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Computerized numerical controls (CNCs) have been invented for the automation of industrial processes. They are used, when the process to be automated is required to be exact and fast with repeatable quality. Originally, the use of CNCs was primarily focused on milling and drilling processes. Today, CNCs are utilized in a wide range of industrial processes due to the growing importance of automation. However, the integration of process information or adaptation to the needs of these processes to achieve advanced manufacturing with CNCs is difficult: Industrial CNCs are rather closed real-time machining systems. Today, process integration is possible, when the interaction between the process and the machine is decoupled in view of the bandwidth of machine dynamics and process dynamics. There are interfaces that allow for process-motivated control loops that are realized on top of the machine control loop (e.g. chatter control). Then, machine-integrated real-time control is not the focus. Besides, it is often possible to change desired values inside the control loop (position, velocity, acceleration) on an axes basis. In this case, adaptation to the process can be realized in each computation cycle. However, process dynamics and machine axes dynamics are generally treated separately. The same holds for extra actuators (e.g., in the spindle) for position control. This paper has two goals. First, it wants to create an understanding for different levels of process-machine coupling. Second, the problem of direct coupling of process dynamics and machine dynamics is focused. Machining systems design propositions as well as some examples for the coupling of process and machine dynamics in the CNC are given. [ABSTRACT FROM AUTHOR]

Industrial robot electrochemical milling has an important application prospect in efficient and precise machining of hard-to-cut part materials due to its advantages of high flexibility and strong sensing ability and is now becoming a hot spot in the research of new electrochemical machining technology. However, the weak stiffness and low accuracy problems of industrial robots themselves can create errors in the normal clearance in electrochemical milling machining. The inter-electrode gap (IEG) directly affects the process performance of electrochemical machining in terms of machining accuracy and surface quality. To reduce the effect of robot trajectory error on the interpolar gap, real-time compensation control of the machining gap is required. In this paper, we propose an industrial robot electrochemical milling normal motion error correction control system with a gap control strategy that is based on the coupled multi-physical field theory for averaging and solving the machining conductivity. The machining current is measured in real time, the current fluctuation is converted into a normal error compensation amount, and the end-effector accomplishes the error amount compensation for the machining gap. The response performance of the gap control system is simulated and analyzed, the experimental platform is designed and constructed, and finally, to verify the effectiveness of the machining gap control servo system, the plane workpiece machining experiments are carried out, and the experimental results surface that the method can effectively control the gap fluctuation in robotic electrochemical milling within a small range, and ultimately improve the machining accuracy and surface quality of the parts, which proves that the method can improve the machining accuracy of robotic electrochemical milling. [ABSTRACT FROM AUTHOR]

Round-off error is inevitable in computer numerical control (CNC) systems because it is challenging to ensure that the interpolation time is an integer multiple of the interpolation period. This error can affect both machining precision and motion smoothness. To eliminate the round-off error and improve machining speed and precision, a novel round-off error elimination method is proposed that modifies the S-shaped acceleration/deceleration (ACC/DEC) feedrate scheme. Firstly, an adapted bidirectional scanning algorithm is applied to a jerk-limited S-shaped feedrate profile to improve machining efficiency while respecting feedrate constraints. Secondly, during the interpolation stage, the velocity round-off error is first introduced. Based on the novel properties of the modified S-shaped scheme, the displacement round-off error is then analyzed and addressed using different strategies, such as modifying the S-shaped profile with one-cycle rounding-up in a specified section or segment transition, without crossing the end point. Finally, a series of simulation and real machining experiments are conducted to compare the proposed method with existing round-off error compensation algorithms. The results show that the proposed method offers more flexible and effective round-off error compensation, with significant improvements in machining efficiency and the smoothness of the motion profile, including velocity, acceleration, and jerk. The real machining experiments, conducted in a CNC system with a 250-µs real-time control period and focused on 3D complex surface engraving, verified the practicality of the proposed method. [ABSTRACT FROM AUTHOR]

The engine main shaft forgings have high requirements for product consistency and reliability, which are difficult to be guaranteed by traditional manual free forging. On the basis of the already-in-place machinery, technical optimization was done in order to realize intelligent forging of the engine main shaft forgings. A rail trailer, holding robot, inspection robot, and expert system were installed as well as other hardware and software. Additionally, the procedure and specifications for robot intelligent free forging were revised. Based on the artificial neural network (ANN) model, an optimization model and a prediction model were created, and the process parameters can be controlled during forging. The verification result shows that the intelligent free forging production line can achieve real-time control of the shaft forging process, and obtain the forgings whose shape, size, microstructure, performance and consistency meet the requirements. With the help of this production line, free forging can be produced more quickly and efficiently, which is crucial for realizing the automation, digitization, and intelligence of shaft forging free forging. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of Industry 4.0, optimizing industrial processes like injection molding for enhanced efficiency and quality is paramount. This study endeavors to address this imperative of developing an automatic defect detection system, crucial for achieving intelligent process control and meeting evolving market demands while ensuring cost-effectiveness and sustainability. Utilizing transfer learning, this paper aims to explore and to verify the efficacy of three deep learning architectures—InceptionV2, ResNet50, and Inception-Resnet—unexplored in previous research on defect detection in the field of injection molding. The methodology adopted includes two essential steps to achieve the desired objective. The first step consists of training and testing the three RCNN architectures retained on a small data set after having determined the best values of three hyperparameters considered—learning rate, momentum, and number of iterations—allowing the obtaining of a better detection accuracy. The second step consists of improving the architecture of the best model obtained—here Inception v2—by using its last version v3, to consider and tune the values of additional hyperparameters—Solver and Bach size—and to use a training large dataset after adding other parts and proceeding with the different data augmentation techniques. Compared to the results obtained with the pre-trained models explored in previous studies such as MobileNet-SSD, VGG16, and YOLO, the results obtained during this study and particularly after the improvements undertaken are considered very satisfactory with an accuracy of 92%, a recall of 100%, and an F1 score of 90%. The results obtained during the test in the real world of production in the presence of external factors were very promising and revealed the importance of the luminosity factor as an influence on the precision and speed of detection. [ABSTRACT FROM AUTHOR]

