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.

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.

Approximate-model Based Estimation Method for Dynamic Response of Forging Processes

Many high-quality forging productions require the large-sized hydraulic press machine（HPM） to have a desirable dynamic response. Since the forging process is complex under the low velocity, its response is difficult to estimate. And this often causes the desirable low-velocity forging condition difficult to obtain. So far little work has been found to estimate the dynamic response of the forging process under low velocity. In this paper, an approximate-model based estimation method is proposed to estimate the dynamic response of the forging process under low velocity. First, an approximate model is developed to represent the forging process of this complex HPM around the low-velocity working point. Under guaranteeing the modeling performance, the model may greatly ease the complexity of the subsequent estimation of the dynamic response because it has a good linear structure. On this basis, the dynamic response is estimated and the conditions for stability, vibration, and creep are derived according to the solution of the velocity. All these analytical results are further verified by both simulations and experiment. In the simulation verification for modeling, the original movement model and the derived approximate model always have the same dynamic responses with very small approximate error. The simulations and experiment finally demonstrate and test the effectiveness of the derived conditions for stability, vibration, and creep, and these conditions will benefit both the prediction of the dynamic response of the forging process and the design of the controller for the high-quality forging. The proposed method is an effective solution to achieve the desirable low-velocity forging condition.

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.

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.

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.

Flow-Shop Scheduling with Transportation Capacity and Time Consideration

Planning and scheduling is one of the most important activity in supply chain operation management.Over the years,there have been multiple researches regarding planning and scheduling which are applied to improve a variety of supply chains.This includes two commonly used methods which are mathematical programming models and heuristics algorithms.Flowshop manufacturing systems are seen normally in industrial environments but few have considered certain constraints such as transportation capacity and transportation time within their supply chain.A two-stage flowshop of a single processing machine and a batch processing machine are considered with their capacity and transportation time between twomachines.The objectives of this research are to build a suitable mathematical model capable of minimizing the maximum completion time,to propose a heuristic optimization algorithm to solve the problem,and to develop an applicable program of the heuristics algorithm.AMixed Integer Programming(MIP)model and a heuristics optimization algorithmwas developed and tested using a randomly generated data set for feasibility.The overall results and performance of each approach was compared between the two methods that would assist the decision maker in choosing a suitable solution for their manufacturing line.

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.

Acknowledgments

Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. The electroencephalogram, or EEG, is a physiological method to measure and record the electrical

Feature Setup Determination in Integrated CAD/CAM System For Concurrent Engineering

This paper presents a feature-based method for machining process planning in integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The feature setup generation and machining sequence can be determined automatically in this system. The set of knowledge-based rules for process planning and manufacturability evaluation is provided and can be shared by all stages of full product life-cycle. An approach for MTAD (Multiple Tool Axis Direction) feature setup generation is presented and the appropriate Tool Axis Direction(TAD) is chosen to minimize the total setup numbers of a part. The classification and process planning of interacting feature are discussed and the knowledge-based rules are used to solve the feature interaction problem.

Electrochemical machining of complex components of aero-engines: Developments, trends, and technological advances

Because of several advantages, such as no tool wear, independence on the mechanical properties of the material, and high machining efficiency, electrochemical machining(ECM) has become a viable method for machining components in numerous industrial applications, particularly in the manufacture of typical aero-engine components with complex structures fabricated from materials that are difficult to cut. This paper highlights the current developments, new trends, and technological advances of key factors of ECM, such as electrochemical dissolution characteristics of novel difficult to cut materials which are often used in aero-engine, numerical simulation of electrochemical process, design for the complex profile and structure of cathode tool, flow field simulation and design for uniform electrolyte flow, and innovation of electrochemical machining or hybrid methods which reflect the state of the art in academic and industrial research on electrochemical machining in aero-engine manufacturing.

Assessment of wear micromechanisms on a laser textured cemented carbide tool during abrasive-like machining by FIB/FESEM

The combined use of focused ion beam(FIB)milling and field-emission scanning electron microscopy inspection(FESEM)is a unique and successful approach for assessment of near-surface phenomena at specific and selected locations.In this study,a FIB/FESEM dual-beam platform was implemented to docment and analyze the wear micromechanisms on a laser-surface textured(LST)hardmetal(HM)tool.In particular,changes in surface and microstructural integrity of the laser-sculptured pyramids(effective cutting microfeatures)were characterized after testing the LST-HM tool against a steel workpiece in a workbench designed to simulate an external honing process.It was demonstrated that:(1)laser-surface texturing does not degrade the intrinsic surface integrity and tool effectiveness of HM pyramids;and(2)there exists a correlation between the wear and loading of shaped pyramids at the local level.Hence,the enhanced performance of the laser-textured tool should consider the pyramid geometry aspects rather than the microstructure assemblage of the HM grade used,at least for attempted abrasive applications.

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.

Hidden Process Offline Forensic Based on Memory Analysis in Windows

Malicious software programs usually bypass the detection of anti-virus software by hiding themselves among apparently legitimate programs.In this work,we propose Windows Virtual Machine Introspection（WVMI）to accurately detect those hidden processes by analyzing memory data.WVMI dumps in-memory data of the target Windows operating systems from hypervisor and retrieves EPROCESS structures’address of process linked list first,and then generates Data Type Confidence Table（DTCT）.Next,it traverses the memory and identifies the similarities between the nodes in process linked list and the corresponding segments in the memory by utilizing DTCT.Finally,it locates the segments of Windows’EPROCESS and identifies the hidden processes by further comparison.Through extensive experiments,our experiment shows that the WVMI detects the hidden process with high identification rate,and it is independent of different versions of Windows operating system.