Acoustic Emission

To achieve desired accuracy, precision and surface roughness during laser-material removal process, monitoring and control of the process parameters related to laser, optics, workpiece material and its motion are required. Focus position, defined as a gap between the focusing lens and the surface of the sample workpiece, is one of the most critical process parameters, which determines the projection of the intensity of the laser beam on the surface to be ablated and therefore directly affects volume and geometry of the material removed and there by machining quality. In this paper, acoustic emission (AE) generated by laser-material interactions was statistically analyzed with respect to the variations in the focus position. The study involved on-line measurements of the AE signal from the laser-material interaction zone as a function of the focus position and the width of the machined trenches. Several basic statistical parameters, e.g. average amplitude, variance and power spectrum density were analyzed to select distinct informational parameters. Pattern recognition analysis of three informational parameters based on variances within frequency diapasons of 20-180, 180-300, and 300-500 kHz was used for reliable classification of the focus position and width of the machined trenches. The results provide important information for future development of on-line monitoring and control systems for laser-material removal process. Crown

Detecting and locating damage in structural components and joints that have high feature densities and complex geometry is a difficult problem in the field of structural health monitoring (SHM). Active propagation of diagnostic waves is one approach that is used to detect damage. But small cracks and damage are difficult to detect because they have a small effect on the propagating waves as compared to the effects the complex geometry itself which causes dispersion and reflection of waves. Another limitation of active wave propagation is that pre-damage data is required for every sensor-actuator combination, and a large number of sensors might be needed to detect small cracks on large structures. Overall, the problem of detecting damage in complex geometries is not well investigated in the field of SHM. Nevertheless, the problem is important because damage often initiates at joints and locations where section properties change.

Machine condition monitoring plays an important role in industry to ensure the continuity of the process. This work presents a simple and yet, fast approach to detect simultaneous machinery faults using sound mixture emitted by machines. We developed a microphone array as the sensor. By exploiting the independency of each individual signal, we estimated the mixture of the signals and compared time-domain independent component analysis (TDICA), frequency-domain independent component analysis (FDICA) and Multi-stage ICA. In this research, four fault conditions commonly occurred in industry were evaluated, namely normal (as baseline), unbalance, misalignment and bearing fault. The results showed that the best separation process by SNR criterion was time-domain ICA. At the final stage, the separated signal was analyzed using Instantaneous Frequency technique to determine the exact location of the frequency at the specific time better than spectrogram.

... Spray Conf. (Germany), Proc. CD Section: HVOF-Processes and Materials I, (Ohio: ASM Internaltional) pp 1–4. [6] Faisal NH, Steel JA, Ahmed R and Reuben RL 2009 J. Therm. Spray Technol. (Preprint DOI: 10.1007/s11666-009-9334-1). [7] Coats DL, Krawitz AD 2003 Mater. ...

An acoustic emission tester for aircraft Halon bottles has been developed. The necessary load is applied by heating the bottles. Acoustic emission is monitored during the heating by six sensors held in position by a special fixture. This fixture was designed to fit spheres with diameters between 5 and 16 inches. A prototype has been undergoing testing in two commercial

The use of Diesel Particulate Filters (DPFs) on automobiles to reduce the harmful effects of diesel exhaust gases is becoming a standard in many countries. Although the main purpose of a DPF is to reduce harmful emission of soot particles it also affects the acoustic emission. This paper presents a first attempt to describe the acoustic behavior of DPFs and to present models which allow the acoustic two-port to be calculated. The simplest model neglects wave propagation and treats the filter as an equivalent acoustic resistance modeled via a lumped impedance element. This simple model gives a constant frequencyindependent transmission loss and agrees within 1 dB with measured data on a typical filter (length 250 mm) up to . In the second model, the ceramic filter monolith is described as a system of coupled porous channels carrying plane waves. The coupling between the channels through the porous walls is described via Darcy's law. This model gives a frequency-dependent transmission loss and agrees well with measured data in the entire plane wave range. r

Purpose: In the present work was made the comparative analysis in time domain and frequency domain to the acoustical pressure generate by the electric arc to determinate which of the two analysis methods is better to evaluates the stability in GMAW process. Design/methodology/approach: Welds had been made with the parameters adjusted to get the highest stability. In these conditions, were

The paper presents the latest results of sensing the cutting process on the basis of AE signals and some particularities in further development of the monitoring model for the finish turning process. Due to non-linearity, the large number of influencing parameters and missing information in AE data, the Artificial Neural Networks were chosen as a monitoring decision tool. The problem of accurateness in predicting the surface roughness on the basis of AE -because of the mutual interdependence of the data -requires a special procedure for building a neural network model. The final aim of such an approach is presented as improvements in learning or considerable reduction in error prediction. Further development of the monitoring model has the goal of building a so-called intelligent sensor, which should be able to perform the signal conditioning and feature extraction process.