The estimation and control of the substrate temperature in the Metal Organic Chemical Vapor Deposition (MOCVD) of superconductor tape manufacturing is investigated. In the advanced MOCVD (AMOCVD) for High-Temperature Superconductor (HTS) tape manufacturing developed at the University of Houston, Ohmic resistance heating is implemented to heat the rolling tape at a reference temperature. Control of the electric current through the tape is proposed to ensure substrate temperature uniformity that is essential for reducing nanoscale defect growth. The substrate temperature measurement is corrupted by the continuous deposition of precursor gas material on the crystal rods of the pyrometers in the reaction chamber challenging the real-time control process. The proposed approach for model-based substrate temperature control is based on the real-time estimation of the states of the substrate temperature model dynamics, and the sensor drift and drift rate, using an extended Kalman filter (EKF) combined with particle swarm optimization (PSO) to optimally tune the process and measurement noise covariance matrices. Experimental data from the AMOCVD facility are used to validate the model parameter and the sensor drift estimation process. The estimated temperature is used in a simulation study of a PSO-tuned LQR controller with adjustable gains designed to ensure uniform heating along the tape by controlling the electric current, and the results are compared with those of a PI controller. Precise temperature regulation in Ohmic heating is critical within a multitude of manufacturing and material processes, and the proposed methods constitute a systematic framework for model-based temperature estimation and control. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has surfaced as a pivotal component in the evolving field of intelligent manufacturing, offering an array of benefits compared to conventional production techniques. Nevertheless, the industry grapples with issues relating to manufacturing instability and inconsistent repeatability, making it challenging to meet desired microstructure and performance standards. The optimization of processing variables within specific equipment and parameter sets often necessitates expensive trial-and-error experiments, given the diversity and intricacy of AM process parameters. To mitigate these challenges, the digital twin (DT) technical concept has been implemented to bolster AM by offering real-time projection and mirroring of physical attributes for both the fabricated products and the AM machinery, thereby facilitating real-time feedback control to alleviate AM-induced defects and achieve optimal performance of the manufactured parts. DT techniques streamline process monitoring, performance prediction, anomaly detection, process parameter optimization, and production cost forecasting, thereby enhancing the entire AM process. Within the framework of Industry 4.0, DTs in AM have attracted considerable attention and experienced significant progress. Auxiliary techniques such as the Internet of Things (IoT), big data analysis, cloud manufacturing, and machine learning (ML) have substantially driven the expansion of DTs in AM. This review's contribution lies in the comprehensive analysis of how the digital twin (DT) technical concept has been introduced to enhance AM. This review examines existing literature on DTs in AM from six perspectives: background information, structural components, applications, directions for improvement, principal issues encountered, and potential research directions. It identifies current advancements, discusses applications across different domains, suggests areas for improvement, and outlines potential research directions. This review also identifies current advancements, discusses applications across different domains, suggests areas for improvement, and outlines potential research directions. These insights significantly contribute to the understanding and further development of DTs in AM within the context of Industry 4.0, offering a fresh perspective that aligns with the evolution of the intelligent manufacturing industry. [ABSTRACT FROM AUTHOR]

Due to its excellent filling capacity, good forming accuracy, and low die manufacturing costs, the sheet/tube flexible-die forming process has become an important supplement to traditional rigid die forming processes, widely used in the multi-variety, small and medium-sized batch manufacturing of complex thin-walled components in high-end manufacturing fields, such as aviation, aerospace, and automobiles. To match the deformation characteristics of different types of sheet/tube components, flexible mediums with different physical and mechanical properties were used as pressure-carrying dies and exhibited their respective advantages. Among them, what is remarkable is that magnetorheological materials, a type of intelligent material, have been introduced into flexible-die forming and develop into sheet/tube magnetorheological controllable flexible-die forming process. Real-time control of magnetorheological flexible-die performance can be achieved through an external adjustable magnetic field, improving the process control flexibility of the forming process and enhancing the forming performance and quality of parts. This article reviews the latest research progress in the sheet/tube magnetorheological controllable flexible-die forming process in recent years, and elaborates on the forming principles, control patterns, forming methods, and forming mechanisms. Finally, the future development direction of the magnetorheological controllable flexible-die pressure forming process for sheet/tube materials is prospected. [ABSTRACT FROM AUTHOR]

Injection moulding (IM) tools with embedded sensors can significantly improve the process efficiency and quality of the fabricated parts through real-time monitoring and control of key process parameters such as temperature, pressure and injection speed. However, traditional mould tool fabrication technologies do not enable the fabrication of complex internal geometries. Complex internal geometries are necessary for technical applications such as sensor embedding and conformal cooling which yield benefits for process control and improved cycle times. With traditional fabrication techniques, only simple bore-based sensor embedding or external sensor attachment is possible. Externally attached sensors may compromise the functionality of the injection mould tool, with limitations such as the acquired data not reflecting the processes inside the part. The design freedom of additive manufacturing (AM) enables the fabrication of complex internal geometries, making it an excellent candidate for fabricating injection mould tools with such internal geometries. Therefore, embedding sensors in a desired location for targeted monitoring of critical mould tool regions is easier to achieve with AM. This research paper focuses on embedding a wireless surface acoustic wave (SAW) temperature sensor into an injection mould tool that was additively manufactured from stainless steel 316L. The laser powder bed fusion (L-PBF) "stop-and-go" approach was applied to embed the wireless SAW sensor. After embedding, the sensor demonstrated full functionality by recording real-time temperature data, which can further enhance process control. In addition, the concept of novel print-in-place venting design, applying the same L-PBF stop-and-go approach, for vent embedding was successfully implemented, enabling the IM of defectless parts at faster injection rates, whereas cavities designed and tested without venting resulted in parts with burn marks. [ABSTRACT FROM AUTHOR]

In the direct laser deposition (DLD) process, it is common to employ constant processing parameters. The utilization of the constant parameters may lead to fluctuations in the dynamic evolution of the molten pool, primarily due to the intricate thermal effects involved, which will significantly impact the processing quality. To address this issue, this study proposed a closed-loop control approach that effectively modifies processing parameters in real time by targeting on the molten pool transient area. The type of the laser used in this study is a flat-top beam. Laser power, powder feeder rate, and scanning speed are the input variables of the study to control molten pool, considering the manufacturing system. The most suitable processing parameter to control the molten pool area was found to be the laser power by a set of orthogonal experiments, with a correlation coefficient of 0.706, and significance level of 0.002. Then, the dynamic response relationship between laser power and the molten pool area was mathematically characterized by a third-order transfer function model to simplify the complex physical model of the DLD process. Subsequently, a PID controller with a filtering coefficient and anti-windup compensation was chosen compared with the other controller. In the validation experiments, it was observed that the closed-loop processing group demonstrated improved stability in maintaining the molten pool transient area, with a notable decrease of 33.7% in variability compared to the open-loop processing group. As a result, the deposited layer of the closed-loop processing group exhibited a much more satisfying surface quality and heat affect zone than the open-loop group. This study established a third-order mathematical model of the dynamic molten pool and realized the optimization of deposition quality by controlling the molten pool transient area with an enhanced PID controller, providing a fundamental basis for improving the consistency of the direct laser deposition processing quality through the implementation of real-time feedback control of molten pool physics. [ABSTRACT FROM AUTHOR]

