Interactive virtual machining system using informative data structure and on-site machine tool status

Author(s):  
A. Z. Abdul Kadir ◽  
X. Xu
2018 ◽  
Vol 25 ◽  
pp. 338-343 ◽  
Author(s):  
Nikolas Theissen ◽  
Theodoros Laspas ◽  
Károly Szipka ◽  
Andreas Archenti

2021 ◽  
Author(s):  
Giancarlo Nota ◽  
Alberto Postiglione

This paper presents an innovative methodology, from which an efficient system prototype is derived, for the algorithmic prediction of malfunctions of a generic industrial machine tool. It integrates physical devices and machinery with Text Mining technologies and allows the identification of anomalous behaviors, even of minimal entity, rarely perceived by other strategies in a machine tool. The system works without waiting for the end of the shift or the planned stop of the machine. Operationally, the system analyzes the log messages emitted by multiple data sources associated with a machine tool (such as different types of sensors and log files produced by part programs running on CNC or PLC) and deduces whether they can be inferred from them future machine malfunctions. In a preliminary offline phase, the system associates an alert level with each message and stores it in a data structure. At runtime, three algorithms guide the system: pre-processing, matching and analysis: Preprocessing, performed only once, builds the data structure; Matching, in which the system issues the alert level associated with the message; Analysis, which identifies possible future criticalities. It can also analyze an entire historical series of stored messages The algorithms have a linear execution time and are independent of the size of the data structure, which does not need to be sorted and therefore can be updated without any computational effort.


2010 ◽  
Vol 97-101 ◽  
pp. 1849-1852
Author(s):  
Tong Yue Wang ◽  
Ning He ◽  
Liang Li

Thin-walled structure is easy to vibrate in machining. The dynamic milling model of thin-walled workpiece is analyzed based on the analysis of degrees in two perpendicular directions of machine tool-workpiece system. In high speed milling of 2A12 aluminum alloy, the compensation method based on the modification of inertia effect is proposed and accurate cutting force coefficients are obtained. The machining system is divided into “spindle-cutter” and “workpiece-fixture” two sub-systems and the modal parameters of two sub-systems are acquired via modal analysis experiments. Finally, the stability lobes for high speed milling of 2A12 thin-walled workpiece are obtained by the use of these parameters. The results are verified against cutting tests.


2017 ◽  
Vol 5 (3) ◽  
pp. 299-304 ◽  
Author(s):  
Hong-seok Park ◽  
Bowen Qi ◽  
Duck-Viet Dang ◽  
Dae Yu Park

Abstract Feedrate optimization is an important aspect of getting shorter machining time and increase the potential of efficient machining. This paper presents an autonomous machining system and optimization strategies to predict and improve the performance of milling operations. The machining process was simulated and analyzed in virtual machining framework to extract cutter-workpiece engagement conditions. Cutting force along the cutting segmentation is evaluated based on the laws of mechanics of milling. In simulation, constraint-based optimization scheme was used to maximize the cutting force by calculating acceptable feedrate levels as the optimizing strategy. The intelligent algorithm was integrated into autonomous machining system to modify NC program to accommodate these new feedrates values. The experiment using optimized NC file which generates by our smart machining system were conducted. The result showed autonomous machining system, was effectively reduced 26%. Highlights The smart machining system was implemented in the CNC machine. Optimal feed rates enhance machine tool efficiency. The smart machining system is reliable to reduce machine time.


2012 ◽  
Vol 2012 (01) ◽  
pp. 310-314
Author(s):  
Andreas Archenti ◽  
Mihai Nicolescu ◽  
Thomas Lundholm

2010 ◽  
Vol 4 (3) ◽  
pp. 235-242 ◽  
Author(s):  
Hirohisa Narita ◽  
◽  
Keiichi Shirase ◽  
Eiji Arai ◽  
Hideo Fujimoto ◽  
...  

Test cutting used to verify cutting conditions and machining accuracy after a numeric control (NC) program is written for end milling the mold and die indispensable to manufacturing is generally effective, because it is based on trial and error. The virtual machining simulator we designed to verify machining accuracy uses an accuracy-prediction model and an error prediction expression for workpieces, integrating machine-tool deformation and geometric error models. We also propose calculation for copying errors to a workpiece.


Author(s):  
P Vichare ◽  
A Nassehi ◽  
S Newman

The capability of any manufacturing system primarily depends on its available machine tools. Thus machine tool representation is a vital part of modelling any manufacturing system. With the rapid advances in computerized numerically controlled (CNC) machines, machine tool representation has become a more challenging task than ever before. Today's CNC machine tools are more than just automated manufacturing machines, as they can be considered multi-purpose, multi-tasking, and hybrid machining centres. This paper presents a versatile methodology for representing such state-of-the-art CNC machining system resources. A machine tool model is a conceptual representation of the real machine tool and provides a logical framework for representing its functionality in the manufacturing system. There are several commercial modelling tools available in the market for modelling machine tools. However, there is no common methodology among them to represent the wide diversity of machine tool configurations. These modelling tools are either machine vendor specific or limited in their scope to represent machine tool capability. In addition, the current information models of STEP-NC, namely ISO 14649, can only describe machining operations, technologies, cutting tools, and product geometries. However, they do not support the representation of machine tools. The proposed unified manufacturing resource model (UMRM) has a data model which can fill this gap by providing machine specific data in the form of an EXPRESS schema and act as a complementary part to the STEP-NC standard to represent various machine tools in a standardized form. UMRM is flexible enough to represent any type of CNC machining centre. This machine tool representation can be utilized to represent machine tool functionality and consequential process capabilities for allocating resources for process planning and machining.


2014 ◽  
Vol 599-601 ◽  
pp. 405-408
Author(s):  
Xiao Min Cheng ◽  
Gen Zhou ◽  
Peng Wu ◽  
Yue Ding Wu

This paper establishes a system that can automatically generate CNC code based on readable image formats such as *. jpg, *. gif, *. bmp, *. pcd, *. tif, and make use of machine tool for CNC machining. This system first vectorizes an image by using preprocessing such as smooth processing, edge detection, and contour tracking, and converts it into CNC code, then generates a program that can be identified by PMAC, and drives machine tool to process CNC machining. At last, an example proves the validity of this method.


2021 ◽  
Vol 67 (5) ◽  
pp. 235-244
Author(s):  
Mohsen Soori ◽  
Mohammed Asmael

To simulate and analyse the real machined parts in virtual environments, virtual machining systems are applied to the production processes. Due to friction, chip forming, and the heat produced in the cutting zone, parts produced using machining operation have residual stress effects. The machining force and machining temperature can cause the deflection error in the machined turbine blades, which should be minimized to increase the accuracy of machined blades. To minimize the residual stress and deflection error of machined parts, optimized machining parameters can be obtained. In the present research work, the application of a virtual machining system is presented to predict and minimize the residual stress and deflection error in a five-axis milling operations of turbine blades. In order to predict the residual stress and deflection error in machined turbine blades, finite element analysis is implemented. Moreover, to minimize the residual stress and deflection error in machined turbine blades, optimized parameters of machining operations are obtained by using a genetic algorithm. To validate the research work, experimentally determining residual stress by using a X-ray diffraction method from the machined turbine blades is compared with the finite element results obtained from the virtual machining system. Also, in order to obtain the deflection error, the machined blades are measured by using the CMM machines. Thus, the accuracy and reliability of machined turbine blades can be increased by analysing and minimizing the residual stress and deflection error in virtual environments.


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