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.

Adaptive control of machining process based on extended entropy square error and wavelet neural network

Combining information entropy and wavelet analysis with neural network,an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error(EESE)and wavelet neural network(WNN).Extended entropy square error function is defined and its availability is proved theoretically.Replacing the mean square error criterion of BP algorithm with the EESE criterion,the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter,translating parameter of the wavelet and neural network weights.Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network.The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions,thus improving the machining efficiency and protecting the tool.

Improved Quality Prediction Model for Multistage Machining Process Based on Geometric Constraint Equation

Product variation reduction is critical to improve process efficiency and product quality, especially for multistage machining process（MMP）. However, due to the variation accumulation and propagation, it becomes quite difficult to predict and reduce product variation for MMP. While the method of statistical process control can be used to control product quality, it is used mainly to monitor the process change rather than to analyze the cause of product variation. In this paper, based on a differential description of the contact kinematics of locators and part surfaces, and the geometric constraints equation defined by the locating scheme, an improved analytical variation propagation model for MMP is presented. In which the influence of both locator position and machining error on part quality is considered while, in traditional model, it usually focuses on datum error and fixture error. Coordinate transformation theory is used to reflect the generation and transmission laws of error in the establishment of the model. The concept of deviation matrix is heavily applied to establish an explicit mapping between the geometric deviation of part and the process error sources. In each machining stage, the part deviation is formulized as three separated components corresponding to three different kinds of error sources, which can be further applied to fault identification and design optimization for complicated machining process. An example part for MMP is given out to validate the effectiveness of the methodology. The experiment results show that the model prediction and the actual measurement match well. This paper provides a method to predict part deviation under the influence of fixture error, datum error and machining error, and it enriches the way of quality prediction for MMP.

The General Machining Process Simulator GMPS

Automated manufacturing system is characterized by flexibility. It aims at producing a variety of products with virtually no time loses to change over from one part to the next. In this paper, the Machining Process Simulator GMPS is introduced, which can be used as a supported environment for machining process. It can be executed off-line or on-line in manufacturing systems in order to predict the collisions of tool with machined workpieces, fixtures or pallets. First, the functional model of GMPS is described, then adopted critical techniques in the simulator are introduced. Finally, an application of GMPS in CIMS ERC of China is presented.

Multiconditional machining process quality prediction using deep transfer learning network

The quality prediction of machining processes is essential for maintaining process stability and improving component quality. The prediction accuracy of conventional methods relies on a significant amount of process signals under the same operating conditions. However, obtaining sufficient data during the machining process is difficult under most operating conditions, and conventional prediction methods require a certain amount of training data. Herein, a new multiconditional machining quality prediction model based on a deep transfer learning network is proposed. A process quality prediction model is built under multiple operating conditions. A deep convolutional neural network (CNN) is used to investigate the connections between multidimensional process signals and quality under source operating conditions. Three strategies, namely structure transfer, parameter transfer, and weight transfer, are used to transfer the trained CNN network to the target operating conditions. The machining quality prediction model predicts the machining quality of the target operating conditions using limited data. A multiconditional forging process is designed to validate the effectiveness of the proposed method. Compared with other data-driven methods, the proposed deep transfer learning network offers enhanced performance in terms of prediction accuracy under different conditions.

Machining fixture for adaptive CNC machining process of near-net-shaped jet engine blade

Low stiffness and positioning problems are difficulties and challenges in the precise machining of near-net-shaped blades.This paper aims to achieve high accuracy in manufacturing by fixture-and deformation-control in the adaptive CNC machining process.Adaptive CNC machining technology is first analyzed,and new fixture-evaluation criteria and methods to evaluate the adaptive CNC machining process fixture design are built.Second,a machining fixture is designed and manufactured after analyzing its positioning scheme,clamping scheme,materials(PEEK-GF30),and structure characteristics.Finally,the designed fixture is analyzed by FEA and experimentally verified by a cutting experiment.The results show that the deformation of the blade is an overall rigid-body displacement,the main deformation of the blade-fixture system occurs on the four clamping heads,and this fixture can effectively protect the blade from local deformation.The proposed clamping-sequence method reliably and effectively controls the local maximum deformation of the blade.The system stiffness is increased by 20 Hz,with each clamping force increased by 200 N.Both high-and low-frequency displacement in roughing milling or finishing milling are acceptable relative to the accuracy demand of blade machining.This fixture and an adaptive CNC machining process can achieve high accuracy in blade manufacturing.