Reducing energy consumption while maintaining high contouring accuracy for industrial machinery is a highly desirable goal. Simple adaptive control (SAC) is a practical technique that can achieve high tracking accuracy and energy saving by adjusting control parameters in real time to ensure that the system responds as the reference model. Implementation of the SAC technique requires that the "almost strict property real (ASPR)" property is satisfied. The ASPR property guarantees the stability of the controlled system even when adaptive gains are high. This paper proposes simple adaptive contouring control (SACC) using jerk-based augmented output signal for the ASPR property. SACC is designed by following the tangent-contour control scheme while using the SAC technique to enhance the contouring accuracy. Jerk-based augmented output signal ensures that the ASPR property is met and allows the SACC to track accurately the desired contour at high frequency. The proposed contouring approach achieves lower contour error and energy consumption by about 45%, and 3% respectively, as compared to the most common parallel feedforward compensation approach. [ABSTRACT FROM AUTHOR]

Spark-Assisted Chemical Engraving (SACE) requires precise control over key factors to overcome gas film instability and achieve reproducible optimal resolution and machining speed. This paper presents a substantial advancement in the SACE micromanufacturing technique by introducing a composite algorithm. This algorithm leverages deep learning and time series classification for sequence-to-sequence intelligent classification. Two distinct architectures, the Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM), were subjected to evaluation. They were trained and optimized using Bayesian optimization, resulting in impressive accuracies of 97.18% for TCN and 96.44% for LSTM. The algorithm relies on TCN due to its superior performance for classifying current data points and subsequently computing derived parameters like gas film formation time, lifetime, mean discharge current and energy. Its versatility is demonstrated across various experimental conditions, showcasing its potential for rapid and accurate systematic studies. By highlighting the algorithm's applicability in real-time process control for SACE, this study establishes a foundation for future advancements in the field of glass micromanufacturing. [ABSTRACT FROM AUTHOR]

Laser directed energy deposition (L-DED) is an additive manufacturing (AM) technology that offers unique advantages for creating and repairing metallic parts. The quality of the final print is highly dependent on the consistency of the printing parameters, and as a result, the deposited bead geometry along the printing pathway. Inconsistencies, particularly in the bead width, can produce defects between adjacent beads, increasing the material susceptibility to failure. Therefore, it is crucial to monitor and predict the printing parameters in real time during the process. The objective of this study is to develop a machine learning model that accurately predicts the width of the deposited track in real time. To achieve this, a dataset with approximately 12,379 melt pool images was used to train the algorithm and predict the width of the deposited track. Each one of the deposited track was measured by a profilometer at 10 points along the pathway, and intermediate labels were predicted using a spline curve, making the model semi-supervised. To implement the machine learning model, pre-trained frozen networks based on convolutional neural networks (CNN) architectures VGG, ResNet, and DenseNet were employed, using transfer learning principles. These networks were integrated into a dense, fully connected network containing trainable parameters. The results demonstrate a good model fit, with a mean absolute error of 4.5% and a mean absolute error of 0.0358 mm. Moreover, the processing frequency of the best model, at 55 Hz, enables real-time control of the L-DED manufacturing process. It is important to highlight, however, that the VGG-based model shows a frequency of 250 Hz and similar results. Thus, accurately predicting the width of the deposited bead has the potential to significantly improve the quality of the final part by reducing overall defects, such as lack of fusion and porosity, whenever this data is used as input to a feedback control system. This is particularly valuable in industries where mechanical properties and fatigue life are critical, such as automotive, aerospace, and biomedical sectors. The models developed in this study offer a promising approach to effectively provide data to control the L-DED manufacturing process in real time, enabling the reliable production of high-quality components associated with reduced manufacturing costs. [ABSTRACT FROM AUTHOR]

With high efficiency and low energy loss, high-temperature superconductors (HTS) have demonstrated their profound applications in various fields, such as medical imaging, transportation, accelerators, microwave devices, and power systems. The high-field applications of HTS tapes have raised the demand for producing cost-effective tapes with long lengths in superconductor manufacturing. However, achieving the uniform and enhanced performance of a long HTS tape is challenging due to the unstable growth conditions in the manufacturing process. Although it is confirmed that the process parameters during the advanced metal organic chemical vapor deposition (A-MOCVD) process influence the uniformity of the produced HTS tapes, the high-dimensional process parameter signals and their complicated interactions make it difficult to develop an effective control policy. In this paper, we propose a local measure for the uniformity of HTS tapes to provide instant feedback for our control policy. Then, we model the manufacturing of HTS tapes as a Markov decision process (MDP) with continuous state and action spaces to assess the instant reward in real time in our feedback control model. As our MDP involves continuous and high-dimensional state and action spaces, a neural fitted Q-iteration (NFQ) algorithm is adopted to solve the MDP with artificial neural network (ANN) function approximation. The collinearity of process parameters can restrict our capability of adjusting the process parameters, which is addressed by the principal component analysis (PCA) in our method. The control policy adjusts the PCA of process parameters using the NFQ algorithm. Based on our case studies on real A-MOCVD dataset, the obtained control policy increases the average uniformity of tapes by 5.6% and performs especially well on sample HTS tapes with a low uniformity. [ABSTRACT FROM AUTHOR]

Consistent part geometry and minimal part dimensional variations are among the greatest challenges in deep drawing operations. In this regard, spring-back is a phenomenon created when the residual energy generated during this forming process causes some changes from desired part dimensions when released from the tooling system. There are many parameters affecting spring-back which can generally be classified as design parameters, material properties, and process variables. This paper reports on experimental results obtained from a test setup and their comparison with simulation studies of these processes. The results are part of a broader comprehensive analysis of deep drawing and study of possible real-time control of the process, to minimize final part dimensional variations. It reports on experimental and simulation results of the data and deals with identifying the relative importance of process parameters and their contribution to spring-back. In this regard, an experimental setup is designed and developed for utilization in this study. Design of experiment (DoE) is used as a tool to analyze the data, and those results are reported in this paper. [ABSTRACT FROM AUTHOR]

Digital twin (DT) technology has become an effective means of industrial digital transformation due to its ability to realize virtual-real mapping, data fusion, etc. In recent years, the application of DT technology to the assembly process of complex mechanical products in multiple fields can effectively overcome the drawbacks of the current assembly method, which is a new strategy for achieving high-precision and high-efficiency intelligent assembly. However, there are few reviews of DT's intelligent assembly technology and application for complex mechanical products. Based on the composition of the DT assembly system, the paper systematically summarizes the critical technologies of DT assembly, including DT model representation of the assembly objects, DT model construction of the assembly process, and DT data collection/management. The application of the DT assembly model based on virtual and real fusion in online monitoring, quality prediction, process planning, real-time control, etc. is comprehensively reviewed. Finally, the challenges faced by DT intelligent assembly of complex mechanical products are described, and future research directions are predicted. [ABSTRACT FROM AUTHOR]

