Analysis Of Force Signals Research Articles

Combining features on vertical ground reaction force signal analysis for multiclass diagnosing neurodegenerative diseases

Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world’s population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson’s disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington’s disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments.

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE (concurrent spatial and channel squeeze and excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and continuous wavelet transform. Concurrently, the images undergo processing using contrast limited adaptive histogram equalization and the structural similarity index method, allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer and Ti–6Al–4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model’s effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0.

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material's susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool.

Tool Run-Out in Micro-Milling: Development of an Analytical Model Based on Cutting Force Signal Analysis.

Micro-machining is a widespread finishing process for fabricating accurate parts as biomedical devices. The continuous effort in reducing the gap between the micro- and macro-domains is connected to the transition from conventional to micro-scale machining. This process generates several undesired issues, which complicate the process's optimization, and tool run-out is one of the most difficult phenomena to experimentally investigate. This work focuses on its analytical description; in particular, a new method to calibrate the model parameters based on cutting force signal elaboration is described. Today, run-out prevision requires time-consuming geometrical measurements, and the main aim of our innovative model is to make the analysis completely free from dimensional measurements. The procedure was tested on data extrapolated from the micro-machining of additively manufactured AlSi10Mg specimens. The strategy appears promising because it is built on a strong mathematical basis, and it may be developed in further studies.

Adaptive detection of tool-workpiece contact for nanoscale tool setting based on multi-scale decomposition of force signal

Due to tool wear under long-time and long-distance cutting, it is inevitable to change the tool to achieve relay machining. However, the existing tool setting methods based on visual inspection and trial cutting have the problems of low precision and complicated process, resulting in low precision and poor consistency of surface quality in relay machining. In order to solve the above problems, this paper proposes a method to detect the contact status between the tool and the workpiece with the discrete wavelet transform (DWT) of force signal to realize the nanoscale tool setting. Firstly, a weighted smoothness evaluation metric is proposed to guide the selection of wavelet basis function and decomposition scale of DWT to achieve noise suppression and impact signal enhancement. Then, the force signal processed by DWT under the optimal parameters is input to density-based spatial clustering of applications with noise (DBSCAN) to detect the contact status between the tool and the workpiece. The parameters selection of DBSCAN is adaptively realized by differential evolution (DE) algorithm so as to improve the accuracy of the contact detection. Finally, ultra-precision tool setting experiments were carried out on the single-point diamond turning tools with a radius of 10um and workpieces with various materials such as brass, copper and red copper. The results show that the wavelet basis function and decomposition scale with the minimum value of the proposed metric are optimal for the task. In analysis of force signals with different signal-to-noise ratios (1 dB, 5 dB, 12.5 dB, 20 dB), the proposed metric all works well. In terms of the contact detection between the tool and workpiece, the proposed method achieves 0 % false positive rate compared to other detection methods, and the corresponding tool setting accuracy is about 3 nm, which is much higher than the traditional tool setting method.

Quality Evaluation and Analysis of Force Signals in CFRP Helical Milling

Nowadays, drilling is one of the most widespread operations due to the need to obtain holes for mechanical joints. This operation is particularly relevant in the aeronautical sector due to the high number of operations performed, the high demands and the use of materials that are difficult to machine. The use of materials that enhance aircraft performance, such as carbon fiber composites (CFRP), poses a challenge for axial drilling operations. The characteristics of these materials and typical defects such as delamination are difficult to control due to the axial forces produced during drilling operations. Therefore, more efficient alternatives are required. In this context, helical milling operations have advantages such as lower axial stress, higher flexibility, and higher efficiency in heat and chip evacuation that put it in the spotlight. In this work, research has been carried out in which helical milling operations have been performed on CFRP. The main objectives were the analysis of surface quality, delamination, tool wear and force analysis. For this purpose, a working methodology has been developed combining machining parameters that define the kinematics of the cutting process (cutting speed, axial feed rate, and tangential feed rate). Finally, this information has allowed finding a correlation between quality indicators such as delamination and the forces generated during the cutting process and associated with progressive tool wear. The results show that the force signal could be used for on-line monitoring of the machining process.