In order to design structural components using composite materials a deep understanding of the material behaviour and its failure mechanisms is necessary. To create a better understanding of the initiation, growth and interaction of the different types of damage, damage monitoring during mechanical loading is very important. To this direction, AE is a powerful non destructive technique for real time monitoring of damage development in materials and structures which has been used successfully for the identification of damage mechanisms in composite structures under quasi static and dynamic-cycle loading. In this present work, pure resin plate and GFRP composite laminates with stacking sequence of [0 0 ] 6 , are fabricated using Hand lay-up method. During the layup a Teflon tape of width 45 mm is kept in the mid plane of the laminate which serves as an initiator for delamination during loading. As per ASTM STD D5528 01 DCB (Double Cantilever Beam) specimens are cut out from the laminates and are subjected to tensile test in the transverse direction along with acoustic emission monitoring. While loading, Markings are made on the sides of the specimen to track the crack front using a magnifying lens. Parametric analysis is performed on the AE data obtained during crack propagation to discriminate the failure modes. Fast Fourier Transform (FFT) enabled the calculation of frequency content of each damage mechanism. Further STFFT analysis is performed on a portion of the waveforms representing the dominant frequency content pertaining to each damage mechanism.

Transformers are a major capital item and the cost of a failure is high both in direct costs and downtime. For this reason they are monitored using a variety of methods. Partial discharges in power transformers are often a predecessor of a serious fault for this reason. Partial discharge measurements are an important diagnostic tool to monitor the insulation condition of a transformer. For on-line measurements, a detection method based on the acoustic signals emitted by the discharging source is used and can be detected. Moreover, the acoustic technique offers the possibility to locate and identify the PD source. This paper covers general aspects of acoustic methods for PD detection and localization in time, frequency and time-frequency domain. I.

Tool condition monitoring (TCM) systems employed in industry are mostly used to detect tool fracture, although the prevention of it should be the principal aim. This would not only allow for the avoidance of any fracture-related damage to both the workpiece and machine tool, but also for recondition the tool for further use.

In this study an optical fibre sensor system was conceived for simultaneous detection of the strain and the acoustic emission due to damage. It consists of a fibre Bragg grating and a Fabry-P erot interferometer. For the interrogation of the Fabry-P erot cavity, an innovative low cost process based on the generation of two quadrature phase-shifted signals using two fibre Bragg gratings was implemented, allowing the recovery of the change in the cavity length. An automated processing system, based on Kohonen's self-organizing maps is also presented, for future classification of the acoustic emission waves detected by the optic fibre sensor.

Numerous condition monitoring techniques and identification algorithms for detection and diagnosis of rotating machinery faults have been proposed for the past few years. Motors are one of the common used elements in almost all rotating machinery. They cause the machine failure upon getting faulty. Therefore advance and effective condition monitoring techniques are required to monitor and detect the motor problems at incipient stages. This avoids catastrophic machine failure and costly unplanned shutdown. In this paper the acoustic emission (AE) monitoring system is established. It discusses a method based on time and frequency domain analysis of AE signals acquired from motors used in chemical process pilot plant. A real time measurement system is developed. It utilizes MatLAB to process and analyze the data to provide valuable information regarding the process being monitored.

The acoustic emission of fracture precursors, and the failure time of samples of heterogeneous materials (wood, fiberglass) are studied as a function of the load features and geometry. It is shown that in these materials the failure time is predicted with a good accuracy by a model of microcrack nucleation proposed by Pomeau. We find that the time interval δt between events (precursors) and the energy ε are power law distributed and that the exponents of these power laws depend on the load history and on the material. In contrast, the cumulated acoustic energy E presents a critical divergency near the breaking time τ which is E ∼ , τ -t τ -γ . The positive exponent γ is independent, within error bars, on all the experimental parameters.

