Heavy machine tool work under such high-load conditions that chatter vibrations are prone to occur, which significantly diminishes the machining efficiency and quality. Stability lobe diagrams are commonly used to select appropriate spindle speed and axial depth of cut to get rid of chatter and maximize the material removal rate. However, this needs precise identification of the dynamics of the entire machine tool structure, especially in the low-frequency range. Operational modal analysis has been the proven technique for estimating dynamic characteristics of machine tool structures in operation conditions. In this article, a complete methodology was presented for employing operational modal analysis for heavy machine tool in machining conditions. A random cutting exciting method originally presented by Minis is modified which generates pseudorandom impulse force to excite a heavy vertical lathe structure. And the excitation signal of random cutting force was modeled to analyze the effect of cutting parameters on energy and frequency band of the excitation. One operational modal analysis method, the pLSCF (referred to as PolyMAX) method, was employed to estimate modal parameters during machining. It was also observed in chatter tests that the operational modal analysis results are more accurate than the traditional impact test results in characterizing the dynamics of machine tool structure in machining.

Machine tool structure has a strong influence on the dynamic properties of the tool. The change of a machine tool's structure will cause variations in the dynamic parameters of the entire tool, such as its natural frequency, which will result in changes to the stability of the tool and poor machining quality. Thus, a study on the variations of machine tool dynamics is essential for high performance cutting. In this paper, using the mass change method, a basic mathematical model for predicting the natural frequency change resulting from structural change was presented followed by an experimental validation of the model. The mathematical model indicates that structural change will lead to the outward variation of the natural frequency, which is essentially related to the change of the squared mode shape values between the original position and the modified position of the moving component. With this natural frequency change rate prediction model, the natural frequency in the case of structural change can be easily predicted. The predicted results indicate that the positional change of different moving components has differing influences on the natural frequency of the machine tool.

Dynamic properties of machine-tool structures are likely to change under machining conditions. Thus, the dynamic parameters obtained by traditional experimental modal analysis in the static state may not characterize accurately the dynamics of the machine tool structure in operation. This paper proposes a new method of so-called AEMA (Active Excitation Modal Analysis) to identify the dynamic modal parameters of a machine tool structure during machining. A random cutting excitation technique realized by cutting a specially designed workpiece is proposed to provide strong and evenly distributed excitation within the frequency range of interest. The surface of the workpiece has a long narrow random zigzag width, which randomizes the resulting cutting forces. The LSCE (Least Square Complex Exponential) method is employed to estimate the modal parameters from just the measured responses. Then an algorithm based on two novel tools, the harmonic frequency fence and the spectrum abruptness ratio, is presented to eliminate the harmonic modes attributed to AC power and rotation frequency. The abruptness ratio is used to detect the basic frequency, and then the fence filters out the harmonic modes caused by peaks at integer multiples of the basic frequency through narrow frequency fence slots followed by a damping ratio limit. Finally, the proposed AEMA method is experimentally validated and shows satisfactory results.

Output-only (or operational) modal analysis (OMA) has proven to be a desirable tool for a wide range of applications for identifying dynamic modal parameters while structures are in service. It is especially useful for machine tools because evident changes will occur in dynamic properties between the working state and the static condition. However, the absence of input measurements obstructs OMA from assembling the frequency response function (FRF), which is necessary for many applications. Recently, a random cutting technique was proposed to excite the machine tools in order to extract the dynamic parameters through OMA. The present study is an extension of that work to estimate the FRF of machine tools. In this paper, the random cutting process is modeled to predict the cutting forces according to available information, such as the cutting parameters, the tool geometry, and the workpiece material, which are all known before machining. A function is introduced into the model to evaluate the influence of the variation of spindle speed on the cutting forces. Then, the calculated cutting forces are employed to replace the missing input measurement to estimate the FRF. The proposed method was experimentally validated and showed satisfactory agreement between the measured and estimated results in magnitude, although the phase is not that desirable. Finally, the approximation error of the method was analyzed and some error reduction strategies were presented.

Measuring the dynamics of a machine tool is important for improving its processing or design. In general, the dynamics of the machine tool structure is identified by the experimental modal analysis approaches that require the measurement of both the input loadings and the corresponding structural responses. However, the primary limitation for this method is that the input loadings are difficult or impossible to be measured when the machine tool is under operational conditions. In this paper, a method that is based on random decrement technology was used to identify the operational modal parameters of a machine tool without the knowledge of any of the inputs. To estimate the frequency response functions, FRFs, a structural change method was proposed. The approach is based on the sensitivity of the eigenproperties to structural modifications caused by the drive positions. The proposed method was verified experimentally by traditional hammer tests. Because no elaborate excitation equipment is used, the dynamics of the machine tool structure with arbitrarily feed rate or working position can be easily identified using the proposed active excitation modal analysis method.