A Multistage Cutting Tool Fault Diagnosis Algorithm for the Involute form Cutter Using Cutting Force and Vibration Signals Spectrum Imaging and Convolutional Neural Networks

In a machining system, tool condition monitoring systems are required to get a high-quality product and to prevent the downtime of machine tools due to tool failures. For this purpose, tool condition monitoring systems have become very important during the years since the mechanical faults can cause high cost. This study introduces a multistage cutting tool fault diagnosis method to detect the presence and level of the involute form cutter faults on the by the cutting force and vibration signal analysis. Therefore, different fault levels (low, medium and high) were generated on the involute form cutter as a tool breakage. During the experiments, the cutting force, vibration and acoustic signals were gathered with three different feed rates for each fault level. The gathered signals were processed by a multistage signal processing algorithm developed in the MATLAB environment. As an initial step, the continuous wavelet transform of the obtained signals was taken and saved as an image by the developed algorithm. After that, a convolutional neural network model is trained and tested by using the obtained images. The developed algorithm firstly checks the presence of the cutting tool fault. Once the algorithm labels the cutting tool is damaged, it then checks the damage level of the cutting tool fault. It is observed from the results, cutting force analysis is sufficient for the detection of cutting tool fault. On the other hand, the cutting force signal analysis is insufficient to detect the damage level of the cutting tool. Therefore, the vibration signal analysis is required to detect the damage level of the cutting tool. Results prove that, by the vibration analysis, the developed algorithm could detect not only the presence of the damage on the cutting tool but also the damage level. The results of the algorithm for each stage and signal are given in the results section.

Recurrence quantification analysis of force signals to assess neuromuscular fatigue in men and women

Recurrence quantification analysis (RQA) is a nonlinear method providing information on the temporal structure of time series. RQA has been extensively used to explore various noisy and nonstationary physiological signals. However, the application of RQA to force signals acquired during voluntary fatiguing contractions performed until exhaustion remain to be investigated. We aimed to explore the sensitivity of the percentage of determinism (DET), an RQA predictability measure, to detect changes of force signal complexity induced by fatigue and recovery. Changes in force signal complexity were compared between women and men to explore the ability of DET measures to detect different fatigue profiles. Nineteen women and nineteen men performed intermittent isometric contractions of knee extensors at 50% of maximal voluntary contraction (MVC) until exhaustion. Participants performed MVC before, during and after the fatiguing task to assess neuromuscular fatigue. Recovery measurements were performed three minutes after exhaustion. Particular attention has been given to the selection of the input parameters of RQA and to the influence of nonstationarity. A detailed methodology is provided to apply RQA to force signals. At the whole group level, complexity decreased with fatigue then increased after recovery. Greater fatigability of men was associated with a faster loss of complexity (i.e. faster increase of DET) of force signals. After recovery, complexity returned to baseline value only for women. These findings confirm that RQA is suited to explore force signal temporal structure and is able to reveal changes of complexity induced by fatigue and recovery by taking into account sex differences.

Tool Life Stage Prediction in Micro-Milling From Force Signal Analysis Using Machine Learning Methods

Abstract Tool condition monitoring is difficult in micro-milling due to irregular wear and chipping of the cutting edges, which lead to unexpected tool breakage. This study demonstrates the use of force data to reliably predict different tool life stages until tool breakage, while micro-milling hard materials like stainless steel (SS304) using tungsten carbide tools of 500 μm diameter. Extensive experiments involving machining of 465 slots over 62 min of machining time were performed in this study. The resulting voluminous force data were analyzed to divide the tool life into three stages based on the variation in the forces and other related features. The first stage is the initial 12.5% of the tool life, second stage consists of 12.5–70% of tool life, and the third stage is from 70% to 100% tool life. The analysis of the tool wear and cutting forces shows that the average tool diameter reduces by 32 μm, 67 μm and 108 μm, and the average resultant cutting force were 2.45 N, 4.17 N, and 4.93 N in stage 1, 2, and 3, respectively. To avoid catastrophic breakage of the tool, the tool life stages are predicted from the force data using machine learning models. Among the machine learning models, random forest method gave a better prediction accuracy of 88.5%. The model was further improved by incorporating the initial cutting edge radius as an additional feature, and the variance in the prediction was seen to drop by 48.76%.