Five-axis linear commands are blended as the local smoothed toolpaths by inserting clothoid and airthoid splines at corners in five-axis CNC machining. The contour error is the bottleneck to achieve the precise dimension of the machined parts, when following the local smoothed toolpaths. This paper presents a contour error estimation and control method for the five-axis smoothed toolpaths with airthoid splines, according to the geometric characteristics of the toolpaths. The tool-tip contour error is analytically calculated based on the expression of the smoothed toolpaths. Consequently, the tool-orientation contour error is obtained by synchronizing the tool-orientation contour point with the tool-tip item based on the motion time through the designed time scale coefficient, when the toolpaths are scheduled by the time-synchronization scheme. Furthermore, a contour error compensation strategy is constructed to adaptively determine the compensator gain. It can be qualified to maximally eliminate the contour errors and steadily hold the control stability of the feed drives, in spite of the modeling error between the nominal and actual control models. The simulation and experiment results show that the estimation algorithm has higher accuracy than traditional methods, and the compensation strategy effectively eliminates the five-axis contour error. [ABSTRACT FROM AUTHOR]

Aerospace, marine energy, and other high-end equipment have a higher and higher demand for performance indicators. The realization of high-performance manufacturing of equipment is particularly critical, and the assembly is the key link. The continuous expansion of application requirements and the continuous improvement of service performance have resulted in increased difficulty and complexity of assembly. Currently, it is a challenge to effectively realize the initial assembly performance of equipment and its long-term stability. The traditional way of assembly is driven by experience and geometric tolerances have certain randomness and uncertainty. These issues result in low assembly efficiency and accuracy and low one-time pass rate. In service, the service performance is inconsistent with the initial assembly performance, resulting in abnormal vibration or failure of equipment. Thus, performance-oriented digital twin (DT) assembly can overcome the shortcomings of traditional assembly under the high assembly performance (HAP) requirements of high-end equipment. The DT can potentially realize the HAP of high-end equipment using real-time mapping and control of physical entities in virtual space through virtual–real interaction, data fusion, iterative optimization, and other means. However, the performance-oriented DT assembly technology lacks systematic research. Thus, this paper expounds on the connotation of performance-oriented DT assembly technology based on the research status of high-end equipment assembly technology. This article presents a technical framework for performance-oriented DT assembly and discusses the realization approach and key technologies of performance-oriented DT assembly. Finally, its research direction and suggestions are given. The research will reveal the urgent engineering problems of performance-driven active and accurate assembly of high-end equipment and provide a reference for improving assembly performance, consistency, and efficiency. [ABSTRACT FROM AUTHOR]

Linear tool path with the inherent shortcoming of tangential and curvature discontinuities leads to poor machining efficiency and surface finish. A lot of local corner smoothing methods which inset a tiny spline curve at the corners have been proposed in literature. However, there exists potential overlap of the two adjacent spline curves while smoothing, and still no optimal solution to fully use the segment length of each corner while eliminating the overlaps. To optimally eliminate the overlaps and smooth the transition curve curvature in real time, a symmetrical non-uniform rational B-spline (NURBS) curve transition based on look ahead optimal method is proposed in this paper, by scanning a set of unconstrained pre-cornering transition curve information, analysing the overlap type and backward determining the optimal transition length with curvature extreme smoothness in consideration, thus fully use the linear segment length of each corner and fully increasing the feedrate at the corners. G 2 continuity, analytical expression of the curvature extreme, approximation error constraint, and real-time performance are considered simultaneously in this paper. Simulation and real machining experiments are made to compare with the existing symmetrical rounding algorithms, the results show that the proposed method has a significant curvature decrease of 17.64% and 4.44% improvement of efficiency, and the curvature distribution of the adjacent curves are balanced, thus improved the machining velocity smoothness. In addition, the real machining experiment results validate the proposed method can work well in 125 μs real-time control period CNC system. [ABSTRACT FROM AUTHOR]

Magnetic smart materials were employed as pressure-carrying medium in sheet metal flexible die forming process to achieve the real-time control of sheet formability in recent years. Yet, few studies involved the mechanism behind this phenomenon, which restricts the improvement of process design. Thus, elastic composites with magnetic-field-dependence (e.g., magnetorheological elastomer, MRE) were taken as an example to investigate their effects on sheet formability and the reason behind that in this work. MREs with different contents of hydroxy iron particles (φ) were fabricated, and their mechanical properties in the presence of external magnetic field with different flux density (B) were determined and introduced into finite element analysis (FEA). Bulge tests of Al1060 sheet employing MREs as the flexible dies were carried out under external magnetic field with the monitoring of digital image correlation (DIC) system. The results showed that, in the case of B = 1950 Gs, limit bulge height (H*) and critical strain (ε*) of deformed sheet respectively increased by 6.7% and 16.4% when adopting appropriate φ (i.e., φ = 20%), and, in the case of φ = 10%, they separately increased by 13.8% and 35.2% when adopting appropriate B (i.e., B = 1650 Gs). Compared with silicone rubber, forming pressure transmitted by MREs decreased under external magnetic field, which reduced equivalent stress of sheet metal and increased stress ratio of two in-plane stresses. By controlling MREs' mechanical properties, their pressure-transmitting properties could be adjusted. On the basis of stress state analysis, mechanism on the increased sheet formability when MREs served as the flexible dies was clarified. [ABSTRACT FROM AUTHOR]

Gas metal arc welding (GMAW) process is one of the most important in the industry, so different efforts have been made to anticipate the parameters to convert this process into a stable one capable of joining parts with minimum human interference. In this sense, controlling is essential for automated applications because properties such as the weld mechanical strength are defined by the metal composition, the microstructure, and the weld bead geometry. Nevertheless, performing this automatic control to guarantee quality characteristics similar to a human expert's in mechanized welding systems is still tricky. Nowadays, although various sensors have been used in the monitoring for control, it is still hard to detect effective options to real-time identify geometry characteristics in the formation process of the welds. Furthermore, even today, a process much more complex is to control more than one parameter simultaneously or control the weld penetration using a single sensor. Then, this research describes two intelligence systems for real-time control of the weld bead geometry in the GMAW process. The first is a passive vision system with sensor fusion that controls the width and height; the second is an active vision system that controls the penetration. Results indicate that the proposed methodology can be applied to simultaneously control external geometrical parameters without a predefined model of the welding process. In the case of penetration, a fuzzy controller and a neural network-based model help the system adapt to input parameter variations throughout the welding process, thus correcting instabilities under changing operating conditions. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing (AM) has the advantages of novel part morphology, new material property, and short production cycle, attracting significant attention in automobile, aerospace, and other fields. Nevertheless, the repeatability and stability of part quality are difficult to guarantee in the current metal AM process, hindering the development of metal AM technology. Vital process variables such as molten pool characteristics, spatter characteristics, surface morphology, and radiation temperature are directly related to the quality of parts. Monitoring the dynamic changes vital variables is critical. Image processing techniques such as image transformation, recognition, segmentation, and enhancement are introduced to obtain manufacturing process information through processing and analyzing rich image features. The monitoring system's processing results are usually collected in real time to help solve the repeatability and stability problems of the metal AM process. Presently, in situ and real-time monitoring methods are the most popular, and unfortunately, the literature lacks a comprehensive report. Thus, this paper thoroughly describes in situ and real-time process monitoring on the basis of image processing for metal AM. This paper reviews the in situ and real-time monitoring on the basis of traditional image processing to analyze monitoring objects, process classifications, and image processing carriers. Then, the advantages and disadvantages of in situ and real-time monitoring of metal AM based on artificial intelligence are discussed and compared. Finally, image processing algorithm generalization, quality, small samples, and image labeling problems are analyzed and discussed. A technical route for metal AM real-time feedback control is proposed by combining image processing with other technologies. [ABSTRACT FROM AUTHOR]

Plasma arc additive manufacturing (PAM) is an additive manufacturing technology that has been widely used in the past and has practical applications in many fields. The non-destructive testing methodology of PAM workpiece quality monitoring requires high level of accuracy and real-time capability. The characteristics of the melt pool and plasma arc are the keys to characterizing the dynamic manufacturing process during the powder feed PAM process, allowing for process prediction and real-time feedback control. This paper describes a new image recognition system that uses a fully convolutional network (FCN) to acquire melt pool and plasma arc morphologies simultaneously. Its image segmentation performance is compared with four conventional methods and three artificial intelligence methods. Results show that the FCN method can extract melt pool and plasma arc quickly and accurately, even in a complex manufacturing environment. The image segmentation-based FCN's accuracy is 95.1%, and the average processing time is only 84 ms, according to the results. The performance is far superior to the existing seven methods. The relationship between average captured areas (melt pool and plasma arc) and parameters of the plasma arc (current intensity and scanning speed) is then analyzed. Finally, the quality of products in terms of sample surface roughness is measured, and its relationship with the average areas of the melt pool and plasma arc is clarified. [ABSTRACT FROM AUTHOR]

The future of machining lies in the fully autonomous machine tool. New technologies must be developed that predict, sense and action intelligent decisions autonomously. Digital twins are one component on this journey and are already having significant impact in the manufacturing industries. Despite this, the implementation of machining Digital Twins has been slow due to the computational burden of simulating cutting forces online resulting in no commercially available Digital Twin that can automatically control the machining process in real time. Addressing this problem, this research presents a machining Digital Twin capable of real-time adaptive control of intelligent machining operations. The computational bottleneck of calculating cutter workpiece engagements online has been overcome using a novel method which combines a priori calculation with real-time tool centre point position data. For the first time, a novel online machine-induced residual stress control system is presented which integrates real-time model-based simulations with online feedback for closed loop residual stress control. Autonomous Digital Twin technologies presented also include chatter prediction and control and adaptive feed rate control. The proposed machining Digital Twin system has been implemented on a large-scale CNC machine tool designed for high-speed machining of aerostructure parts. Validation case studies have been conducted and are presented for each of the machining Digital Twin applications. [ABSTRACT FROM AUTHOR]

The potential of additive methods to integrate with machine learning (ML) models in hybrid manufacturing is significant in developing closed-loop processes for intelligent and adaptive control. This study presents a framework to defect mitigation that leverages a hybrid deep convolutional neural network (CNN) architecture with laser powder bed fusion additive systems. It connects on-site optical vision and sensor streams to identify process abnormalities and report real-time remedial actions. The hybrid CNN automatically connects reported anomalies to the physical actuation where quality issues might occur in a closed-loop fashion. This enables self-learning and autonomous corner-cutting while offering a pathway to new applications in digital manufacturing, such as surface finish optimisation and shape adaptation within a single build. In a case study, we demonstrate that our framework enables inspection and control at a level never seen before, as demonstrated by its precision in product inspection with an F-score of 93.8% and its ability to adjust control at a frequency exceeding 10 Hz. This work not only enables new learning paradigms for the development of real-time control systems for hybrid manufacturing, but also accelerates the process of AI-driven intelligent manufacturing by equipping industrial production systems with intelligence, flexibility and resilience. Our technique for tight coupling of process signals and actuation is a significant technical breakthrough in this critical pathway. [ABSTRACT FROM AUTHOR]

The influences of bypass current and wire feed speed are utilized to change the metal droplet transfer mode for bypass coupling wire-arcing additive manufacturing (BC-WAAW). To fully understand the influence of experimental factors on the wire melting process, this study establishes a mathematical model for the wire melting process, and several simulation experiments are carried out to prove the effectiveness of this mathematical model. The influence of changing the filler wire process on the stability of the wire melting process is simulated. A negative feedback control method with a single input and single output is proposed, which can control the stability of the wire melting process. The results show that the established mathematical model can well reflect the wire melting process in bypass coupling arcing additive manufacturing. The wire feed speed has a strong influence on the wire extension. The negative feedback control method can realize real-time control of filler wire process stability. The deposition process is more stable in the arcing additive manufacturing process, and the forming accuracy of a single multilayer deposition wall is further improved. [ABSTRACT FROM AUTHOR]

This study reports new development of a practical model-based controller (namely model predictive control, MPC) for shot peening, which is the first application to such a process for fully automated operation. In particular, the feedback MPC is developed based on a process model that links inlet air pressure of the machine to air pressure at a peening nozzle. In addition, a proxy model is developed to link measurement variable(s) to peening intensity as the intensity cannot be measured online for real-time feedback control. During process control, the process model is used to simulate future dynamics of the peening process to guide the controller for optimal control action, while the proxy model translates the setting intensity to air pressure reference set-point for real-time tracking. Both the controller and model development rely on the physical machine's constraints and capabilities. The pressure sensors and sensor locations are carefully selected to ensure controllability and observability. Single input/single output feedback MPC with future process pre-view capability is developed and integrated into the actual shot peening machine. The MPC has been demonstrated and validated using both in-silico and onsite controls for different scenarios. The obtained results show that the developed MPC is stable, robust, and accurate as it can automatically adjust inlet air pressure to attain the desired intensity. Finally, MPC can also help to reduce up to 25% of production cost by eliminating the cost, time, materials waste, and labor in performing experimental trials to build the saturation curve for actual operational guidance. [ABSTRACT FROM AUTHOR]

Nanotechnology is a significant research tool for biological and medical research with major advancements achieved from nanoparticle (Np) applications in biosensing and biotherapeutics. For laser ablation synthesis in solution (LASiS) to be chosen by researchers for Np colloid production, the process must effectively compete with chemical synthesis in terms of produced colloid quality and productivity while taking advantage of LASiS benefits in terms of its 'green-synthesis' and single-step functionalisation abilities. In this work, a newly developed integrated LASiS Np manufacturing system is presented including a Np flow reactor design, an at-line Np size monitoring via 180° dynamic light scattering, and a UV-Vis spectroscopy system used to estimate colloid concentration and stability. The experimental outcomes are discussed in terms of Np productivity and quality via these at-line measurements from the UV-Vis and DLS systems. The developed instrument was validated via off-line SiNps DLS, UV-Vis and morphology tests via TEM. Ultra-high quality and nanoparticle fabrication rate efficiency was achieved and is reported here. [ABSTRACT FROM AUTHOR]

Ultrasonic pulsating water jet for non-thermal and selective removal of acrylic bone cement is studied. Variation of acoustic chamber length is used for tuning of the ultrasonic system into the resonance regime to gain maximum power transmission. The study investigates the minimal technological conditions such as nozzle traverse speed and supply water pressure required to generate disintegration grooves in bone cement mantle. It also proposes the safe standoff distance range, which is essential for its potential application during the extraction of bone cement without compromising host bone. Palacos R+G bone cement was used for the experiments. Generated groove depths were measured using MicroProf FRT and analyzed using SPIP software. Depth values showed an increasing trend with an increase in acoustic chamber length, decrease in traverse speed, and increase in supply pressure values. From the entire experimental domain, a maximum depth of 615 μm was obtained at 22-mm chamber length, 0.5-mm/s traverse speed, 10-MPa pressure, and a standoff distance of 4 mm. Brittle fractured surface features like material chipping, micro-pits, cracks, and sheared material layers were observed in the SEM images. Disintegrated debris, diameter 21–37 μm, conceived from pit diameters can be used to design a suction unit. Real-time control of the disintegration process using accelerometer sensors was shown. The results support the idea of using pulsating water jet for bone cement removal in a single blind hole. Minimal technological parameters reduce reaction force of the hand tool, allowing bone cement removal without bone fracture or perforation. [ABSTRACT FROM AUTHOR]

In this research, a data-driven model is developed to monitor the seam during the laser beam welding under the influence of an external magnetic field (LBW-AMF). Firstly, a visible LBW-AMF system is built for tracking the laser melting pool and keyhole. Then, the features of the laser melting pool and keyhole are extracted with image processing techniques. The approach for an ensemble of different neural networks which includes radial basis function neural network, back-propagation neural network, and generalized regression neural network is proposed to establish the correlations of the characteristics of the laser melting pool and keyhole and the welding seam. Finally, LBW-AMF experimental results are obtained to validate the performance of the proposed data-driven model. Results illustrate that the developed model can provide a reliable result for monitoring the weld bead, which could give guidance for controlling the processing parameters in real time to improve the weld quality for practical LBW-AMF. [ABSTRACT FROM AUTHOR]

This paper presents a laser-based system for a local transformation of the magnetic properties in austenitic stainless steel. The process is applied to linear actuators with an integrated, in-axis position measurement where durability and high precision are needed. The developed system consists of a 400-W, single-mode fiber laser, the focusing optics, and a three-axis actuation system. An important feature is the optical feedback for the adaptive, high-speed (40 kHz) laser power regulation. The experimental results show that the optical feedback effectively compensates for the external disturbances, like an increased roughness and a gradual heat accumulation. The regulation decreases the dispersion of the magnetic signal's response by more than 100% and shortens the transient time at the beginning of the inscription process from 100 to 15 ms. [ABSTRACT FROM AUTHOR]

In this paper, a feedforward proportional-integral (PI) controller is developed to effectively control and tune the laser cladding melt pool geometry in real time. Width setpoint is included in the computer numerically controlled (CNC) programming, making possible its instantaneous change in relation to the position and time, as opposed to conventional controllers that do not have real-time information about these variables. The new concept of variable setpoint at different positions applied to laser cladding represents a great improvement in its use for changeable geometry applications such as blade fabrication. Several experiments are performed to characterize the behaviour of the system, revealing some key factors from monitoring system and image processing crucial for the controller. Laser power is selected as the input control variable, and the clad width is chosen as the output. The width of the melt pool is obtained based on measurements of CMOS camera images and an in-house image processing software algorithm. Closed-loop parameters are identified from the experimental data and Matlab simulations. The architecture of the controller consists on a conventional PI feedback loop and a feedforward module that shows low overshoot and fast response times. Instantaneous connections between laser, CNC, and PC systems allow for knowing the relationship among the exact position and real and setpoint melt pool values. The performance of the controller is verified in the fabrication of cladded parts with variable widths and in real time. [ABSTRACT FROM AUTHOR]

Thermal errors adversely affect the precision of a machine tool operation. Therefore, reduction of thermal errors is essential for improving of machining accuracy. In this paper, in order to realize real-time compensation, taking a multi-axis machine tool as an example and a method for robust modeling and predicting thermally induced positional error is proposed. First of all, the number of temperature measuring points required in thermal error's model was reduced based on the rough set theory, which greatly reduced variable searching and modeling time. Then through grey relation theory, systematic analysis of the similarity degree between thermal error and temperature data was carried out to select sensitive temperature measuring points, and the temperature variables in the thermal error's model were reduced from 24 to 7 after optimization, which eliminated the coupling problems. For reducing the influence of unpredictable noises, radial basis function (RBF) and back propagation (BP) neural network modeling methods were adopted to predict thermally induced positional errors, and in comparison, the prediction accuracy of RBF neural network was found superior to that of traditional BP neural network. Finally, some measured data were selected to verify the validity of the proposed method, and the results showed that prediction accuracy of the proposed thermally induced positional error model was reliable. [ABSTRACT FROM AUTHOR]

Effective approach of collecting, transmitting, and handling complete product manufacturing information during machining process is necessary for realizing high efficiency manufacturing. In this paper, the architecture and implementation of closed-loop machining system (CLMS) is discussed and the method of realizing machining process control is introduced. An integrated information flow is built based on object-oriented description method to transfer complete product manufacturing information in CLMS. The functional and informational model of CLMS is established by using integration definition method. Online and real-time machining process control is implemented on an open STEP-NC controller which is collecting and analyzing information of machining process condition and inspection results during machining process. The software kernel of CNC controller implemented on a computer platform integrates with machine tool, sensors, and probe for real-time data collecting and online inspection. Finally, test parts are machined and inspected to verify the proposed machining process control method and open STEP-NC controller. [ABSTRACT FROM AUTHOR]

A novel closed-loop process is demonstrated to control deposition microstructure during laser additive manufacturing (LAM) in real-time. An infrared imaging system is developed to monitor surface temperatures during the process as feedback signals. Cooling rates and melt pool temperatures are recorded in real-time to provide adequate information regarding thermal gradients, and thus control the deposition microstructure affected by cooling rates during LAM. Using correlations between the cooling rate, traveling speed, and the clad microstructure, a novel feedback PID controller is established to control the cooling rate. The controller is designed to maintain the cooling rate around a desired point by tuning the traveling speed. The performance of the controller is examined on several single-track and multi-track closed-loop claddings in order to achieve desired microstructures with specific properties. Results indicate that the closed-loop controller is capable of generating a consistent controlled microstructure during the LAM process in real-time. [ABSTRACT FROM AUTHOR]