¶Laboratory experiments on rock samples were carried out to understand the relationship between the acoustic emission (AE) parameters and rock fabric. AE activity was recorded during the uniaxial compression with constant stress rate of several granitic rocks showing variable microfabric (e.g. grain size, shape and crystallographic preferred orientation) and macrofabric (e.g. magmatic isorientation, metamorphic banding). A broad band multichannel recording system with a high dynamic recording range enabled individual acoustic emission events to be analysed in a wide frequency and energy range. The microfabric of the rocks was studied by quantitative petrographic analysis of thin sections allowing precise description of mode, grain size and shape. It has been proved that the energy-frequency distribution of AE events strongly reflects the fabric of the samples. The characteristic energy value of AE events reflects the most frequently occurring grain dimensions. The AE parameters like acoustic rate or cumulative energy are highly sensitive to fabric arrangement in rocks with pronounced fabric (e.g. foliation, lineation etc.).

Acoustic emission (AE) is considered one of the main methods of on-line detection of catastrophic tool failure (CTF). Some strategies have claimed a subsequent increase of the root mean square value of the AE signal (AE RMS ) which in turn has been used as a measure of the CTF. However this measure was found to be not always sensitive to CTF. The aim of this paper is to present a method of catastrophic tool failure detection which uses symptoms other than the direct AE RMS signal. The method is based on the statistical analysis of the distributions of the AE RMS signal. The i distribution which was assumed in this study has been used with a density function of two parameters. The skews and kurtosis of the i distribution were the main measures employed. They were found to be highly sensitive to changes in tool condition and have given promising results with regard to chipping and tool breakage detection.

The tension±tension fatigue behaviour of woven fabric composites was investigated in the on-axis and off-axis directions. Similar to the static tensile properties of woven fabric composites, the tension±tension fatigue behaviour of woven fabric composites was also observed to be anisotropic. The fatigue damage development in both the directions was studied using scanning electron microscopy and acoustic emission techniques. The in¯uence of the applied fatigue frequency and the fatigue stress ratio in both the directions was investigated. At a fatigue frequency of 5 Hz, the hysteresis heating will occur more severely in the off-axis direction than in the on-axis direction. Increasing fatigue stress ratio will lead to a lower hysteresis heating. q

A review of the literature on helicopter rotor system health monitoring is conducted in this paper. An introduction is provided to the work on rotor track and balance and commercial health and usage monitoring systems. Research on the modelling of typical rotor system faults using aeroelastic analysis is discussed and the use of damage detection algorithms based on neural network, fuzzy logic, and system identification is pointed out. The use of non-destructive testing (NDT) approaches, such as modal methods, acoustic emission, and wave-based approaches for rotor health monitoring is discussed. Finally, work on the health monitoring of composite helicopter rotors is discussed and inverse problem solution and life prediction issues are addressed. Future research needs in the area are pointed out.

Newborn hearing screening has been recognized as an essential component of public health care in early childhood in developed countries. However, such screening is yet to be widely embraced in the developing world. The new national health policy in Nigeria seeks, for the first time, to reduce the impact of permanent hearing loss on early childhood development through early detection and timely intervention services. The aim of this paper is to ascertain if newborn hearing screening satisfies the conventional criteria for a screening programme as an early detection strategy in this developing country. A review of the available literature shows that permanent childhood hearing loss is a significant health condition and its detection through screening with oto-acoustic emissions and/or auditory brainstem response is feasible in the target population. Amplification with hearing aids is an effective and preferred option for early intervention by parents. The risk of maternal anxiety from potential falsepositives or the psychological cost of false assurance from false-negatives is unlikely to outweigh the benefit of screening. Newborn hearing screening is therefore a potential early detection strategy for permanent childhood hearing loss in Nigeria.

Different concentrations of polyamide fibres with given aspect ratios were associated to a commercial plaster in a view to investigate the mechanical behaviour. The presence of these water-absorbent fibres perturbs the hydration of the plaster and causes changes in the plaster microstructure. All the mechanical parameters decrease monotonically as the concentration of fibres is increased, except the fracture toughness which shows a different trend. The ability of the composites to retain strength at the onset of matrix cracking has been forecast and analysed on a phenomenological basis in terms of a fibre efficiency factor which is a useful supplement to the usual mechanical characterisation of such materials. #

A novel rolling contact fatigue (RCF)/wear competitive life test machine based on double-roll mechanism was designed to investigate the RCF/wear failure mode and predict the competitive life regularity of surface coatings. The test machine was designed for variable slip ratio to simulate the complex service conditions. The failure can be monitored using signals which are acquired from acoustic emission (AE) of the contacting rollers. In the experimental studies, the typical morphologies of RCF damage such as pits, surface abrasion, and delamination were observed. The effectiveness of the AE signals used as a RCF failure monitoring tool was analyzed. The P-N and P-S-N plots were established to predict the life regularity of surface coatings.

