Real Machine Research Articles

Tool wear prediction based on K-means and Adaboost auto-encoder

Abstract A new tool wear prediction model is proposed to address the tool wear issue, aimed at monitoring tool wear based on specific task requirements and guiding tool replacement during actual cutting operations. In the data preprocessing phase, tool wear states are classified using unsupervised K-means clustering. The time, frequency, and time-frequency domain features are then labeled and fused using an autoencoder neural network applied to the original set of signal features from the tool. For tool wear prediction, an enhanced autoencoder neural network leveraging AdaBoost is employed to establish the prediction model. The reconstruction error serves as the chosen loss function to assess the autoencoder's performance, taking into account data correlation and the inherent lossy nature of the autoencoder. Experimental results from real machining data obtained from a CNC milling machine demonstrate that the proposed model achieves higher prediction accuracy while reducing data dimensions.

An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO2 emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching 85% compared to 80.88% for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of 9.5 s compared to 2.100 s for the other model.

Novel Approach for Detection of BRAF and KRAS Mutations Using ARMS (amplification refractory mutation system) Primers for Colon Cancer Therapeutic Response Prediction

Abstract Introduction/Objective KRAS and BRAF V600E mutations are confirmed predictive biomarkers for anti-EGFR monoclonal antibody therapy response and prognosis for colorectal cancer and often considered mutually exclusive with few exceptions (0.05% of mCRC cases). We have developed a multiplex PCR assay utilizing ARMS (amplification refractory mutation system) Primers and Probes for screening and further testing of BRAF mutation status in a 2 step process. Methods/Case Report The Multiplex PCR Assay or BRAF/KRAS mutation was designed as a 5 color assay. Primers and Probes consisted of Wild Type Kras and wild and mutant (V600E/V600K) type BRAF primers and probes. For specimen, Positive and negative control standards made of engineered positive control plasmids for wild type KRAS & BRAF and mutant type V600E & V600K BRAF as well as internal control of bet actin and 2 Patient samples with known BRAF V600E mutant status were run. Mutant type V600E & V600K BRAF were mixed with wild type V600 controls in different ratios as well. Results were interpreted as BRAF wild KRAS wild/ BRAF wild KRAS mutant/ BRAF V600E KRAS wild/BRAF V600K KRAS wild type and NGS was recommended in cases of concurrent BRAF and KRAS mutations. Results (if a Case Study enter NA) Positive/negative/internal control standards for wild type KRAS & BRAF and mutant type V600E & V600K BRAF showed expected results in both CFX 96 and ABI 7500 runs in triplicates. Graphs were able to determine separate Ct values and sigmoidal amplification plots that readily differentiated KRAS wild/BRAF wild/ BRAF V600E types. Mutant type V600E & V600K BRAF that were mixed with wild type V600 controls in different ratios all showed expected results and were able to differentiate between wild and mutant types with accuracy. Also, the 2 patient samples with known (Sanger sequencing-diagnosed) V600E mutation status showed positive Ct values for KRAs wild and V600E probes and negative CT values for V600 wild/V600K probes. No invalid runs or unexpected control results were observed. Conclusion The ARMS BRAF/KRAS multiplex test is a efficient, accurate 2 step assay. In the patients and positive and negative controls prepared, it was able to reliably diagnose KRAS and BRAF mutation status in a matter of 4 hours running time. Given the fact that this is a cost effective assay that can utilize most commonly used Real time PCR machines this test may be clinically implemented if larger clinical trials utilizing tissue samples from a larger number of colon cancer patients are conducted.