For application in large machine tools, the machined part quality, accuracy as well as machining speed depende greatly on the dynamics of the structure. In this paper, an active-excitation modal analysis (AEMA), using inertial force of the moving slider to excite the structural modes, is proposed. Modal parameters of the machine tool structure estimated by AEMA are experimentally validated. Since the artificial excitation produced by elaborate excitation equipment is replaced by the inertial force of the slider, the proposed method is much more practical and economical than traditional methods.

The dynamics of the machine tool structure are important in high precision machining. Some researchers have studied that the dynamics are expected to change under different machining conditions. However, the dynamic behaviors of the machine tool at different worktable feed speeds are rarely studied. In this paper, an output-only modal identification available to predict the dynamics of the machine tool at different feed speeds is proposed. The excitation of this method uses the inertia force sequence caused by random idle running of the worktable. The first six modes of the entire machine tool structure are estimated using the proposed method. The results indicate that the running state of the worktable can influence the modes in which the worktable vibrates. The estimated natural frequencies and damping ratios decrease obviously as the feed speed increases. Furthermore, because this method enable to determine modal parameters by measuring the response of machine tool structure without using any artificial excitation, it can be used to predict the dynamic behaviors of the machine tool in entire working space effectively.

Dynamic properties of the whole machine tool structure including tool, spindle, and machine tool frame contribute greatly to the reliability of the machine tool in service and machining quality. However, they will change during operation compared with the results from static frequency response function measurements of classic experimental modal analysis. Therefore, an accurate estimation of the dynamic modal parameters of the whole structure is of great value in real time monitoring, active maintenance, and precise prediction of a stability lobes diagram.Operational modal analysis (OMA) developed from civil engineering works quite efficiently in modal parameters estimation of structure in operation under an intrinsic assumption of white noise excitation. This paper proposes a new methodology for applying this technique in the case of computer numerically controlled (CNC) machine tools during machining operations. A novel random excitation technique based on cutting is presented to meet the white noise excitation requirement. This technique is realized by interrupted cutting of a narrow workpiece step while spindle rotating randomly. The spindle rotation speed is automatically controlled by G-code part program, which contains a series of random speed values produced by MAMAS software following uniform distribution. The resulting cutting produces random pulses and excites the structure in all three directions. The effect of cutting parameters on the excitation frequency and energy was analyzed and simulated. The proposed technique was experimentally validated with two different OMA methods: the Stochastic Subspace Identification (SSI) method and the poly-reference least square complex frequency domain (pL'SCF or PolyMAX) method, both of which came up with similar results. It was shown that the proposed excitation technique combined successfully with OMA methods to extract dynamic modal parameters of the machine tool structure. (C) 2013 Elsevier Ltd. All rights reserved.

Dynamic properties of machine tool structure under machining operations are different from static results of impact or shaker tests. This paper proposes a complete methodology for applying operational modal analysis (OMA) in the case of heavy machine tool to estimate the dynamic modal parameters of the whole structure during machining. A random cutting technique is presented to meet the white noise excitation requirement of OMA. This technique is realized by interrupted cutting of a specially designed workpiece, of which the surface is modulated with pseudorandomly distributed teeth and channels. The resulting cutting force excites the structure in all three directions. Finally, Machine tool dynamic modal parameters are estimated and verified.

This study presents a novel measurement strategy and an error-compensation model for the measurement of large-scale free-form surfaces in on-machine laser measurement systems. To improve the measurement accuracy, the effects of the scan depth, surface roughness, incident angle and azimuth angle on the measurement results were investigated experimentally, and a practical measurement strategy considering the position and orientation of the sensor is presented. Also, a semi-quantitative model based on geometrical optics is proposed to compensate for the measurement error associated with the incident angle. The normal vector of the measurement point is determined using a cross-curve method from the acquired surface data. Then, the azimuth angle and incident angle are calculated to inform the measurement strategy and error-compensation model, respectively. The measurement strategy and error-compensation model are verified through the measurement of a large propeller blade on a heavy machine tool in a factory environment. The results demonstrate that the strategy and the model are effective in increasing the measurement accuracy.