The wire electrical discharge machining (WEDM) process has inherent instability due to its high complexity and stochastic characteristics. For more stable and efficient machining, the process must be actively controlled in real time while precisely determining the state of the instantaneous machining process. Feedback information when used in conjunction with an unstable discharge pulse ratio and instantaneous discharge energy can be applied to this micro-control system. This study presents the design and implementation of a system that fulfills these demands. Experimental results demonstrate that the proposed system is capable of simultaneously enhancing the machining stability, machining efficiency, and machining performance. For fine machining, the surface quality was enhanced by approximately 10 % without a loss of machining efficiency. Overall, both the feedrate and the surface roughness could be improved concurrently by more than 5 %. [ABSTRACT FROM AUTHOR]

Micro electrical discharge machining (EDM) has been widely applied for manufacturing precise and complex 3D micro shapes. However, in 3D micro EDM machining, electrode wear, caused by discharged pulses, is a serious problem, which significantly affects machining accuracy. In this work, a real-time, micro EDM monitoring system was developed to classify and count the number of discharge pulses using the uniform wear method. Based on the normal number of discharges and tool wear, an empirical relationship between them was established. From this relationship, this study also proposed a new electrode wear compensation method, called as NNU method, based on the number of normal discharges and uniform wear method (UWM). Experimental results indicated that the grooves or 3D shapes could be fabricated with high accuracy. [ABSTRACT FROM AUTHOR]

The capability to generate complex geometry features at tight tolerances and fine surface roughness is a key element in implementation of Creep Feed grinding process in specialist applications such as the aerospace manufacturing environment. Based on the analysis of 3D cutting forces, this paper proposes a novel method of predicting the profile deviations of tight geometrical features generated using Creep Feed grinding. In this application, there are several grinding passes made at varying depths providing an incremental geometrical change with the last cut generating the final complex feature. With repeatable results from coordinate measurements, both the radial and tangential forces can be gauged versus the accuracy of the ground features. The results of the tangential force were found more sensitive to the deviation of actual cut depth from the theoretical one. However, to make a more robust prediction on the profile deviation, its values were considered as a function of both force components. In addition, the power signals were obtained as these signals are also proportional to force and deviation measurements. Genetic programming (GP), an evolutionary programming technique, has been used to compute the prediction rules of part profile deviations based on the extracted radial and tangential force and correlated with the initial 'gauging' methodology. It was found that using this technique, complex rules can be achieved and used online to dynamically control the geometrical accuracy of the ground features. The GP complex rules are based on the correlation between the measured forces and recorded deviation of the theoretical profile. The mathematical rules are generated from Darwinian evolutionary strategy which provides the mapping between different output classes. GP works from crossover recombination of different rules, and the best individual is evaluated in terms of the given best fitness value so far which closes on an optimal solution. Once the best rule has been generated, this can be further used independently or in combination with other close-to-best rules to control the evolution of output measures of machining processes. The best GP terminal sets will be realised in rule-based embedded coded systems which will finally be implemented into a real-time Simulink simulation. This realisation gives a view of how such a control regime can be utilised within an industrial capacity. Neural networks were also used for GP rule verification. [ABSTRACT FROM AUTHOR]

This paper presents a new technology of real-time arc length monitoring and sag-depression prediction through arc sound signal, which is essential to realize the welding penetration control during gas tungsten arc welding of arc length. A set of automatic measurement and control system have been proposed to achieve real-time arc length control via audio sensing system. After preprocessing of arc sound signal, the piecewise linear models of arc sound signal were established under two different arc length variation 3-4 and 4-5-6 mm, analyzing the prediction errors of linear model, which were proved to be good enough for online monitoring of arc length in pulse GTAW. Based on the linear relationship between arc sound and arc length, the linear fitting model was implemented on predicting the surface height of weld pool. A segmented self-adaptive PID controller was proposed to achieve the monitoring and controlling of arc length;, the confirmatory experiments have been designed to test the control effect of arc-length monitoring based on arc sound signal. [ABSTRACT FROM AUTHOR]

In micro-electrical discharge machining (micro-EDM) process, the property of high-frequency discharge and complex interference causes the discharge state to vary fast, and conventional control methods of micro-EDM have the hysteresis characteristic, which make it very difficult to conduct efficient and stable control. Firstly, in this paper, based on calamities grey prediction theory, a new method for discharge state prediction is proposed, where the voltage and current signal are replaced with the output velocity of conventional control system as the direct predictive target, and the negative velocity (electrode pullback) is regarded as a calamity value. Owing to avoidance of processing voltage and current signals with nonstationarity, nonlinearity, and internal coupling characteristics, the proposed discharge state prediction method, which predicts that whether the future processing cycles are in short-circuit discharge state, exhibits good real-time performance. Thereafter, by virtue of multi-thread technology, the proposed discharge state-predictive method is added to a conventional control system, and a discharge state real-time predictive control system of micro-EDM is established in this paper. Finally, experiments on machining micro-holes and micro-grooves are conducted using the proposed predictive control method and fuzzy control method. Experiment results indicate that the proposed predictive control method has stable and good real-time performance, and it can effectively reduce electrode pullbacks and improve the processing efficiency and processing quality in both micro-hole and micro-groove EDM. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide an integrated real-time control system for resistance spot welding (RSW), which is capable of producing welds with predetermined nugget diameters. Nugget diameter is a commonly used criterion for estimating the weld quality; thus, the system can be used for online quality control of RSW. The proposed system consists of two parts: a constant current controller and an online nugget diameter estimator. The constant current controller is used to guarantee the consistency of the nugget formation and growth during the welding process, while the online nugget diameter estimator can be used as a sensor for online estimation of the nugget diameter. The proposed online nugget diameter estimator is a mathematical function of the heat energy absorbed by the weld, which is taken into account only after the time when the first melting point of a weld appears. This time can be obtained by means of the dynamic resistance curve with a high resolution. A mathematical model of the online nugget diameter estimator is proposed and realized based on the experiment results and mathematical analysis. The welding process would be terminated when the difference between the actual nugget diameter and the predetermined desired value is within a given tolerance. According to a series of experiments, the welds used in the experiments had nugget diameters, which were well within the limits and the errors were within the given tolerance. [ABSTRACT FROM AUTHOR]

Industrialization of parallel robots requires the development of control laws that exploit their capabilities in high performance tasks. Although research in this area is increasing, theoretical results need to be experimentally tested in order to evaluate the proposed solutions. In this paper, implementation requirements of control laws in parallel robotics are discussed, and a design for implementing a real-time control and validation environment is proposed. This design provides three main advantages: (1) is focused on reducing the implementation and validation effort of control laws on prototypes, (2) is defined as an open and modular design, which allows to implement new functionalities, and (3) proposes a hardware abstraction layer which simplifies the implementation of different control laws for a given prototype. The proposed design is validated by its implementation in a five revolute joints parallel robot prototype. [ABSTRACT FROM AUTHOR]