Hybrid predictive maintenance model – study and implementation example

Abstract In this paper, the concept of hybrid predictive maintenance for a single industrial machine is presented. A review of the solutions in the area of machine maintenance (especially predictive maintenance) which have been described in the literature is provided. The assumptions of the hybrid predictive maintenance model for modules, machines, or systems are presented. The methods used within the developed methodology are described. This includes the use of diagnostic data, experience, and a mathematical model. A case study of an industrial machine on which a system for collecting diag-nostic data has been pilot-implemented, using, among others, vibration sensors and drive system pa-rameters for damage detection is presented. The registered data can be used to precisely determine the time of upcoming failure after detection of the characteristic symptoms resulting from component wear In addition, an analysis of the durations of correct operation and failure events was performed and indicators describing these values were determined. The values of the aforementioned indicators were determined based on empirical data and described using a gamma distribution. The objective of the research was to prepare, implement and draw conclusions on a hybrid predictive maintenance model. A real industrial machine was used in the research study. The hybrid predictive maintenance model presented in this paper enables the use of data of different types (diagnostic, historical and mathemat-ical model-based) in scheduling machine downtime for maintenance actions. On the basis of the re-search conducted, it was determined which machine operating parameters are characterised by varia-bility that enables the detection of upcoming failure. This allows for precise planning of maintenance activities and minimization of unplanned downtime.

Backstepping control of DFIG wind turbine system based rotor flux observer

This paper introduces a novel approach to controlling a wind energy system based on a doubly fed induction generator (DFIG) using backstepping control. The proposed control method allows for independent control of the active and reactive power of the stator. Unlike traditional methods that rely on vector control of the stator fluxes and neglect the stator resistance, this approach takes into account the nonlinearities and coupled nature of the system. By using a nonlinear model in the Park frame, the control is more accurate and closer to the behavior of a real machine. This method has yet to consider any estimation of the rotor flux model. Using a high-gain observer can resolve the issue of measuring rotor flux. The stability of the nonlinear observer is demonstrated through the application of Lyapunov theory. The simulation results indicate that the suggested control scheme has enhanced the dynamic performance of the system.

Shaping of the risk of a series-parallel manufacturing structure maintenance according to quasi-coherence and the [formula omitted]-th survival value

The paper presents the authors’ model for determining the probability value of the survival function of the difference of two random variables: TP – the amount of time of correct operation and TB – the amount of time of failure. For this purpose, the concept of K-th survival value is introduced. The K-th survival value defines the probability that the total time of correct operation of the production system will be longer by at least k time units (for k≥0) than the total amount of time of failures. The model was exemplified by a real production system with a series–parallel structure. In the nesting manufacturing structure, the availability of machines determines the number and the type of flow streams of the processed material. To formalise the chains of relations at a given Δt, this paper utilises the quasi-coherence property. The paper identifies four possible cases of system operation: 1) a fully operational system in which every machine is capable of meeting the assumed production schedule; 2) a system which has lost the property of quasi-coherence (certain system objects are in an inoperable state) but it is possible to meet the assumed production schedule; 3) a system which has lost the property of quasi-coherence but the assumed task schedule is possible to meet after redefining the material flow streams; 4) a system which has lost the property of quasi-coherence and it is impossible to meet the assumed production schedule. Based on the definition of the survival function [1] S of the random variable T: ST:=PT>τ=1-FTτ,τ∈R, the concept of K-th survival value was introduced. For possible cases, K-th survival value was defined as: KT1,Ti=PT1-Ti≥k=1-FT1-Tik (for i=2,3,4), where T1 and Ti are random variables with given probability distributions. In turn, k is a deterministic value that determines the total number of time units of correct system operation in relation to the total failure time. The application of the developed algorithm enables the system to be analysed in terms of three aspects: (1) to determine the probability of on-time execution of tasks, taking into account historical values of machine failure and correct operation durations; (2) to determine the stability of the system based on empirical data; (3) to monitor the level of risk in real time. The paper validates the model by determining the stability of the analysed system for real machine failure data. The introduced criterion of stochastic stability of the production system has been verified for data in which distributions of correct operation times and failure times are random variables from the family of Gamma distributions. The presented model can be included in the group of multifactorial risk assessment methods.

Review of surface metrology artifacts for additive manufacturing

Test artifacts, resembling real machine parts, allow quantitative evaluation of system performance and insight into individual errors, aiding in improvement and standardization in additive manufacturing. The article provides a comprehensive overview of existing test artifacts, categorized based on geometric features and material used. Various measurement techniques such as stylus profilometry and computed tomography are employed to assess these artifacts. It was shown that Selective Laser Melting (SLM) technology and titanium alloys are prevalent in artifact creation. Specific artifact categories include slits, angular aspects, length parameters, variable surfaces, and others, each accompanied by examples from research literature, highlighting diverse artifact designs and their intended applications. The paper critically discusses the main problems with existing geometries. It paper underscores the importance of user-friendly and unambiguous artifacts for dimensional control, particularly in surface metrology. It anticipates the continued growth of metrological verification in future manufacturing environments, emphasizing the need for precise and reliable measurement results to support decision-making in production conditions

