A non-uniform allowance allocation method based on interim state stiffness of machining features for NC programming of structural parts

For thin-walled parts,uniform allowance to each machining surface is allocated by the traditional machining method.Considering the sequence of the adjacent machining features,it may cause poor stiffness for some side walls due to a minor wall thickness,which may cause the deformation of the final formed parts to be large,or deduce machining efficiency for some machining features due to too thick remains.In order to address this issue,a non-uniform allowance allocation method based on interim state stiffness of machining features for the finishing of thin-walled structural parts is proposed in this paper.In this method,the interim state model of machining features is constructed according to the machining sequence of the parts,and the stiffness of the side wall is taken as the evaluation index to allocate reasonable allowance value to the corresponding machining surface to ensure the stiffness requirement of the parts in the machining process.According to the finite element simulation results,the non-uniform allowance allocation method proposed in this paper can effectively improve the stiffness of the parts and reduce the deformation of the parts,when compared with the traditional uniform allowance machining method.

A Definition Formalism of Machining Features for DFM

Benefiting from advances in feature technology for design and manufacture can not be expected before a formal methodology is established. This paper makes attempt to establish a definition formalism of machining features in design for manufacturability from two aspects: formal definition and manufacturability analysis. Some definitions for machining feature based upon the selection and sequencing of material removal operations for component in accordance with the design geometry are presented and a framework of feature based design for manufacturability is outlined correspondingly. The proposed scheme contributes to several aspects of feature based CAD/CAM integration, especially to encourage potentially a more generic approach to the automation of design.

Pre-Compensation for Continuous-Path Running Trajectory Error in High-Speed Machining of Parts with Varied Curvature Features

Parts with varied curvature features play increasingly critical roles in engineering, and are often machined under high-speed continuous-path running mode to ensure the machining efficiency. However, the continuous-path running trajectory error is significant during high-feed-speed machining, which seriously restricts the machining precision for such parts with varied curvature features. In order to reduce the continuous-path running trajectory error without sacrificing the machining efficiency, a pre-compensation method for the trajectory error is proposed. Based on the formation mechanism of the continuous-path running trajectory error analyzed, this error is estimated in advance by approximating the desired toolpath with spline curves. Then, an iterative error pre-compensation method is presented. By machining with the regenerated toolpath after pre-compensation instead of the uncompensated toolpath, the continuous-path running trajectory error can be effectively decreased without the reduction of the feed speed. To demonstrate the feasibility of the proposed pre-compensation method, a heart curve toolpath that possesses varied curvature features is employed. Experimental results indicate that compared with the uncompensated processing trajectory, the maximum and average machining errors for the pre-compensated processing trajectory are reduced by 67.19% and 82.30%, respectively. An easy to implement solution for high efficiency and high precision machining of the parts with varied curvature features is provided.

Soft measurement for component content based on adaptive model of Pr/Nd color features

For measurement of component content in the extraction and separation process of praseodymium/neodymium(Pr/Nd), a soft measurement method was proposed based on modeling of ion color features, which is suitable for fast estimation of component content in production field. Feature analysis on images of the solution is conducted,which are captured from Pr/Nd extraction/separation field. H/S components in the HSI color space are selected as model inputs, so as to establish the least squares support vector machine(LSSVM) model for Nd(Pr) content,while the model parameters are determined with the GA algorithm. To improve the adaptability of the model,the adaptive iteration algorithm is used to correct parameters of the LSSVM model, on the basis of model correction strategy and new sample data. Using the field data collected from rare earth extraction production, predictive methods for component content and comparisons are given. The results indicate that the proposed method presents good adaptability and high prediction precision, so it is applicable to the fast detection of element content in the rare earth extraction.

Optimization strategy in end milling process for high speed machining of hardened die/mold steel

An optimization strategy for high speed machining of hardened die/mold steel based on machining feature analysis was studied. It is a further extension of the previously presented study on the thermal mechanism of end milling and constant cutting force control. An objective function concerning machining cost and associated optimization algorithm based on machining time and cutting length calculation was proposed. Constraints to satisfy specific machining strategies when high speed machining the hardened die/mold steel, trochoid tool path pattern in slot end milling to avoid over-heat and feed rate adaptation to avoid over-load, were also discussed. As a case study, the tool selection problem when machining a die part with multiple machining features was investigated.

Assessment of glaucoma using extreme learning machine and fractal feature analysis

Dear Sir,Iam Dr.Kavitha S,from the Department of Electronics and Communication Engineering,Nandha Engineering College,Erode,Tamil Nadu,India.I write to present the detection of glaucoma using extreme learning machine(ELM)and fractal feature analysis.Glaucoma is the second most frequent cause of permanent blindness in industrial

THE RESEARCH OF MANUFACTURING TOOLS(FIXTURES) PLANNING FOR ARMOURED FLEXIBLE CONVEYOR (AFC) USING CAD TECHNIQUE

The deepth and width of CAD application in coal mining equipments need promote furtherly. The information stream method is applied as the main clue to deal with the related technology and problems in research of manufacturing tools (Fixtures) planning for AFC (mining scraper bars conveyor) using CAD technique.

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.

A New Solution for Feature Recognition from 2D Engineering Drawing

The paper presents a promised way of feature recognition from 2D engineering drawing——developing special system and extracting features from machining drawings. In general, researchers inclined to extract features from design drawings and ignored machining drawings. Actually, both of machining and design information shows the same importance in developing new products. Not only can machining drawing provide us with feature model or 3D geometrical model of the part, but also they can be easily recognized. In the paper the processes and methods of feature recognition from three-cone-bit (A Kind of aiguilles used to drill oil well) machining drawings are introduced. Firstly, overall approach is explained. Secondly, two methods of form feature recognition are introduced: symbol-matching method used to analyze annularity or chained graph and method based on feature-hint used to recognize the general features. Thirdly, feature parameters are extracted. Finally, a practical implementation is given.

Feature-based Integrated CAD/CAPP/CAM System For Concurrent Engineering

This paper presents methodologies and technologies of feature_based integrated product designing and manufacturing system for CE(Concurrent Engineering) application. The product information is represented on the basis of hierarchical and dynamic structure of feature representation. The Object_Oriented feature modeling method is adopted to represent the feature classification, feature relationship and feature interaction. The set of knowledge_based rule for process planing and manufacturiability evaluation is provided and can be shared by all stages of full product life_cycle. The feature_based machining operation and machining sequence can be determined automatically. The machining process of the machining feature can be determined according to the set of knowledge_based rule.

Fatigue driving detection based on Haar feature and extreme learning machine

As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers＇ fatigue driving by using the human eye tracking technology.

A Feature Selection Method for Prediction Essential Protein

Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction.