Explore the vast range of titles available.

Tool wear is one of the consequences of a machining process. Excessive tool wear can lead to poor surface finish, and result in a defective product. It can also lead to premature tool failure, and may result in process downtime and damaged components. With this in mind, it has long been desired to monitor tool wear/tool condition. Kernel principal component analysis (KPCA) is proposed as an effective and efficient method for monitoring the tool condition in a machining process. The KPCA-based method may be used to identify faults (abnormalities) in a process through the fusion of multi-sensor signals. The method employs a control chart monitoring approach that uses Hotelling’s T2-statistic and Q-statistic to identify the faults in conjunction with control limits, which are computed by kernel density estimation (KDE). KDE is a non-parametric technique to approximate a probability density function. Four performance metrics, abnormality detection rate, false detection rate, detection delay, and prediction accuracy, are employed to test the reliability of the monitoring system and are used to compare the KPCA-based method with PCA-based method. Application of the proposed monitoring system to experimental data shows that the KPCA based method can effectively monitor the tool wear.

A tool condition monitoring system can increase the competitiveness of a machining process by increasing the utilised tool life and decreasing instances of part damage from excessive tool wear or tool breakage. This article describes an inexpensive and non-intrusive method of inferring tool condition by measuring the audible sound emitted during machining. The audio signature recorded can be used to develop an effective in-process tool wear monitoring system where digital filters retain the signal associated with the cutting process but remove audio characteristics associated with the operation of the machine spindle. This study used a microphone to record the machining sound of EN24 steel being face turned by a CNC lathe in a wet cutting condition using constant surface speed control. The audio signal is compared to the flank wear development on the cutting inserts for several different surface speed and cutting feed combinations. The results show that there is no relationship between the frequency of spindle noise and tool wear, but varying cutting speed and feed rate have an influence on the magnitude of spindle noise and this could be used to indicate the tool wear state during the process.

In the information era, information security monitoring tools would be helpful for enterprises/organizations to monitor employees’ computer usage behaviors and improve their information security protection. The Windows-based operating systems have the largest market share in the world. Therefore, the study target is the development of a Windows-based information security monitoring tool in this study. We proposed an assessment model for developing an information security tool in this study to explore the significances of functionalities in a Windows-based information security monitoring tool and the decision-makers’ decision opinions. We adopted four steps with four study methods: the literature study method, the Delphi method, the analytic hierarchy process (AHP) method, and the analysis methods related to data-driven decision-making in the proposed model. In Step 1, we studied some literature about information security monitoring, and we discovered 26 functionalities as the decision criteria in this study. In Step 2, using the Delphi method, we confirmed the decision criterion set with potential decision-makers and organized the decision criteria hierarchy. In Step 3, we designed an AHP questionnaire to get the criterion weight vectors from the 12 decision-makers. With the AHP method, this study received the weights of the decision criteria and found that the 16 functionalities among the 26 functionalities should receive their corresponding developing priority in a Windows-based information security monitoring tool. Finally, we used the Pearson correlation coefficient and cosine distance to explore the correlations and similarities among the decision-makers’ decision opinions. This study found the relevance among the decision-makers’ decision opinions in a Windows-based information security monitoring tool developed with the Pearson correlation coefficients/the cosine distances among all pairs of decision-makers’ decision opinions.

Tool wear monitoring in machine process is essential for quality assurance, efficiency improvement, and cost reduction. Data-driven methods based on deep learning have become an effective solution for tool wear monitoring, but in actual cutting process, multiple sensors are often used for high-frequency sampling to obtain multi-level information, so that tool wear data has multi-channel spatial structure and time series characteristics, respectively. Therefore, aiming at the distribution of tool wear data, a one-dimensional residual structure is designed and a novel hybrid model integrating bi-directional long short-term memory (BLSTM) network is proposed, which is suitable for tool wear monitoring, named Re-BLSTM. Considering the spatial distribution of tool wear signal, a residual structure is constructed based on one-dimensional convolution neural network (1-D CNN) to realize abstract feature extraction and signal denoising, while gradient vanishing and degradation problem can be solved as the deeper of network. Additionally, batch normalization (BN) layer and dropout operation are introduced to form a pre-activation structure, which can alleviate the over fitting problem caused with the increase of model parameters; Subsequently, in view of the temporal correlation of feature, long-term dependence of concatenated feature is explored by integrating BLSTM and the learning range is also expanded. To verify the performance of the proposed model, experiments are carried out compared with several state-of-the-art method. The results show the effectiveness and generalization of the proposed method.

