Energy saving design of the machining unit of hobbing machine tool with integrated optimization
Yan LV1, Congbo LI1(), Jixiang HE2, Wei LI1, Xinyu LI3, Juan LI1
1. State Key Laboratory of Mechanical Transmission, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China 2. Department of Process Technology, Sichuan Aerospace Fenghuo Servo Control Technology Corporation, Chengdu 611130, China 3. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase. The optimization design is a practical means of energy saving and can reduce energy consumption essentially. However, this issue has rarely been discussed in depth in previous research. A comprehensive function of energy consumption of the machining unit is built to address this problem. Surrogate models are established by using effective fitting methods. An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models, and the parameters of the motor and structure are considered simultaneously. Results show that the energy consumption and tool displacement of the machining unit are reduced, indicating that energy saving is achieved and the machining accuracy is guaranteed. The influence of optimization variables on the objectives is analyzed to inform the design.
. [J]. Frontiers of Mechanical Engineering, 2022, 17(3): 38.
Yan LV, Congbo LI, Jixiang HE, Wei LI, Xinyu LI, Juan LI. Energy saving design of the machining unit of hobbing machine tool with integrated optimization. Front. Mech. Eng., 2022, 17(3): 38.
Kinetic energy of mechanical transmission components
fBA
Basic frequency of the inverter
fc
Coulomb friction force
fi
ith order natural frequency of the machine tool after optimization
[fi]
ith order natural frequency before optimization
fv
Viscous friction force
fz
Axial feed of the hob
Fc
Cutting force
Fcz
Cutting force on the slide plate
Ff
Friction force between the sliding plate and the vertical guide rail
FN
Normal force on the guide rail
Ft
Slide plate load force
g
Gravitational acceleration
h
Heat dissipation coefficient
Jl, Js, Jsm
Moments of inertia of the coupling, ball screw, and servo motor, respectively
Js*
Moment of inertia equivalent to the motor shaft of the transmission system
K1, K2, K3
Coefficients associated with the materials, hardness, and helical angle of the gear, respectively
l1
Distance between the barycenters of the slide plate and the axis of the ball screw
l2
Distance between the barycenters of the tool post and the axis of the ball screw
l3
Distance between the barycenters of the main gearbox and the axis of the ball screw
l4
Distance between the barycenters of the additional component and the axis of the ball screw
l5
Distance between the barycenters of the hob and the axis of the ball screw
Lb
Ball screw lead
m
Normal modulus of the hob
M, Ma, Mc, Mh, Mk, Mt
Masses of the machining unit, additional components, main gearbox, slide plate, tool post shell, and tool post support plate, respectively
M0
Equivalent nonload Coulomb friction moment
Mit
Maximum iteration number
n*
Initial speed before the motor is accelerated
ngm
Hob speed
nN
Rated speed of the motor
nN1, nN2
Rated speeds of the main and servo motors, respectively
nsm
Servo motor speed
Ni
Motor speed under working condition i
Nip
Initial population number
Ns
Number of samples
p
Number of pole pairs of the motor
P
Power of the machining unit
Pad-i
Additional loss power under working condition i
Pc
Cutting power
PCu
Peak copper loss power
PCu-i
Copper loss power under working condition i
Pe
Peak eddy current loss power
Pe-i
Eddy current loss power under working condition i
Ph
Peak hysteresis loss power
Ph-i
Hysteresis loss power under working condition i
Ploss
Peak loss power of the motor
Ploss-i
Motor loss power under working condition i
Pm-i
Mechanical loss power under working condition i
Pmax
Peak power of the motor
Pmax1, Pmax2
Peak powers of the main and servo motors, respectively
Pmec
Mechanical loss power
Pmo
Main motor output power
Pn-i
Motor input power under working condition i
PN
Rated power
PN1
Rated power of the main motor
PN2
Rated power of the servo motor
Psm
Servo motor output power
Po-i
Motor output power under working condition i.
PSA
Motor acceleration power
PSR
Motor shaft rotation power
sgn(·)
Symbolic function
t
Operation time of the machine tool.
t*
Duration of the rotation acceleration
tA
Acceleration time of the inverter
tr
Limit value of temperature rise
Tb
Frictional moment generated by the bearing preload
Ti
Motor output torque under working condition i
Tl
Coupling output torque
Tsm
Servo motor output torque
Tt
Output torque of the ball screw
Tz
Input torque of the ball screw
Τ
Output threshold vector
vc
Cutting speed
Vz
Axial feed speed of the moving components along the Z axis
x
Sample data set
xi, xj
Sample points
x1, x2, x3, x4, x5
Structure parameters of the tool post support plate
Fitting expression of y
Z
Tooth number of the workpiece
α
Load factor of the mechanical transmission system
α*
Motor angular acceleration
γi
ith observed value
ith predicted value
Mean value of the ith observed value
δ
Tool displacement
?(·)
Global prediction polynomial
Θ(·)
Random error
κ(·)
Radial basis function
ηb
Bearing transmission efficiency
ηm, ηsm
Motor efficiencies of the main and servo motors, respectively
ηz
Ball screw transmission efficiency
η(i)
Motor efficiency under working condition i
μc
Coulomb friction coefficient
μv
Viscous friction coefficient
χ
Center of κ(·)
*
Angular velocity of the motor
,
Final and initial values of the inertia weight, respectively
Angular velocity of the spindle motor shaft
Servo motor output angular velocity
ξ0, ξi, ξij
Undetermined coefficients of the RSM model
?(·)
Linear polynomial function
?2
Variance of Θ(·)
Ξ(·)
Correlation function
?k
Related parameter determined by the maximum likelihood estimation
?i
Adaptability weight coefficient
?50×1
Synaptic weight matrix
||·||
Euclidean norm
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