Study and characterization of the ductile-brittle transition zone in sintered zirconia

Abstract Ceramics, being brittle in nature are highly prone to surface damage during machining. However, at depths of cut lower than a critical depth of cut, brittle materials undergo microscopic plastic shearing resulting in a crack-free surface. The transition from ductile to brittle takes place over a finite time and depth variation. The identification and characterization of the transition zone is important to attain damages free surface grinding. This work focuses on the identification of the ductile-brittle transition (DBT) zone in yttria stabilized sintered zirconia via the analyses of the force signatures. An algorithm has been developed for the prediction of the onset and end of the transition zone based on the gradients and fluctuations in the cutting force signals. The visual inspection of the ground surface validates the proposed methodology based on force signal processing. The transition zone characteristics were found to be affected by the process parameters and the tool geometry. The increase in the scratch speed reduced the transition onset depth but had negligible effect on the transition end depth. Smaller tool tip radius resulted in increased surface damage whereas larger tip radius gave a relatively smoother surface finish. This methodology of accurate prediction of DBT zone using cutting/thrust force behavior allows the manufacturers to achieve crack-free surfaces in brittle materials without keeping track of cutting depth during machining. Thus, the prediction of the ductile regime by the analysis of force signals opens new horizons in deciding threshold force and depth values in real time, which should be maintained to achieve a damage free surface during grinding.

Mechanism of material removal in orthogonal cutting of cortical bone

Analysisof a mechanism of bone cutting has an important theoretical and practical significance for orthopaedic surgeries. In this study, the mechanism of material removal in orthogonal cutting of cortical bone is investigated. Chip morphology and crack propagation in cortical bone for various cutting directions and depth-of-cut (DOC) levels are analysed, with consideration of microstructural and sub-microstructural features and material anisotropy. Effects of different material properties of osteons, interstitial matrix and cement lines on chip morphology and crack propagation are elucidated for different cutting directions. This study revealed that differences in chip morphology for various DOCs were due to comparable sizes of the osteons, lamellae and DOC. Acquired force signals and recorded high-speed videos revealed the reasons of fluctuations of dynamic components in tests. Meanwhile, a frequency-domain analysis of force signals showed a frequency difference between formation of a bulk fractured chip and small debris for different cutting directions. In addition, SEM images of the top and side surfaces of the machined bone were obtained. Thus, the analysis of the cutting force and surface damage validated the character of chip formation and explained the material-removal mechanism. This study reveals the mechanism of chip formation in the orthogonal cutting of the cortical bone, demonstrating importance of the correlation between the chip morphologies, the depth of cut and the microstructure and sub-microstructure of the cortical bone. For the first time, it assessed the fluctuations of cutting forces, accompanying chip formation, in time and frequency domains. These findings provide fundamental information important for analysis of cutting-induced damage of the bone tissue, optimization of the cutting process and clinical applications of orthopaedic instruments.

Dynamic force signal analysis in dry finish turning of Aluminium metal matrix composites

Metal matrix composites (MMC) have found wide applications in the transportation sector. But, the presence of hard particles in the MMCs causes catastrophic tool failures. The current study presents a method to select process parameters to increase the material removal rate in finish turning of Al-MMC (Al 6061, 5% SiC, 3% C) using TiN coated carbide inserts. The key aspects of the method are (a) Using the fractional factorial method for performing experiments economically (b) Using radial force instead of cutting force (c) Using frame statistics and linear spectrum of the radial cutting force signal to select process parameters. The experiments were conducted on a precision lathe. A 6-component piezoelectric dynamometer was used to measure the cutting force.

