Modeling Method for Flexible Energy Behaviors in CNC Machining Systems

CNC machining systems are inevitably confronted with frequent changes in energy behaviors because they are widely used to perform various machining tasks. It is a challenge to understand and analyze the flexible energy behaviors in CNC machining systems. A method to model flexible energy behaviors in CNC machining systems based on hierarchical objected-oriented Petri net(HOONet) is proposed. The structure of the HOONet is constructed of a high-level model and detail models. The former is used to model operational states for CNC machining systems, and the latter is used to analyze the component models for operational states. The machining parameters having great impacts on energy behaviors in CNC machining systems are declared with the data dictionary in HOONet models. A case study based on a CNC lathe is presented to demonstrate the proposed modeling method. The results show that it is effective for modeling flexible energy behaviors and providing a fine-grained description to quantitatively analyze the energy consumption of CNC machining systems.

Development of an In-Situ Laser Machining System Using a Three-Dimensional Galvanometer Scanner

In this study, a three-dimensional (3D) in-situ laser machining system integrating laser measurement and machining was built using a 3D galvanometer scanner equipped with a side-axis industrial camera. A line structured light measurement model based on a galvanometer scanner was proposed to obtain the 3D information of the workpiece. A height calibration method was proposed to further ensure measurement accuracy, so as to achieve accurate laser focusing. In-situ machining software was developed to realize time-saving and labor-saving 3D laser processing. The feasibility and practicability of this in-situ laser machining system were verified using specific cases. In comparison with the conventional line structured light measurement method, the proposed methods do not require light plane calibration, and do not need additional motion axes for 3D reconstruction;thus they provide technical and cost advantages. The insitu laser machining system realizes a simple operation process by integrating measurement and machining,which greatly reduces labor and time costs.

The Development of a Distributed Surface Machining System

This paper focuses on the development of a distributed surface machining system. Traditional manufacturing engineering activity analysis has been conducted in developing the proposed system structure. The advantages of a distributed system structure such as easy to manage,high expandability and flexibility will enhance the efficiency of an integral system operation,and achieve the goal of networked manufacture. The IDEF0 was used to describe each stage of the traditional surface machining activities,and then UML (Unified Modeling Language) technology was adopted to verify the feasibility and accuracy of the established integrated system. The developed distributed system structure and sub-functional modules (CAD/CAM/CAPP) have been implemented based on the proposed systematic approach; and a freeform surface has been used as an example for verification. The proposed approach has been successfully implemented and could be adopted to assist engineers in integrating machining activities that are located in dispersed places; and various domains experts also can exchange their expertise among themselves. Thus,the development time of a product machining processes can be shortened and so is its enhancement on the competitive advantages. In addition,this distributed system has also integrated multi-functional ontology and service agent to facilitate the selection and reconfiguration in manufacturing customization. The proposed system has presented the feasibility in incorporating the agent-based technology in a distributed freeform surface machining environment. Service agents communicate via pre-defined performatives underlying knowledge query and manipulation language (KQML) for the surface machining capability. The developed system has then successfully demonstrated the feasibility in implementing the agent-based technology into a distributed surface machining system.

Investigations and practices on green manufacturing in machining systems

A machining system is a typical manufacturing system. A green manufacturing function framework of machining systems is structured to describe the traits of input, output and control elements in the system. Based on the function framework, the green manufacturing problem framework of machining systems is presented. The green manufacturing problems in machining systems are classified into three classes and related series of subclass problems. The three classes of problems in the green manufacturing problem framework are the problems of the minimization of resource consumption, the minimization of environmental discharge, and the synthesized minimization of both of them. A series of investigations and practices on green manufacturing in machining system, performed by the authors for quite a long period, are introduced in brief, such as the optimizing system for raw material cutting, the matching system for energy-saving in machining, the design of highly efficient dry hobbing machine tools, the multi-objective decision-making model for green manufacturing in machining systems, and the decision-making supporting system for green manufacturing in machining processes.

Recent progress on artificial intelligence-enhanced multimodal sensors integrated devices and systems

Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.