In drilling Carbon-Fibre-Reinforced Polymers (CFRP)/Al stacks, adaptive drilling facilitates the optimisation of cutting parameters for each constituent stack layer and tool wear, thus enhancing cutting efficiency and borehole quality. This study proposed a knot–Temporal Pyramid Pooling (TPP) model aimed at monitoring both process incidences and tool wear in the drilling of hybrid stacks, which subsequently informs the machine tool to adjust cutting parameters or, if necessary, replaces the tool. TPP is introduced to remove the restriction of input dimensions, allowing for the acceptance of inputs with arbitrary shapes. On the other hand, a knot structure has been proposed to incorporate the classification of process incidences into the tool wear analysis, thereby enhancing prediction accuracy. The proposed model achieves a process incidence identification accuracy of 99.19% and a Mean Absolute Error (MAE) of 10 μm in tool wear prediction, demonstrating robust performance across a wide range of sampling conditions. This achievement facilitates decision-making and optimisation relating to cutting parameters and tool replacement in the context of adaptive drilling of aerospace materials.

Deep twist drilling technique with length per diameter ratio of more than 10 is widely used, especially in tool and die industries. This technique can improve the quality and production of drilling products by increasing feed rate and can shorten the machining time. The limitation in this process is premature tool breakage due to tool wear, chip clogging, and tool failures. In this study, deep drilling process was analyzed via cutting force and vibrations by using three-axis force dynamometer and accelerometer sensors to detect failure criteria. Deep twist drills were analyzed through cutting parameters, such as cutting speeds, feeds, and depth of cut. The effects on the tool condition during cutting operations were measured by three-axis data of vibrations and force sensors and then analyzed in time and frequency domain. Results indicated that both sensors are capable of monitoring tool conditions. However, data produced by vibration sensors are more appropriate to detect initial conditions before tool failure. Thus, monitoring tool conditions in three axes can lead to precise data and earlier detection in the y axis instead of in the z axis. Cutting condition analysis found that cutting speed and feeds of more than 50 m/min and 0.25 mm/rev, respectively, result in tool failures under safety threshold in x , y , and z . Tool monitoring conditions in three axes are useful to show the deep drilling process failure criterion, such as good, small corner wear, large corner wear, blunt, and fracture. Tri-axial sensors are useful in developing an online condition monitoring tool for deep drilling process, especially in tool and die industries.

Presented in this work is a novel tool-condition criterion dubbed as the current rise criterion (CRC). This criterion is based on the measured current values of the machine tool’s spindle and drive motors. The CRC comprises two components: (1) the current rise index (CRI) and (2) a sensitivity factor (SF) indicated as a subscript to the CRI. Current rise criterion is described mathematically as CRC = CRI SF . The CRI that accounts for the damage (including wear) suffered by the tool is calculated as the square root of the sum of the squared percent increase in the root mean square (RMS) current values of the spindle and drive motors. To indicate the relative contribution of each of the machine tool motors to the CRI, the sensitivity factor (SF) reflects the ratio of the drive motor current percent rise to that of the spindle motor. The reference current used in calculating the percent rise of the motor current for both CRI and SF is measured at the first cut of the fresh tool. The versatility of the CRC was demonstrated here using two different machining processes: milling and drilling. Quantitative polar maps of the CRI and the associated sensitivity factor of cutting tools as well as qualitative descriptions of the various modes of tool condition afflicting the cutting tools are presented. CRC is demonstrated to be capable of monitoring the tool condition for a variety of cutting parameters of speeds and feeds. Another study demonstrated the versatility of CRC as a discriminator of the quality of chisel drills. It was found that the criterion successfully tracks the tool condition along a variety of process levels. CRC may be used to monitor tool condition and prognostics across practically all machining operations and process parameters, thus rendering the criterion “process independent.” CRC can also be used to monitor the change in power consumption of machine tools while cutting with worn tools.