Analysis of Force Signals for the Estimation of Surface Roughness during Robot-Assisted Polishing.

In this study feature extraction of force signals detected during robot-assisted polishing processes was carried out to estimate the surface roughness during the process. The purpose was to collect significant features from the signal that allow the determination of the end point of the polishing process based on surface roughness. For this objective, dry polishing turning tests were performed on a Robot-Assisted Polishing (RAP) machine (STRECON NanoRAP 200) during three polishing sessions, using the same polishing conditions. Along the tests, force signals were acquired and offline surface roughness measurements were taken at the end of each polishing session. As a main conclusion, it can be affirmed, regarding the force signal, that features extracted from both time and frequency domains are valuable data for the estimation of surface roughness.

Machining quality and cutting force signal analysis in UD-CFRP milling under different fiber orientation

Increasing usage of carbon fiber-reinforced polymer (CFRP) components in the airplane industry has led to higher demand in the machining quality of CFRP. In order to meet the quality requirements of final assembly, the signal monitoring of various processing parameters can be used to ensure the machining setup in perfect order. In the present work, a set of experiments for UD-CFRP side milling has been carried out with different fiber cutting angle. The effects of fiber cutting angle on machining surface quality as well as the corresponding milling force signal features have been investigated based on time, frequency domain analysis, and wavelet analysis methods. The analysis results demonstrate that, for the against fiber cutting condition, the cutting force signal has a significant increase for high-frequency component energy percentage, and the higher frequency components are more sensitive. In addition, the fluctuation trend of power percentage for frequency band S3 (1500–2500 Hz) and surface roughness is quite close, which can be employed as a monitoring index for CFRP machining surface roughness.

Novel paddle stroke analysis for elite slalom kayakers: Relationship with force parameters.

This study was divided into two complementary parts. In Part 1, we proposed a novel paddle strokes analysis based on the force signal from a 30-s all-out tethered test; and compared these results with video recordings. In Part 2, we investigated the relationship between force data from the same test with paddle stroke results from both methods. Eleven male elite slalom kayakers (Brazilian national team) were evaluated. The tethered test was conducted for force parameters analysis (peak-force, mean-force, impulse). Video recording analysis was conducted, and the performed strokes (V.NumberPaddle) was counted and frequency (V.FrequencyPaddle) calculated by the V.NumberPaddle divided by 30 (i.e. total time of test). The new method consisted of performed strokes and frequency achievement from a load cell force signal analysis (S.NumberPaddle and S.FrequencyPaddle, respectively). Paired test-t did not show difference between methods results, but significant correlations were only obtained for the number of paddle strokes. Force parameters were only correlated with S.NumberPaddle and S.FrequencyPaddle. Overall, considering the theoretical and practical application, we propose that the new method should be used as an alternative to the video recording.

Process monitoring in milling unidirectional composite laminates through wavelet analysis of force signals

The extensive application of composite materials in the aerospace and automobile industry and significant machining challenges warrant the need of process monitoring techniques. These techniques help in detecting dynamical instabilities, monitoring machining induced damage and surface quality within a given tolerance. This study discusses the application of continuous wavelet transform analysis in trimming of unidirectional graphite epoxy laminates. The suggested approach is used to obtain time-frequency scalograms and windowed Shannon entropy to characterize the process for different machining conditions and fiber orientation angles. The relative wavelet entropy (RWE) proved to be an efficient classifier in characterizing and developing optimum values at different experimental conditions. A deviation from these optimum values indicated severe damage. In addition, a positive correlation was found between RWE and surface quality.

In vitro evaluation of the acetabular cup primary stability by impact analysis.