To survive in the highly competitive global economy, manufacturing systems must be able to adapt to new circumstances. An important prerequisite for adaptation is the ability to learn, a process based on knowledge discovery and growth. The aim of this research is to uncover knowledge by examining a large volume of real-time manufacturing data collected during manufacturing operations and to use the insights gained to support decision-making and adaptive process control. The paper presents the concept of a self-learning autonomous work system. This concept introduces a learning loop into a manufacturing system composed of data acquisition, data mining (DM), and knowledge-building models. Two methods for DM are applied. A descriptive DM method enables discovery of patterns in data that may contribute to a better understanding of the manufacturing processes. A predictive process provides knowledge in the form of rules, which can then be used for enhanced decision-making. To illustrate the utility of the knowledge models, the concept of adaptive process control is introduced and implemented in a high pressure die-casting domain. A case study based on industrial data collected during die-casting operations provides a demonstration of the concept. [ABSTRACT FROM AUTHOR]

Modern manufacturing industry requires computer numerical control (CNC) systems to be more open, extendable, and networked. Real-time Ethernet is introduced and its use in CNC is discussed in this paper. Compared to traditional fieldbus, a real-time Ethernet which modifies the Ethernet data link layer to achieve fast delivery time and high synchronization accuracy is discussed. The method of synchronizing distributing clocks to reduce cumulative jitter of transmission for a synchronization frame is proposed. Some special considerations of this real-time Ethernet are discussed, e.g., incorporation with Windows NT and making feedback returned faster. A CNC system based on such a real-time Ethernet and Windows NT has been developed and presented. The system involves the service-oriented Windows Communication Foundation as an up-layer interface to the Internet, and a real-time Ethernet for connecting to hardware components of a machine tool. The non-real-time aspect of Windows NT is accounted for; for example, a driver rewriting the interrupt descriptor table is introduced and packets to be sent are buffered. Design and implementation of this system, such as main classes and services in the NC kernel, main modules (e.g., data link layer) in salve nodes, are also discussed. At last, experimental results are presented to illustrate the Windows NT's performance and the real-time accuracy of the synchronization method. [ABSTRACT FROM AUTHOR]

Non-uniform rational B-spline (NURBS) interpolator has been widely used in modern manufacturing systems to machine arbitrary geometries with great relief of the data flow bottleneck and feedrate fluctuation. However, in practice, real-time feedrate does not always meet the computer numerical control (CNC) command exactly subject to the system dynamics. To solve this problem, we present a real-time NURBS interpolator with feedrate optimization for CNC machining tools in this work. The parametric curve is first approximated with the Adams-Bashforth method which provides uniform feedrate in each sampling period in the interpolation process. And then, a feedback scheme is introduced to adjust the feedrate in real time so as to guarantee a specified deviation between the measured and the desired feedrate. The convergence condition for this closed-loop algorithm is presented and analyzed. Simulation and real experiments on an X- Y table are employed to verify its feasibility. And the comparisons with traditional interpolators based on Taylor's expansion are also provided to demonstrate its improvement in accuracy. [ABSTRACT FROM AUTHOR]

The Supervisory Control Theory (SCT) introduced by Ramadge-Wonham (RW) is a general theory to design supervisors (controllers) for discrete event systems. Although over the two decades SCT has received wide attention in academia, industrial applications are very few, due to the fact that there is a discrepancy between the abstract RW supervisor and its physical implementation. In this paper, an easy to use, general and practical technique is proposed for the PLC-based implementation of RW supervisors with time delay functions. The applicability of the proposed method is demonstrated by means of a PLC-based real-time control of an experimental manufacturing system. [ABSTRACT FROM AUTHOR]

Increasing automation and complexity of today's production systems affect the constraints of real-world shop-floor control and scheduling problems. To deal with these constraints, either the existing modeling and analysis techniques were improved or new scheduling approaches were developed. This study attempts to propose an object-oriented approach for modeling and analysis of shop-floor scheduling problem of flexible manufacturing systems (FMSs) using high-level Petri Nets (PNs). The graphical nature and mathematical foundation has made PN-based methods appealing in real-time scheduling and control of FMSs. In the proposed approach, we firstly construct an object-modeling diagram of an FMS and develop a heuristic rule-base to solve the resource contention problem then formulate the dynamic behavior of the system by high-level PNs. The modeling methodology is illustrated by an FMS, and the proposed rule-based system is compared with several dispatching rules with respect to part flow time and tardiness-related performance measures. [ABSTRACT FROM AUTHOR]

Flank wear of an alumina-based ceramic cutting tool was determined in hard turning two workpieces (AISI 4340 and 52100 hardened steels) at three cutting speeds (142, 181, and 264 m/min) to devise a real-time monitoring system. Results of the six turning tests were assessed using Kruskal-Wallis test, regression models, and linear trend analysis. Multiple non-linear regression models that explained variation in flank wear as a function of time (second) had a range of $$ R_{\rm{adj}}^2 $$ values of 27.7% for the test 4340-142 to 95% for the test 52100-181. Linear trend models revealed that the highest flank wear rate of the ceramic cutting tool belonged to the test 52100-181. Interaction effect of the three cutting speeds and the two workpiece types was determined to account for 82.2% of variation in flank wear ( P < 0.001). The real-time monitoring system designed in this study appeared to be promising in terms of determining and quantifying flank wear behavior of the ceramic cutting tool and optimal hard turning conditions. [ABSTRACT FROM AUTHOR]

Condition-based maintenance (CBM) is a decision-making strategy based on real-time diagnosis of impending failures and prognosis of future equipment health. It is a proactive process that requires the development of a predictive model that can trigger the alarm for corresponding maintenance. Prognostic methodologies for CBM have only recently been introduced into the technical literature and become such a focus in the field of maintenance research and development. There are many research and development on a variety of technologies and algorithms that can be regarded as the steps toward prognostic maintenance. They are needed in order to support decision making and manage operational reliability. In this paper, recent literature that focuses on the machine prognostics has been reviewed. Generally, prognostic models can be classified into four categories: physical model, knowledge-based model, data-driven model, and combination model. Various techniques and algorithms have been developed depending on what models they usually adopt. Based on the review of some typical approaches and new introduced methods, advantages and disadvantages of these methodologies are discussed. From the literature review, some increasing trends appeared in the research field of machine prognostics are summarized. Furthermore, the future research directions have been explored. [ABSTRACT FROM AUTHOR]

Real time simulation and visualization are important for robot programmers to verify and optimize the path planning for the robotic belt grinding process. A new free-form surface representation based on discrete surfel element is developed to facilitate the system implementation, which exploits the advantage of the new development of point-based rendering technology in computer graphics. A local process model is integrated to calculate the material removal rate by considering the local geometry information and non-uniform force distribution. The final surface grinding error is easy to be assessed and visualized for quality evaluation. The experiments show that the simulation error is below 15%, even for a non-uniform contact under stable cutting conditions. [ABSTRACT FROM AUTHOR]