Three-dimensional modeling and reconstructive change of residual stress during machining process of milling, polishing, heat treatment, vibratory finishing, and shot peening of fan blade

Residual stress during the machining process has always been a research hotspot,especially for aero-engine blades.The three-dimensional modeling and reconstructive laws of residual stress among various processes in the machining process of the fan blade is studied in this paper.The fan blades of Ti-6Al-4V are targeted for milling,polishing,heat treatment,vibratory finishing,and shot peening.The surface and subsurface residual stress after each process is measured by the X-ray diffraction method.The distribution of the surface and subsurface residual stress is analyzed.The Rational Taylor surface function and cosine decay function are used to fit the characteristic function of the residual stress distribution,and the empirical formula with high fitting accuracy is obtained.The value and distribution of surface and subsurface residual stress vary greatly due to different processing techniques.The reconstructive change of the surface and subsurface residual stress of the blade in each process intuitively shows the change of the residual stress between the processes,which has a high reference significance for the research on the residual stress of the blade processing and the optimization of the entire blade process.

Machining Process Simulator for Concurrent Engineering

Integration is a key component of CIMS and concurrent engineering is an important step in realizing CIMSprocess integration. Concurrent engineering is itself dependent on an effective machining process simulator that verifies the machining process. A machining process simulator for concurrent engineering CMPS was developed at CIMSERC to meet the need for an effective simulator. This paper introduces the CMPS structure. key techniques. including geometry model construction and kinematics. NC code translation. collision and interference checks, material removal simulation. machining animation. etc. The model uses the solid Ray-representation method and the Voxelsplus B-representation algorithm. The Ray-representation method simplifies the Boolean operation process and improves the material removal simulation speed. The Voxels plus B-representation algorithm quickly detects collisionand interference problems. Finally. CMPS is applied to an actual NC milling process as an example.

MICRO ELECTRICAL DISCHARGE MACHINING DEPOSITION IN AIR

A new deposition method is described using micro electrical discharge machining （EDM） to deposit tool electrode material on workpiece in air. The basic principles of micro electrical discharge deposition （EDD） are analyzed and the realized conditions are predicted. With an ordinary EDM shaping machine, brass as the electrode, high-speed steel as the workpiece, a lot of experiments are carried out on micro EDD systematically and thoroughly. The effects of major processing parameters, such as the discharge current, discharge duration, pulse interval and working medium, are obtained, As a result, a micro cylinder with 0.19 mm in diameter and 7.35 mm in height is deposited. By exchanging the polarities of the electrode and workpiece the micro cylinder can be removed selectively. So the reversible machining of deposition and removal is achieved, which breaks through the constraint of traditional EDM. Measurements show that the deposited material is compact and close to workpiece base, whose components depend on the tool electrode, material.

A Cooperated Imperialist Competitive Algorithm for Unrelated Parallel Batch Machine Scheduling Problem

This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.

Event-driven process execution model for process virtual machine

Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.

Heat Transfer of Boiling R134a and R142b on a Twisted Tube with Machine Processed Porous Surface

The objective of this work was to investigate nucleate pool boiling heat transfer performance and mechanism of R134a and R142b on a twisted tube with machine processed porous surface （T-MPPS tube） as well as to determine its potential application to flooded refrigerant evaporators. In the experimental range, the boiling heat transfer coefficients of R134a on a T-MPPS tube were 1.8-2.0 times larger than those of R134a on a plain tube. In addition, the developed experimental correlations verified that the predictions of the heat transfer coefficients of boiling R134a and R142bon a T-MPPS tube at the experimental conditions were considerably accurate.

Determination of reservoir induced earthquake using support vector machine and gaussian process regression

The prediction of magnitude （M） of reservoir induced earthquake is an important task in earthquake engineering. In this article, we employ a Support Vector Machine （SVM） and Gaussian Process Regression （GPR） for prediction of reservoir induced earthquake M based on reservoir parameters. Comprehensive parameter （E） and maximum reservoir depth] （H） are considered as inputs to the SVM and GPR. We give an equation for determination oil reservoir induced earthquake M. The developed SVM and GPR have been compared with] the Artificial Neural Network （ANN） method. The results show that the developed SVM and] GPR are efficient tools for prediction of reservoir induced earthquake M. /

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Modelling and simulation of high-speed milling chatter regeneration based on MATLAB

Considering the deficiency in milling process parameters selection, based on the modelling of dynamic milling force and the deduction of chatter stability limits, the chatter stability lobes simulation program for milling is developed with MAT- LAB. The simulation optimization application software of dynamics was designed using engineering simulation software Visio Basic. The chatter stability lobes for milling, which can be used as an instruction guide for the selection of process parameters, are simulated with frequency response functions （FRFs） gained by hammer test. The validation and accuracy of the simulation algorithm are verified by experiments. The simulation method has been used in a factory with an excellent application effect.