Abstract: The paper reviews the background and methodology of the primary calibration of Acoustic Emission (AE) sensors. Two practically realised basic calibration methods are described and explained. The method of reciprocity calibration is based on the reciprocity theorem. The ...

The research dealt with the characterization of damage in quasi-isotropic carbon fibre reinforced epoxy resin laminate loaded at the centre. Load was applied by means of a servo hydraulic machine and it was supposed to simulate a low velocity impact. The acoustic emission (AE) technique was used to detect damage progression. Tensile resistance after indentation was investigated and correlated with acoustic emissions parameters. This was been done for different lamination sequences on specimens cut from the damaged plates.

The Internet of Things (IoT) concept facilitates our life in many areas. One of the facilities provided in this area is undoubtedly condition monitoring. Unlike regular maintenance, IoT systems that perform continuous control operations can provide great advantages to the company with a warning that a serious failure will occur. It is a vital importance to determine defective bearings during the rotation of the power generating and power consuming machines without reaching the critical level. In this study; an experimental setup was created, and a Condition Monitoring Application (CMA) was performed in the laboratory environment which could monitor vibration, temperature, acoustic emission (AE), current and revolution measurements for ORS 6203 type defective bearing detection. In the designed test setup, a microcontroller platform used as a system brain, a direct current machine with a shaft mounted bearing, and miscellaneous sensors used such as 3-axis accelerometer that senses vibrations, a hall sensor module for revolution measure, a microphone module for AE detection, current and voltage sensor modules for power consumption measurement, contactless temperature sensor module for temperature measurement. CMA has been designed for monitoring of system via Bluetooth module. With the help of the microcontroller platform, the data collected from the sensors is transmitted wirelessly to the Android smartphone to be plotted and recorded. After the data exceeds certain thresholds, the control system is able to provide SMS and/or e-mail notification to the related maintenance team with the Android smartphone. Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Decision Tree (DT) algorithms applied to the collected data and models were created. As a result of accuracy analysis over %96 success was achieved for all models.

A b s t r a c t . Commercial corn flakes were equilibrated to different water activities and subjected to mechanical deformation. Breaking of co rn flakes was done in a plastic tube in which flakes were piled one over another. Sounds emitted during breaking were detected by a piezoelectric accelerometer and recorded after amplification. Def ormation curves were

Bearings are critical components of rotating machinery and monitoring their condition is important to avoid catastrophic failures and reduce the machinery down-time. Acoustic emission (AE) is gaining ground as a complementary condition monitoring technique as it offers earlier fault detection compared with other more established techniques, such as vibration analysis or oil analysis. However, AE signals always include a significant level of noise reducing the potential of defect detection at early stage. For this reason, this paper proposes a novel envelope analysis method for bearing incipient defect detection. This method is able to identify localized defects in an incipient stage, in which the signal-to-noise ratio (SNR) is extremely low. This method combines Wavelet packet, for AE signal denoising, the Hilbert Transform (HT) for envelope extraction and autocorrelation function, to find patterns in the AE signal. An extensive experimental investigation was carried out in order to evaluate the performance of the proposed method under extremely low SNR, adding high level of noise to the signals. The results indicate that the proposed enhanced envelope method is able to detect incipient defects with 9 dB lower SNR than traditional envelope analysis.

This paper investigates failure in Carbon Fibre Reinforced Plastics (CFRP) using Acoustic Emission (AE). Signals have been collected and post-processed for various test configurations: tension, Compact Tension (CT), Compact Compression (CC), Double Cantilever Beam (DCB) and four-point bend End Notched Flexure (4-ENF).

An experimental investigation has been carried out in order to evaluate the detection of cavitation in actual hydraulic turbines. The methodology is based on the analysis of structural vibrations, acoustic emissions and hydrodynamic pressures measured in the machine. The proposed techniques have been checked in real prototypes suffering from different types of cavitation. In particular, one Kaplan, two Francis and one Pump-Turbine have been investigated in the field. Additionally, one Francis located in a laboratory has also been tested.