Machine learning prediction of hepatic encephalopathy for long-term survival after transjugular intrahepatic portosystemic shunt in acute variceal bleeding

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)is an effective intervention for managing complications of portal hypertension,particularly acute variceal bleeding(AVB).While effective in reducing portal pressure and preventing rebleeding,TIPS is associated with a considerable risk of overt hepatic encephalopathy(OHE),a complication that significantly elevates mortality rates.AIM To develop a machine learning(ML)model to predict OHE occurrence post-TIPS in patients with AVB using a 5-year dataset.METHODS This retrospective single-center study included 218 patients with AVB who underwent TIPS.The dataset was divided into training(70%)and testing(30%)sets.Critical features were identified using embedded methods and recursive feature elimination.Three ML algorithms-random forest,extreme gradient boosting,and logistic regression-were validated via 10-fold cross-validation.SHapley Additive exPlanations analysis was employed to interpret the model’s predictions.Survival analysis was conducted using Kaplan-Meier curves and stepwise Cox regression analysis to compare overall survival(OS)between patients with and without OHE.RESULTS The median OS of the study cohort was 47.83±22.95 months.Among the models evaluated,logistic regression demonstrated the highest performance with an area under the curve(AUC)of 0.825.Key predictors identified were Child-Pugh score,age,and portal vein thrombosis.Kaplan-Meier analysis revealed that patients without OHE had a significantly longer OS(P=0.005).The 5-year survival rate was 78.4%,with an OHE incidence of 15.1%.Both actual OHE status and predicted OHE value were significant predictors in each Cox model,with model-predicted OHE achieving an AUC of 88.1 in survival prediction.CONCLUSION The ML model accurately predicts post-TIPS OHE and outperforms traditional models,supporting its use in improving outcomes in patients with AVB.

INTERNET-BASED MACHINING PARAMETER OPTIMIZATION AND MANAGEMENT SYSTEM FOR HIGH-SPEED MACHINING

The theory and its method of machining parameter optimization for high-speed machining are studied. The machining data collected from workshops, labs and references are analyzed. An optimization method based on the genetic algorithm (GA) is investigated. Its calculation speed is faster than that of traditional optimization methods, and it is suitable for the machining parameter optimization in the automatic manufacturing system. Based on the theoretical studies, a system of machining parameter management and optimization is developed. The system can improve productivity of the high-speed machining centers.

PRECISION OF HSK TOOLING SYSTEM IN HIGH SPEED MACHINING

Based on the theory of elastic mechanics and material mechanics, the orientation precision of the hohl schaft kegel（HSK） tooling system in static and dynamic states is theoretically and experimentally studied. The relation between the clamping force and the shank taper is obtained. And a proper clamping force is found to be essential to assure the axial and radial orientation precisions of the HSK tooling system in high speed machining （HSM）. Analytical results show that the reason why the HSK tooling system can keep high precision at the high rotational speed is that the actual axial clamping force keeps the two surfaces of the shank and the spindle in contact all the time.

IMPROVEMENT OF MACHINING ACCURACY OF CANTILEVER BORING BAR SYSTEM USING PIEZOELECTRIC ACTUATOR

This paper is concerned with the work involved in improving the machining accuracy of a cantilever boring bar by on line compensation with a piezoelectric actuator. A boring bar is made into lever structure, with strain gauges attached to the bar for measuring its force induced deflections. The piezoelectric actuator is employed to compensate the deflections of the boring bar for accuracy improvement. Due to the mechanical advantage of the structure, the boring bar can be made into smaller size. The diameter of the bar implemented is 10 mm and the ratio of length to diameter (L/D) is larger than 8. It is found that the machining accuracy is improved considerably by using the piezoelectric actuator compensation system.