A Multi-Objective Hybrid Algorithm for the Casting Scheduling Problem with Unrelated Batch Processing Machine

The casting production process typically involves single jobs and small batches,with multiple constraints in the molding and smelting operations.To address the discrete optimization challenge of casting production scheduling,this paper presents a multi-objective batch scheduling model for molding and smelting operations on unrelated batch processing machines with incompatible job families and non-identical job sizes.The model aims to minimise the makespan,number of batches,and average vacancy rate of sandboxes.Based on the genetic algorithm,virus optimization algorithm,and two local search strategies,a hybrid algorithm(GA-VOA-BMS)has been designed to solve the model.The GA-VOA-BMS applies a novel Batch First Fit(BFF)heuristic for incompatible job families to improve the quality of the initial population,adopting the batch moving strategy and batch merging strategy to further enhance the quality of the solution and accelerate the convergence of the algorithm.The proposed algorithm was then compared with multi-objective swarm optimization algorithms,namely NSGA-ll,SPEA-l,and PESA-ll,to evaluate its effectiveness.The results of the performance comparison indicate that the proposed algorithm outperforms the others in terms of both qualityand stability.

ANALYSIS OF UN-COINCIDE COORDINATE ERROR IN SINGLE-AXIS ROTATING FIBER OPTIC STRAP-DOWN INERTIAL NAVIGATION SYSTEM

The un-coincide coordinate error in the single-axis rotating fiber optic strap-down inertial navigation system（SINS） is analyzed. Firstly, a rotating modulation technology is presented for SINS. The method provides the enhanced property of SINS when using the same-leveled inertial measurement units. Then, the rotating struc- ture modification is derived and augmented to resolve the un-modulated error-accumulated problem. As the insuf- ficient machine processing, the horizontal and the vertical errors on the machine surface are inevitable, and the in- volved coordinates are difficult to get the exact coincident. So, two major kinds of coordinate situation are stud- ied. The equivalent error models on gyro and acceleration outputs are built for each situation, and the impact is analyzed for compensation. The part of attitude and position error models caused by the built angle-rate error is established to calculate the un-eoincident impact. Considering these conditions of different gyro accuracy and mo- tion states simultaneously, numerical simulations are implemented. Results indicate that the SINS modulation ac- curacy is seriously affected by the combined factors on gyro accuracy and motion conditions.

Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Magnesium(Mg)alloys are extensively used in the automotive and aircraft industries due to their prominent properties.The selection of appropriate process parameters is an important decision to be made because of the cost reduction and quality improvement.This decision entails the selection of suitable process parameters concerning various conflicting factors,so it has to be addressed with the Multiple Criteria Decision Making(MCDM)method.Therefore,this work addresses the MCDM problem through the TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)and COPRAS(COmplex PRoportional ASsessment)methods.The assessment carried out in the material Mg AZ91 with the Solid Carbide(SC)drill bit.The dependent parameters like drilling time,burr height,burr thickness,and roughness are considered with the independent parameters like spindle speed and feed rate.Drilling alternatives are ranked using the above said two methods and the results are evaluated.The optimum combination was found on the basis of TOPSIS and COPRAS for simultaneous minimization of all the responses which is found with a spindle speed of 4540 rpm and a feed rate of 0.076 mm/rev.The identical sequencing order was observed in TOPSIS and COPRAS method.The empirical model was developed through Box-Behnken design for each response.Superior empirical model developed for drilling time which is 3.959 times accurate than the conventional equation.The trends of various dependents based on the heterogeneity of various independents are not identical,these complex mechanisms are identified and reported.The optimized results of the Desirability Function Approach are greater accordance with the TOPSIS and COPRAS top rank.The confirmation results are observed with lesser deviation suggesting the selection of the above independent parameters.

Design and Fabrication of a Three Dimensional Commemorative Artistic Plaque

In this paper, design and fabrication of a commemorative plaque are described and presented. The plaque was fabricated to honour the memory of the 14 women massacred at L＇Ecole Polytechnique in Montreal. This plaque is the result of a project partnership between the Faculties of Engineering and Fine Arts, and was sponsored by the Office of the Vice-President Academic and Provost. An art design was selected through a contest coordinated by the Visual Arts Departmment. The selected art design was then turned over to the Mechanical Engineering Department to be converted to a 3-dimensional （3D） solid model and then eventually fabricated on a computer numerical control （CNC） milling machine. The fabricated plaque was unveiled during the December 2010 Memorial event at UVic.

Application of artificial intelligence in ophthalmology

Artificial intelligence is a general term that means to accomplish a task mainly by a computer, with the least human beings participation, and it is widely accepted as the invention of robots. With the development of this new technology, artificial intelligence has been one of the most influential information technology revolutions. We searched these English-language studies relative to ophthalmology published on PubMed and Springer databases. The application of artificial intelligence in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, agerelated macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. According to the above studies, we conclude that the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7%, for non-proliferative diabetic retinopathy ranged from 75% to 94.7%, for age-related macular degeneration it ranged from 75% to 100%, for retinopathy of prematurity ranged over 95%, for retinal vein occlusion just one study reported ranged over 97%, for glaucoma ranged 63.7% to 93.1%, and for cataract it achieved a more than 70% similarity against clinical grading.