Federated Many-Task Bayesian Optimization

Bayesian optimization is a powerful surrogate-assisted algorithm for solving expensive black-box optimization problems. While Bayesian optimization was developed for centralized optimization, the availability of massive distributed data has attracted increased interests in exploring federated Bayesian optimization that can use data on multiple clients without leaking the raw data. However, existing federated Bayesian optimization (FBO) approaches assume that either all clients jointly solve the same optimization task, or only one client solves one target optimization task by transferring knowledge from others in a federated way, making them unsuited for many real-world applications. In this paper, we consider FBO for the scenario where multiple related local black-box tasks associated with different clients are jointly optimized by sharing knowledge between tasks without leaking the data privacy. An efficient federated many-task Bayesian optimization framework is proposed to address not independent and identically distributed (non-IID) data while protecting the data privacy in the federated setting. A novel federated knowledge transfer paradigm is developed for dynamic many-task model aggregation according to a dissimilarity matrix. The dissimilarity is measured based on the rank of the predictions and only the hyperparameters in the local Gaussian process models are shared. In addition, a federated ensemble acquisition function is constructed by integrating the predictions of two surrogates using the global and local hyperparameters, respectively, to effectively search for the optimal solution. Experimental results show that our proposed method has reliable performance on both benchmark problems and a real machine learning problem also in the presence of non-IID data.

Methods for Determining Losses and Parameters of Cylindrical-Rotor Medium-Power Synchronous Generators

This paper presents the analytical and numerical application of a method to determine the parameters and power losses in the core of two medium-power synchronous generators. These generators are used as emergency power sources powered by diesel engines, gas engines, and gas turbines. They cover peak electricity demand but can also be used in traction drives. This article presents a new numerical method for determining losses in the generator core based on the use of a time-stepping solution using the FEM method and calculating these losses using analytical formulas. In calculating the losses for the FEM method, approximations of the loss characteristics of the sheet were used with a wide range of induction values and frequencies. This method is specific to the solution used and was adapted from the authors’ previous work on losses in induction machines. A one-phase winding with alternating voltage was supplied to determine the basic parameters in the form of synchronous reactance. Also, an important novelty is the introduction of a new method of determining the saturation state of the magnetic circuit, which significantly affects the machine parameters. The obtained results were used in analytical calculations and implemented in a computer program that allows for the calculation of electromagnetic parameters, operating characteristics, and core losses, taking into account additional losses, total losses, and efficiency, as well as machine parameters in unsteady operating states and the current characteristics of a three-phase symmetrical short circuit at the machine terminals. The calculations obtained were verified experimentally by measurements of real machines.

An effective investigation of chatter prediction system on Al6061 alloy in an end milling process

Visual examination of the surface topography, in conjunction with the other sensors, may confirm the existence of chatter. Online chatter detection during real machining operations is possible with the use of sensors, and the presence of noise in their output and restricted bandwidth are the major drawbacks of these sensors. Productivity drops and manufacturing costs go up when there is a lot of chatter in the machining process. In the present paper, an integrated spindle tool system is modeled using finite element method with Timoshenko beam theory including rotational and shear deformation effects. To maximize the average stable depth of cut in an end milling process while simultaneously minimizing the chatter vibration levels, real time and offline strategies have been investigated. Machining experiments performed on Al6061-alloy specimens provide an empirical confirmation of the stability boundaries. The surface topography methods such as scanning electron microscope (SEM) and optical microscope images along with vibration levels are considered, to identify chatter marks under various machining conditions, which helps to assure cutting process stability. Stability lobe diagrams are plotted with these derived conditions and observed at an incremental level in the axial depths of the cut. The methodology shown in this paper improves the machining stability of the end milling with the reduction in the tool tip vibrations.