The implant primary stability of the acetabular cup (AC) is an important parameter for the surgical success of press-fit procedures used for the insertion of cementless hip prostheses. In previous studies by our group (Mathieu, V., Michel, A., Lachaniette, C. H. F., Poignard, A., Hernigou, P., Allain, J., and Haiat, G., 2013, "Variation of the Impact Duration During the in vitro Insertion of Acetabular Cup Implants," Med. Eng. Phys., 35(11), pp. 1558-1563) and (Michel, A., Bosc, R., Mathieu, V., Hernigou, P., and Haiat, G., 2014, "Monitoring the Press-Fit Insertion of an Acetabular Cup by Impact Measurements: Influence of Bone Abrasion," Proc. Inst. Mech. Eng., Part H, 228(10), pp. 1027-1034), the impact momentum and duration were shown to carry information on the press-fit insertion of the AC within bone tissue. The aim of the present study is to relate the impact momentum recorded during the AC insertion to the AC biomechanical primary stability. The experimental protocol consisted in testing 13 bovine bone samples that underwent successively series of 15 reproducible mass falls impacts (5 kg, 5 cm) followed by tangential stability testing. Each bone sample was tested with different hole sizes in order to obtain different stability configurations. The impact momentum and the tangential primary stability reach a maximum value for an interference fit equal to around 1 mm. Moreover, a correlation between the impact momentum and the stability was obtained with all samples and all configuration (R2 = 0.65). The implant primary stability can be assessed through the measurement of the impact force signal analysis. This study opens new paths for the development of a medical device which could be used as a decision support system to assist the surgeon during the insertion of the AC implant.

Failure Prediction by Means of Cepstral Analysis and Coherence Function between Thrust Force and Torque Signals

Requirements for flexible manufacturing have been increasing in the last years. In order to insure effective operation of expansive manufacturing equipment, which has to run automatically and unattended, tool monitoring is important. Therefore, the essential problem to be overcome to achieve the full potential of unmanned machining is the development of effective and reliable sensors system to monitor the process and corrective actions in case abnormal operation. The ultimate goal of the development of such production equipment is to enhance the overall economic of the manufacturing process. Even when there are at present many monitoring systems commercially available in the market for turning processes, serious difficulties still remain to be solved to apply monitoring systems successfully in machining centres. Being these difficulties mainly related with the limited accessibility to the rotating tool for sensing purposes in tool driven machining processes. Therefore, the primary objective of this paper assess the feasibility of using force signal analysis as means for monitoring tool condition in drilling

Wavelet Transform Denoise of PCB Drilling Force Signal

In the PCB micro drilling, because the force signal is tiny, and when the micro-drill drill to a certain degree of multilayer PCB, alternate force signals will not appear obvious, through to the drilling force signal analysis, we can know the drill bit position and the materials to the influence of the drill failure, so the drilling force signals denoise seems extremely important. In the processing of the non-stationary signal, traditional signal processing method has a certain extent of insufficient, using the wavelet packet decomposition signal, the white noise variance and amplitude decrease with the increase of wavelet scales, but the signal variance and amplitude has nothing to do with the wavelet transform. According to the view of the signal energy, first of all, we make the multiscale decomposition of the signal, then, by using some of the wavelet packet that has efficient energy to reconstruct the original signal. Comparing with the traditional threshold denoising ,using this method in the test signal to deal with the noise can effectively eliminate the white noise interference, and has good denoising effects besides the simple calculation.

SWT-BASED FORCE SENSOR DE-NOISING FOR THE NEUROSURGICAL ROBOT

When our proposed neurosurgical robot is applied, the neurosurgeon usually wants to sense the force on the remote site to operate on patients. The force signal analysis is of critical importance for neurosurgeons to perform stable, reliable, and safe operations. In this paper, based on the stationary wavelet transform (SWT), force information analysis and process is designed. Since force sampled by the JR3 sensor contains noise from the sensor and mechanical vibration when drilling, to smooth the force signal sent to the operator, SWT-based force information de-noising is proposed to reduce the noise significantly, especially for the force along the x and y axes. Simulations and experiments further verified the proposed research.