A multiplex surface plasmon resonance (SPR) biosensor method for the detection of paralytic shellfish poisoning (PSP) toxins, okadaic acid (and analogues) and domoic acid was developed. This method was compared to enzyme-linked immunosorbent assay (ELISA) methods. Seawater samples ( n  = 256) from around Europe were collected by the consortia of an EU project MIcroarrays for the Detection of Toxic Algae (MIDTAL) and evaluated using each method. A simple sample preparation procedure was developed which involved lysing and releasing the toxins from the algal cells with glass beads followed by centrifugation and filtering the extract before testing for marine biotoxins by both multi-SPR and ELISA. Method detection limits based on IC 20 values for PSP, okadaic acid and domoic acid toxins were 0.82, 0.36 and 1.66 ng/ml, respectively, for the prototype multiplex SPR biosensor. Evaluation by SPR for seawater samples has shown that 47, 59 and 61 % of total seawater samples tested positive (result greater than the IC 20 ) for PSP, okadaic acid (and analogues) and domoic acid toxins, respectively. Toxic samples were received mainly from Spain and Ireland. This work has demonstrated the potential of multiplex analysis for marine biotoxins in algal and seawater samples with results available for 24 samples within a 7 h period for three groups of key marine biotoxins. Multiplex immunological methods could therefore be used as early warning monitoring tools for a variety of marine biotoxins in seawater samples.

The great success and widespread use of impregnated diamond (ID) bits are due to their self-sharpening mechanism, which consists of a constant renewal of diamonds acting at the cutting face as the bit wears out. It is therefore important to keep this mechanism acting throughout the lifespan of the bit. Nonetheless, such a mechanism can be altered by the blunting of the bit that ultimately leads to a less than optimal drilling performance. For this reason, this paper aims at investigating the applicability of artificial intelligence-based techniques in order to monitor tool condition of ID bits, i.e. sharp or blunt, under laboratory conditions. Accordingly, topologically invariant tests are carried out with sharp and blunt bits conditions while recording acoustic emissions (AE) and measuring-while-drilling variables. The combined output of acoustic emission root-mean-square value (AE rms ), depth of cut ( d ), torque (tob) and weight-on-bit (wob) is then utilized to create two approaches in order to predict the wear state condition of the bits. One approach is based on the combination of the aforementioned variables and another on the specific energy of drilling. The two different approaches are assessed for classification performance with various pattern recognition algorithms, such as simple trees, support vector machines, k -nearest neighbour, boosted trees and artificial neural networks. In general, Acceptable pattern recognition rates were obtained, although the subset composed by AE rms and tob excels due to the high classification performances rates and fewer input variables.

There are many items in the literature indicating that certain signal features (SFs) of cutting forces, vibrations or acoustic emission are useful for the diagnosis of tool wear in certain single experiments. There is no answer to whether these SFs are universal. The novelty of this article is an attempt to answer these questions and propose a large set of SFs related to tool wear, but without including superfluous SFs. The analysis of the usefulness of the signal properties for the state of the cutting tool in turning was carried out on a large experiment. A number of various SFs obtained for various signal analysis methods were selected for the study. It is found that no SF is always related to the tool wear, so we define many different signal characteristics that can be related to the tool wear (basic set) and automatically select those associated with it in a given machining case. To this end, the relationship between the measures and the wear of the tool was analyzed. Interrelated measures were excluded from it. The obtained results can be used to build a new generation of more effective tool wear diagnostics systems. One of the goals of the tool wear diagnosis system is to save the energy used. The results can also enable the refinement of existing algorithms that predict the energy consumption of a machine.