Primarily, identification of chatter using stability lobe diagram (SLD) is very critical in machining. In machine tools, chatter and non-chatter vibrations separate from each other using SLD, which is a series of interconnected lobes in term of axial depth of cut and spindle speed that demarcates the regions of chatter vibrations. Regions below the lobes mean stable chatter vibrations [
3]. SLD can be developed in either time or frequency domains. It is much faster to develop SLD in the frequency domain than in time domain. Frequency response function (FRF) [
4‐
6], linear time-periodic function [
7], and zero order approximation of the characteristics equation [
8] are commonly employed to predict chatter vibrations in literature. In FRF, Fast Fourier transform (FFT) can determine the actual machining boundary where unstable cut show high amplitude with high vibration and stable cut show lower vibration with low amplitude [
9]. Owing to the above deliberations, two important points, which increased chatter vibration in machining [
4‐
6,
10‐
17] were identified as follows: an increase in depth of cut which move up vibration frequency and the effects of high spindle speed on shifting machine dynamic toward unstable cutting vibrations. Dos Santos and Coelho [
4] improved the accuracy of chatter prediction in machine tools using the stability lobe diagram. Gagnol et al. [
5] develop a spindle dynamic model for rotor equation using Timoshenko beam theory. Eynian [
6] used Nyquist contour to predict the stability through dominant poles of the closed loop delay-differential equation for machining systems. Li et al. [
10] established a comprehensive dynamic model of milling for stability analysis by considering regeneration, helix angle, and process damping. Solis et al. [
11] used nondestructive trials to determine a new transfer function of the milling process for stability analysis. Altintas and Budak [
12] and Altintas et al. [
13] developed analytical methods for the predictions of chatter in milling. Soliman and Ismail [
14] used PD fuzzy controller to limit the chatter indication. Quintana and Ciurana [
15] experimentally determined stability lobe by increasing the depth of cut in feed direction. Tsai et al. [
16] proposed acoustic signal feedback and spindle speed compensation for intelligent control application to avoid chatter vibration in milling process. Abele and Fiedler [
17] created stability lobe of the milling process by considering the dynamic behavior of the system. In the past, efforts have been made by a quite number of researchers to study the problems of machining chatter vibrations [
3]. Experimental and finite element (FE) model of machine tool were combined to investigate the vibrations [
18,
19]. Kersting et al. [
19] highlighted the effects of vibration on the workpiece, which focused on the feedback of workpiece displacement using FE. Dankena et al. [
20,
21] related workpiece vibration to the workpiece material and workpiece structure. They pointed out that compound workpiece was a major cause of unfavorable workpiece performance, such as material height deviations, transition deviation at material joint, and surface roughness deviation, as compared to a single workpiece. Ma et al. [
22‐
25] attributed vibration of the flexible workpiece to the workpiece fixture, and they improved the stiffness of the workpiece by using magnetorheological (MR) fluid. Ma et al. [
22] designed a flexible fixture to investigate the regenerative chatter based on the MR fluids. Ma et al. [
23] also proposed a dynamic analytical model by considering fixture constraints and the damping factor. Zeng et al. [
24] constructed the dynamic model of the workpiece-fixture-cutter system by using the cutting force and the fixture element as the disturbance input and control input, respectively [
24]. Similarly, retrofittable intelligent active fixtures, capable of observing the process in real-time and exert adequate counter-excitations was reported in Ref. [
25]. In spindle speed variation, workpiece vibrations are curtailed by selecting suitable spindle speed so that the excitation frequency corresponds to the workpiece resonance [
26]. Active control is a process that utilized the actuator force to provide adequate damping force that suppresses chatter vibration [
27]. It consumes considerable energy to derive the actuator.
Some techniques on active control were reported in Refs. [
31‐
35]. Control of vibrating actuator was considered using a PID controller [
31]. Active control was proposed based on delayed state feedback control and discrete optimal control [
32]. The effect of a fuzzy logic controller on active magnetic bearing was verified to regulate the spindle position in milling [
33]. Long et al. [
34] designed feedback control through a robust mixed sensitivity method by using two degrees of freedom (TDOF) workpiece holder. A proportional integral controller was employed for nonlinear stiffness function [
35]. Astrom and Hagglund described detail description of proportional integral and derivative (PID) controllers and its application in control system [
36]. Information describing the operational amplifier circuit and its application in filter, amplification, and control capabilities were explained [
37‐
39].
This is the major approach to resolve chatter vibration problem in the milling process via the operational amplifier circuit. Encouraged by the above consideration, a proportional-integral controller is proposed to suppress the chatter vibrations in the milling process. Milling test will be considered to investigate the effects of the proposed controller. This work contributes to minimizing chatter vibration and its negative consequences.