The mechanical removal of materials using miniature tools, known as micro-mechanical milling processes, has unique advantages in creating miniature 3D components using a variety of engineering materials, when compared with photolithographic processes. Since the diameter of miniature tools is very small, excessive forces and vibrations significantly affect the overall quality of the part. In order to improve the part quality and longevity of tools, the monitoring of micro-milling processes is imperative. This paper examines factors affecting tool wear and a tool wear monitoring method using various sensors, such as accelerometers, force and acoustic emission sensors in micro-milling. The signals are fused through the neuro-fuzzy method, which then determines whether the tool is in good shape or is worn. An optical microscope is used to observe the actual tool condition, based upon the edge radius of the tool, during the experiment without disengaging the tool from the machine. The effectiveness of tool wear monitoring, based on a number of different sensors, is also investigated. Several cutting tests are performed to verify the monitoring scheme for the miniature micro-end mills.

Quality control during fabrication and failure monitoring during service have gained importance in the field of metal matrix composites because of their growing uses, especially for structural applications. This review aims to provide a survey of quality control and nondestructive testing relevant to metal matrix composites. The first part presents the determination of the reinforcement parameters which play a very important role in controlling the performance of metal matrix composites. The second part describes the main relevant nondestructive techniques used to identify the defects produced during fabrication or in service. It also shows that the validation of nondestructive techniques and the relationship between their results and component performance should be particularly emphasized in future research work on metal matrix composites. ; Fax: ϩ32 16 32 1992.

This study concerns with fault diagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various types of bearing defects associated with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired from the low speed bearing test rig using acoustic emission (AE) and accelerometer sensors under a constant load with different speeds. The aim of this study is to address the problem of detecting an incipient bearing fault and to find reliable methods for low speed machine fault diagnosis. In this paper, two methods of multi-class classification techniques for fault diagnosis through RVM and SVM are presented and the effectiveness of using AE and vibration signals due to low impact rate for low speed diagnosis. In the present study, component analysis was performed initially to extract the features and to reduce the dimensionality of original data features. The classification for fault diagnosis was also conducted using original data feature and without feature extraction. The result shows that multi-class RVM produces promising results and has the potential for use in fault diagnosis of low speed machine.

This chapter examines metallurgical developments related to bridge construction. Because of availability and cost, iron and steel have played the central role. We examine their roles and applications starting from ancient chain bridges of wrought iron, cast iron, and steel arch bridges, iron and steel truss bridges, wires and cables, and finally to spectacular long-span suspension and dazzling cable-stayed bridges. Introduction This chapter considers metallurgical developments that are related to bridge design and construction. Bridges are naturally of large scale, and iron and steels have been used as the principal components since antiquity. Common knowledge for engineers and historians includes the famous Iron Bridge in England, completed in 1779 (see Figure 1, reproduced from Nicholson 1829). It seems certain that this is the oldest iron arch bridge (Kostof 1985). Some argue that the completion of this bridge was the critical event for the Industrial Revolution, while others contend that Watt's steam engine in 1784 was the main impetus. Perhaps both contributed in tandem, multiplying their influence synergistically. From the metallurgical side, Abraham Darby's use of coke for pig iron production in 1709 represents an important step forward in allowing the construction of the Iron Bridge and for the rapid rise of the British steel industry (Cossons and Trinder 1979). The British Isles did not have an adequate supply of charcoal even in the 18 th century; for example, 70 wt % (from now on %) of its iron was imported from Russia and Sweden in 1770 (Ågren 1998). Yet, British iron production started to accelerate around 1780 (blue arrow), as shown in Figure 2 (data from Birch 1967 and Swank 1888). The history of iron bridges, however, takes us first to ancient times, long before the Industrial Revolution. A natural first place to examine the ancient metallurgy that enabled iron bridge building is in Asia, with a long tradition of constructing bridges. More recently, wires of ultra-high strength steels have been developed by the 1990s, making it possible to build long-span bridges. Advanced microstructural design at the nanoscale and modern fabrication methods are the keys to discover how we progressed from the Brooklyn Bridge to the Akashi Kaikyo Bridge.

The Joint Transportation Research Program serves as a vehicle for INDOT collaboration with higher education institutions and industry in Indiana to facilitate innovation that results in continuous improvement in the planning, design, construction, operation, management and economic efficiency of the Indiana transportation infrastructure.