Adaptive Fuzzy Control System of Servomechanism for Electro-Discharge Machining Combined with Ultrasonic Vibration

For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM combined with ultrasonic vibration is studied, the servomechanism of which is composed of the stepping motor comprising variable steps and the inductive synchronizer. The fuzzy control technology is used to realize the control of the frequency and the step of the servomechanism. The adaptive fuzzy controller has three inputs and two outputs, which can well meet the actual control requirements. The constitution of the fuzzy control regulation for the step frequency is the key to the design of the whole fuzzy control system of the servomechanism. The step frequency is mainly determined by the position error and the change rate of the position error. When the value of the position error is high or medium, the controlled parameters are selected to eliminate the error; when the position error is lower, the controlled parameters are selected to avoid the over-orientation and thus keep the stability of the system. According to these, a fuzzy control table is established in advanced, which is used to express the relations between the fuzzy input parameters and the fuzzy output parameters. The input parameters and the output parameters are all expressed by the level-values in fuzzy field. Therefore, the output parameters used for control can be obtained for the fuzzy control table according to the detected actual input parameters, by which the EDM combined with ultrasonic vibration is improved and the machining efficiency is increased. In addition, a stimulation program is designed by means of Microsoft Visual Basic

SOFTWARE-CONTROLLED SYSTEM OF ULTRA-PRECISION MACHINING AXISYMMETRIC ASPHERIC MIRROR

In order to improve machining accuracy and efficiency, a software-controlled system of ultra-precision machining for axisymmetric aspheric mirror, using techniques of error compensation, remote transmission and modularization, is designed based on industrial PC, Windows 2000 work platform and Visual Basic 6.0. By experiments, this system realizes functions of ultra-precision machining, machining error compensation, remote data transmission and automatic data transformation among first machining, compensation machining and accuracy measurement. The actual application shows that error compensation improves machining accuracy, remote transmission improves machining efficiency while modularization avoids repeated work and improves design efficiency. Therefore, the system has met ultra-precision machining need for aspheric mirror.

A Web-based machining process monitoring system for E-manufacturing implementation

Recently, with the rapid growth of information technology, many studies have been performed to implement Web-based manufacturing system. Such technologies are expected to meet the need of many manufacturing industries who want to adopt E-manufacturing system for the construction of globalization, agility, and digitalization to cope with the rapid changing market requirements. In this research, a real-time Web-based machine tool and machining process monitoring system is developed as the first step for implementing E-manufacturing system. In this system, the current variations of the main spindle and feeding motors are measured using hall sensors. And the relationship between the cutting force and the spindle motor RMS (Root Mean Square) current at various spindle rotational speeds is obtained. Thermocouples are used to measure temperature variations of important heat sources of a machine tool. Also, a rule-based expert system is applied in order to decide the machining process and machine tool are in normal conditions. Finally, the effectiveness of the developed system is verified through a series of experiments.

A Study on Comprehensive Modeling for Errors in on-line Measuring System of Machining Center

Based on the theory of multi-body system (MBS), bine’s and huston’s methods are applied to an on-line measuring system of machining center in this paper. Through the study on modeling technique, the comprehensive model for errors calculation in an on-line measuring System of machining center have been built for the first time. Using this model, the errors can be compensated by soft.ware and the measuring accuracy can be enhanced without any more inveSt. This model can be used in all kinds of machining center.

Systematic analysis of error sources during ultra-precision machining

During ultra-precision machining, machining accuracy is determined by many factors and interaction of these factors. Error sources are systematically analyzed for ultra-precision machine tools, and the influencing degree of each factor is presented to provide orientation for error reduction and error compensation.

Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis

The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

Development of Virtual Simulation System for Remote Collaborative Surface Machining

Most researches about virtual machine tool are emphasized on simulations of machine motion and machining process for single machine. In this paper,a virtual simulation system for remote collaborative surface machining is developed. The motion command of machine tool is generated by an interpolator,which can derive synchronized motion commands according to feedrate. Thus,the system can estimate the machining time. For universal assembly of five-axis virtual machine tool,it is based on the D-H notation representation and machining constraints consideration. The remote collaborative virtual manufacturing system based on the CORBA technology is proposed in this paper. It demonstrated that the developed virtual machine tool can be used to verify and simulate the machining process for the collaboration of the surface design and manufacturing team.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

Integrated CAD/CAPP/CAM system for electric discharge machining

An integrated CAD/CAPP/CAM system modeling for Electric Discharge Machining (EDM) is constructed on the basis of an integrated engineering database. EDM feature objects are developed using the object oriented database provided by AutoCAD R14, and EDM feature modeling is realized in AutoCAD environments.