Enhancing Predictive Maintenance in Contour Cutting Machines: A Similarity-Based Approach for Band Blade Remaining Useful Life Estimation and Early Fault Detection

Anticipating the Remaining Useful Life (RUL) of cutting tools is crucial for optimising operational efficiency, ensuring safety, and minimising maintenance costs. This paper addresses the unresolved challenge of unexpected band blade failures in contour cutting machines within the foam and plastic industry, where such failures disrupt production, lead to material wastage, and pose operational hazards. Band blades circulate continuously at very high speeds during operation, and no classic sensor technology can be applied to them. Any information required to estimate the condition of the band blades must be acquired externally. Using similarity-based prediction models, a strategy for estimating band blades' effective RUL is proposed. Historical failure data is systematically collected from a contour-cutting machine operating under various load conditions using multiple contact and non-contact sensors mounted on the machine. Features are extracted and processed from the mounted sensors in both time and frequency domains using classical condition monitoring techniques. The classification of damaged and undamaged states of the blade is performed using bagged decision trees, which consider predefined health levels of the band blade during test runs. Degradation trends are generated for a test set of blade failures, and a similarity model is trained on this data to determine the remaining life. Different features are selected based on their prognostic capabilities to optimise the proposed algorithm's performance. The developed model is tested on new data, demonstrating efficacy in predicting band blades' health status and RUL with good accuracy. This research contributes to cutting tool condition monitoring and predictive maintenance by offering a novel and reliable approach for early fault detection and estimation of the RUL of band blades in contour cutting machines, mitigating disruptions, reducing material wastage, and enhancing overall operational efficiency. The procedures are validated in situ with a latest generation's real contour-cutting machine.

Achieving federated logistic regression training towards model confidentiality with semi-honest TEE

In the distributed machine learning field, federated learning (FL) serves as a highly effective framework for dismantling data silos and integrating data from multiple sources. However, the flourishing of FL still encounters significant challenges, particularly in the realms of data security and information privacy. In recent years, plenty of privacy-preserving FL schemes have been proposed, yet few of them support global model confidentiality protection for the training server. In this paper, by combining Trusted Execution Environment (TEE) and homomorphic encryption, we propose a TEE-assisted federated logistic regression training scheme with model confidentiality protection, named TFLR. Specifically, we first formalize a cryptography-TEE hybrid security model under the multi-party cooperative computation scenario. Subsequently, within this hybrid security model, we design a series of secure computation protocols with semi-honest TEE to execute non-linear operations while maintaining the encryption form of global model parameters. Therefore, the global model of the training server can be well protected. In addition, TFLR incorporates a double masking technique, further fortifying the privacy of data owners' local updates. Detailed security analysis shows that TFLR is able to protect all sensitive information of data owners and the training server. Furthermore, we evaluate the performance of TFLR with real machine learning datasets, and the results substantiate that TFLR is indeed efficient.

On some methods of determining proper frequencies torsional oscillations in metal-working machines

Forced torsional vibrations that occur in machine tool drives are one of the pressing problems of modern machine tool industry. The reasons for these fluctuations may be imbalances of rotating masses, structural imperfections of bearing units, operating or technological process features (for metal-cutting machines – eccentric clamping of the workpiece, processing of non-round workpieces, etc.). In this work, using the example of a real metal-cutting machine, the task of dynamic analysis of the drive of the main movement of the machine with determination of the natural frequencies of torsional vibrations is realized. The real object is replaced by a dynamic model (DM). When constructing the DM, the inertial and rigidity characteristics are determined from the conditions: – the kinetic energy of the object is equivalent to the kinetic energy of the DM (from this condition the axial moments of inertia of the concentrated masses of the row model are obtained); – the potential energy of elastic deformations of the object is equivalent to the potential energy of deformation of the DM (from this condition the rigidity coefficients of the DM are obtained). The natural frequencies of the row DM were determined in three ways: 1) by solving the frequency equation (exact method) in combination with the method of reducing the number of masses of A.P. Cherevkov; 2) partial systems method (fundamental frequency only); 3) method of residuals (Tolle). The following conclusions were made: 1) the main (smaller) frequency can be found by any of the methods considered (since the results of the three methods coincided); 2) when simplifying the DM using the method of partial systems, the criterion for the possibility of further simplifications over the specified frequency range should be taken into account; the transition from a three-mass DM to a two-mass one if the criterion in this calculation was not met led to an error in determining the value of the fundamental frequency of about 10%; 3) dynamic calculations of machines using DM and methods for determining natural frequencies make it possible, based on known frequencies of disturbing forces, to identify possible near-resonant operating modes and avoid them in advance by making changes to the design of the machine at the design stage

