Workpiece In Machining Process Research Articles

Design of Regenerative Flutter Stability Detection System in Machine Tool Cutting

Cutting chatter is a strong relative vibration between cutting tool and work piece in machining process, which will reduce cutting quality and cutting efficiency, and shorten the service life of cutting tool and machine tool. As long as cutting is carried out in production, vibration will occur, and chatter is a strong self-excited vibration between machining work piece and cutting tool. Flutter problem will occur in almost all cutting processes, which will cause a series of problems such as the reduction of dimensional accuracy of machined work pieces, tool damage and so on. In the dynamic design and dynamic analysis of machine tool structure, in order to evaluate and improve the ability of machine tool to resist chatter, and to select the cutting conditions without chatter, it is necessary to judge the cutting stability of machine tool. How to improve the advanced technology of manufacturing industry is an important topic for manufacturing researchers, and the research on the detection of cutting chatter stability has important practical significance for promoting the development of cutting manufacturing industry to high-end technology.

Early chatter detection in end milling based on multi-feature fusion and 3σ criterion

Chatter detection and suppression/control is an effective way to ensure the quality of workpiece in machining process. In this paper, a new method is proposed to detect early chatter before the workpiece surface is deteriorated. As a preparation work, various samples in stable cutting conditions are collected to train the self-organizing map (SOM) neural network. During online chatter monitoring, firstly, vibration signals are measured in real time and multi-features are extracted with signal processing methods to form a feature vector. Then, the feature vector is input to the trained SOM neural network. The minimum quantization error (MQE), namely, the Euclidean distance between the best matching unit (BMU) of the SOM neural network and the feature vector, is calculated as a new chatter detection indicator. Lastly, the MQE is compared with a 3σ criterion-based threshold, which is independent of cutting conditions, to determine the chatter occurrence. The proposed method was verified with end milling tests, and the results showed that the chatter can be detected before serious marks were left on the workpiece surface.

An Adaptive Parameter Tuning Method with On-Machine Weight Identification Function for CNC Machine Tools

Different inertia effect causes different control errors. To have an optimal control performance, the control parameters of a CNC machine tool should be adaptively tuned based on the variation of the weight of the workpiece in machining process. In this study, simulation based on a theoretical model was made to investigate the influence of inertial effect and friction force on the servo accuracy of a CNC machine tool’s. By using empirical method, an adaptive tuning system that can auto-tune the CNC control parameters based on the detected weight of workpiece was developed. An on-machine weight identification system that can on-machine identify the weight of the workpiece was developed. After integrating the two systems, a control parameter tuning system which can be implanted into CNC controller was developed with the macro executor. Experimental results have shown that the system can effectively improve the accuracy of a machine tool through adaptively tuning control parameters based on the current weight of the machined.

Voxel表現に基づく多軸制御加工切削シミュレーションの大規模並列処理手法

This study proposes a new simulation method of workpiece shape with voxel representation for multi axis controlled machining process. Usually, voxel representation requires enormous number of computational complexity and computer memory to estimate accurate shape of machined workpiece. In this paper, a new simulation algorithm based on octree collision detection with multi level voxel representation is proposed. The proposed algorithm is designed to adapt an characteristic of GPU hardware architecture for parallel processing. In the proposed algorithm, as attribute of each voxel, block number of NC program from the moment when the voxel is machined with endmill is described in computer memory. Then, by referring the attributes, the voxeles locate near the cutting tool can be identified within a short time. By applying the proposed algorithm, the prototype simulation system is developed. The developed system can estimate shape of the workpiece in machining process within a several micro seconds at an arbitrary moments.