A numerical model for rock is proposed in which the rock is represented by a dense packing of non-uniform-sized circular or spherical particles that are bonded together at their contact points and whose mechanical behavior is simulated by the distinctelement method using the two-and three-dimensional discontinuum programs PFC2D and PFC3D. The microproperties consist of stiffness and strength parameters for the particles and the bonds. Damage is represented explicitly as broken bonds, which form and coalesce into macroscopic fractures when load is applied. The model reproduces many features of rock behavior, including elasticity, fracturing, acoustic emission, damage accumulation producing material anisotropy, hysteresis, dilation, post-peak softening and strength increase with confinement. These behaviors are emergent properties of the model that arise from a relatively simple set of microproperties. A material-genesis procedure and microproperties to represent Lac du Bonnet granite are presented. The behavior of this model is described for two-and three-dimensional biaxial, triaxial and Brazilian tests and for two-dimensional tunnel simulations in which breakout notches form in the region of maximum compressive stress. The sensitivity of the results to microproperties, including particle size, is investigated. Particle size is not a free parameter that only controls resolution; instead, it affects the fracture toughness and thereby influences damage processes (such as notch formation) in which damage localizes at macrofracture tips experiencing extensile loading.

The use of acoustic emission (AE) technique for detecting and monitoring damages and the progress on damages in different structures is widely used and has earned a reputation as one of the most reliable and well-established technique in non-destructive testing (NDT). Acoustic Emission is a very efficient and effective technology used for fracture behavior and fatigue detection in metals, fiberglass, wood, composites, ceramics, concrete and plastics. It can also be used for detecting faults and pressure leaks in vessels, tanks, pipes, as well as for monitoring the progression of corrosion in welding. This paper reviews major research developments over the past few years in application of acoustic emission in numerous engineering fields, including manufacturing, civil, aerospace and material engineering.

Acoustic emission (AE) and microseismic (MS) events are indicators of rock fracturing or damage as the rock is brought to failure at high stress. By capturing the AE/MS events, underground excavation induced rock mass degradation or damage can be located and evaluated. A better understanding of the extent and shape of the excavation damaged zone (EDZ) or yield zone around caverns helps to arrive at safe and economic design and construction of the caverns. For this purpose, one needs to understand the AE mechanism associated with the excavation process.

This study focuses on the description of damage and failure behaviour of triaxial braided carbon/epoxy composites under tension. The tensile tests were instrumented with optical surface strain and acoustic emission measurements. Damage was observed using X-ray and microscopy. The damage develops in two stages: (1) intra-yarn cracking: increase of the crack density and crack length, (2) local inter-yarn delamination and conjunction of the intra-yarn cracks. Statistics of crack sizes at both stages were collected and the 3D geometry of cracks was reconstructed. A finite element model of the unit cell of the textile reinforcement is used to predict damage initiation and crack orientation using Puck's criterion. Progressive damage and stiffness deterioration is modelled using the degradation scheme of Murakami-Ohno and the damage evolution law of Ladeveze, applied to the average stress state of the yarns. Good agreement with experimental damage initiation threshold and non-linear tensile diagrams is found both for loading in fibre and off-axis directions.

... b) Source locations of induced microseismic events and compos-ite focal mechanisms for cluster events. ... towards a better understanding of the physical processes related to induced microseismicity at reservoir or ... Passive Seismic Monitoring of Natural and Induced Earthquakes ...

The main focus of this study is to identify the most influential and common sensory features for the process quality characteristics in CNC milling operations-dimensional accuracy (bore size tolerance) and surface roughness-using three different material types (6061-T6 aluminum, 7075-T6 aluminum, and ANSI-4140 steel). The materials were machined on a vertical CNC mill, retrofitted with multiple sensors and data acquisition systems, to investigate the effects of variations in material types and machining parameters. The sensor data include cutting force measurements, spindle quill vibration, and acoustic emission, each of which further divided into measurable components, such as x, y, and z components in cutting force, x and y spindle quill vibration, DC, AC, and Count Rate for acoustic emission signals. Those components were filtered and analyzed to determine the sensory features that best correlate with process quality characteristics. Tool wear rate and machining characteristics appeared differently, depending on the material types, yet some components of the sensory data were found to be significant with relation to the variations in bore size and surface roughness for all three types of materials. This suggests that even under the varying cutting conditions involving different materials, the identified sensory features can be used for the reliable and accurate control of milling operations. 