Adaptive angle-weighted cumulative sum for interpretable machine condition monitoring

Health indicators (HIs) are important indicators in machine condition monitoring (CM) to determine whether downtime is required for maintenance. It can quantitatively characterize the degradation stages of machines in different operating conditions and provide theoretical guidance for predictive maintenance of machines. Therefore, the accurate construction of HIs is of great significance for the safe operation and economic savings of machines. In recent years, extensive research has been conducted on numerous deep learning-based methods for HI construction. However, these approaches heavily depend on historical degradation data from machines. Furthermore, the HIs constructed through deep learning often lack sufficient interpretability, thus restricting their widespread application. To tackle this challenge, an innovative HI construction method tailored for interpretable machines is presented, employing the adaptive angle-weighted cumulative sum (AAWCS) approach derived from a thorough investigation of machine failure degradation mechanisms. Firstly, low-pass filtering is employed to mitigate the effects of rotational speed fluctuations and background noise interference existing in machines for actual industrial applications. Besides, the arctangent transform is applied to improve the signal-to-noise ratio of the original run-to-failure (RF) vibration signal collected by the machine, which is transformed into the angle-domain for the next step of analysis. On this basis, an adaptive angle weight (AAW) assignment mechanism is performed to obtain an angle-domain weight matrix related to the degree of machine failure deterioration in depth, which has a clear interpretability. Besides, the Shannon entropy is employed to quantitatively evaluate the angle-domain weight matrix for measuring correlations with the degree of machine failure deterioration, which enables the accurate construction of HI that can characterize the machine health status. Finally, the validation of the AAWCS method is performed with a typical bearing RF data and two real industrial machines RF data, which demonstrate that the AAWCS method can be generalized to adaptive CM of wind turbines under variable working conditions.

Изучение отображения вибрационных возмущений в геометрии формируемой резанием поверхности при точении

Introduction. The development of virtual digital models of the machining process on metal-cutting machines is a dynamically developing direction of increasing the efficiency of machine-building production. Such models include subsystems of parts quality prediction. Accuracy and validity of its work directly depends on the built model of dynamic cutting system, which is perturbed by force noise, the sources of which have different physical origin. In addition, the autonomous dynamic system itself is a generator of various attracting sets of deformations, such as limit cycles or chaotic attractors. Taking into account various nonlinear transformations in the properties of the dynamics of the cutting process allows increasing the adequacy of the model to the real process and is an actual task in the construction of simulation modeling systems of the dynamics of surface machining by cutting. Study object. Our earlier studies allow us to determine the geometry corresponding to the deformation trajectories of the surface formed by cutting. However, the adequacy of the mapping of the calculated trajectories to the geometry estimates remains not quite clear. The proposed paper focuses on achieving an adequate mapping of calculated as well as measured strain trajectories into the geometric topology of the part. The aim of the work is to evaluate the mapping of vibration perturbations of the system into the geometry of the surface formed by cutting. Method and methodology. The research is of experimental-theoretical nature. The content of the research includes the study of the correspondence of frequency characteristics obtained on the model and in real machining. The main attention is paid to the mapping of deformations to the part geometry. For this purpose, the paper considers the coherence functions between the strain functions and the part profile. Results and Discussion. It is shown that the conditioning of these transformations has a limited frequency range in which the explanation of the variable components of the generated relief is statistically significant. Mathematical modeling of the dynamic cutting system based on the mechanics of interaction between tool and workpiece allows adequate prediction of the macro geometry of the part formed by cutting. The obtained mathematical tools can be used to create systems for predicting the geometry of the machined surface.

