·s^?2)
1	1.220	1.399	1.208
2	1.156	1.367	1.224
3	1.172	1.315	1.200

Tab.5

Fig.17

Fig.18

A_x, A_y, A_z	Average values of milling vibration in the x-, y-, and z-direction, respectively
$E F x$ , $E F y$ , $E F z$	Force measurement errors in the x-, y-, and z-direction, respectively
$E T x$ , $E T y$ , $E T z$	Torque measurement errors in the x-, y-, and z-direction, respectively
f_s	Design frequency of the smart toolholder
F_hf	Free mode force
F_hwx, F_hwy	Working mode forces in the x- and y-direction, respectively
F_p	Perpendicular force
F_r	Radial force
F_v	Velocity force
F_x, F_y, F_z	Forces in the x-, y-, and z-direction, respectively
$F i max ¯$ , $F i min ¯$	Maximum and minimum milling force average values among the three measured datasets, respectively
$F k x ¯$ , $F k y ¯$ , $F k z ¯$	Kistler’s average values of 100000 consecutive force data points in the x-, y-, and z-direction, respectively
$F z ¯$	Smart toolholder’s average value of 100000 consecutive force data points in the z-direction
K_tx, K_ty, K_tz	Torque stiffness indexes in the x-, y-, and z-direction, respectively
K_x, K_y, K_z	Stiffness indexes in the x-, y-, and z-direction, respectively
L	Distance from the tip of the tool to the reference center of the force sensor
$M x ¯$ , $M y ¯$	Smart toolholder’s average values of 100000 consecutive bending moment data points in the x- and y-direction, respectively
n	Spindle speed
p	Number of sampling points of per cutting tooth
R	Tool radius
T_max, T_min	Milling temperatures during machining
T_x, T_y, T_z	Torques in the x-, y-, and z-direction, respectively
v_f	Feed speed
z	Number of cutting teeth
α_e	Radial cut width
α_p	Axial cutting depth
$ε i$	Measurement bending moment and force error
$δ$	Measurement vibration error
$η$	Temperature difference
$μ x$ , $μ y$ , $μ z$	Percentage errors in the x-, y- and z-direction, respectively
θ	Angle between F_r and the x-axis

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