Augmented reality to visualize a finite element analysis for assessing clamping concepts

This paper presents the development of an innovative augmented reality application for evaluating clamping concepts through visualizing the finite element analysis. The focus is on transforming the traditional simulation results into immersive, holographic displays, enabling users to experience and assess finite element analysis in three dimensions. The application development process involves data processing by MATLAB, visualization in the software Unity, and displaying holograms through Microsoft’s Hololens2. The most significant advancement introduces a new algorithm for rendering different finite elements in Unity. The application targets not only university engineering students but also vocational students with limited background in finite element analysis and machining, aiming to make the learning process more interactive and engaging. It was tested in a real machining environment, demonstrating its technical feasibility and potential in engineering education.

Faster Evaluation of Dimensional Machine Performance in Additive Manufacturing by Using COMPAQT Parts

Knowing the tolerance interval capabilities (TICs) of a manufacturing process is of prime interest, especially if specifications link the manufacturer to a customer. These TICs can be determined using the machine performance concept of ISO 22514. However, few works have applied this to Additive Manufacturing printers, while testing most of the printing area as recommended takes a very long time (nearly 1 month is common). This paper, by proposing a novel part design called COMPAQT (Component for Machine Performances Assessment in Quick Time), aims at giving the same level of printing area coverage, while keeping the manufacturing time below 24 h. The method was successfully tested on a material extrusion printer. It allowed the determination of potential and real machine tolerance interval capabilities. Independently of the feature size, those aligned with the X axis achieved lower TICs than those aligned with the Y axis, while the Z axis exhibited the best performance. The measurements specific to one part exhibited a systematic error centered around 0 mm ± 0.050 mm, while those involving two parts reached up to 0.314 mm of deviation. COMPAQT can be used in two applications: evaluating printer tolerance interval capabilities and tracking its long-term performance by incorporating it into batches of other parts.

Remaining useful lifetime prediction for milling blades using a fused data prediction model (FDPM)

AbstractIn various industry sectors, predicting the real-life availability of milling applications poses a significant challenge. This challenge arises from the need to prevent inefficient blade resource utilization and the risk of machine breakdowns due to natural wear. To ensure timely and accurate adjustments to milling processes based on the machine's cutting blade condition without disrupting ongoing production, we introduce the Fused Data Prediction Model (FDPM), a novel temporal hybrid prediction model. The FDPM combines the static and dynamic features of the machines to generate simulated outputs, including average cutting force, material removal rate, and peripheral milling machine torque. These outputs are correlated with real blade wear measurements, creating a simulation model that provides insights into predicting the wear progression in the machine when associated with real machine operational parameters. The FDPM also considers data preprocessing, reducing the dimensional space to an advanced recurrent neural network prediction algorithm for forecasting blade wear levels in milling. The validation of the physics-based simulation model indicates the highest fidelity in replicating wear progression with the average cutting force variable, demonstrating an average relative error of 2.38% when compared to the measured mean of rake wear during the milling cycle. These findings illustrate the effectiveness of the FDPM approach, showcasing an impressive prediction accuracy exceeding 93% when the model is trained with only 50% of the available data. These results highlight the potential of the FDPM model as a robust and versatile method for assessing wear levels in milling operations precisely, without disrupting ongoing production.

Automatic classification of smart sensor data for evaluating machine tool efficiency

The assessment of production plant efficiency is crucial for optimizing the operational performance of manufacturing systems. In traditional facilities, automated data collection is limited and information primarily relies on operators declarations, which are prone to inaccuracy. There is therefore a need for readily accessible digital alternatives. This paper introduces a cost-effective method for classifying the status of machine tools using smart sensors to monitor their primary doors with minimal integration, and a streamlined algorithm for efficient data processing. The innovative algorithm was conceived using data collected in over 3 months in a manufacturing plant comprising 50 diverse machine tools engaged in batch production for the automotive industry, and is based on non-dimensional thresholds, making it suitable for generic applications requiring classification of repetitive patterns. Also, a realistic simulator was developed to provide reliable data for algorithm accuracy evaluation. The classification performance was fully tested using synthetic data, showing very good accuracy. In addition, the performance of the algorithm was compared to basic machine learning approaches further proving the validity of the proposed method. Ultimately, the classification algorithm was employed to assess the Overall Equipment Effectiveness (OEE) of the real plant machines, which were closely aligned with the estimates provided by the enterprise management.

Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT

Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter. Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energy E = 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine. Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003 cm−1 and 0.007 cm−1 to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two. Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object’s shapes and materials do not affect the technique.