Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

Urbikaín, Gorka; Olvera, D.; Lacalle, Luís Norberto López de; Beranoagirre, A.; Elı́as-Zúñiga, Alex

doi:10.3390/app9214718

Cited by 33 publications

(13 citation statements)

References 109 publications

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“…The size of the component F p reflected the entire toolpath in engagement. Research of cutting force, or of cutting force components realized by several experts [11,20,23,34,[34][35][36] in the previous period, was focused mainly on the determination of the impact of tool movement on its load; the creation of mechanistic models; and, last but not least, identification of other impacts, such as machining vibrations. The detected dependencies make it possible to get an accurate idea of the current tool load.…”

Section: Analysis Of Results For the Computation Of The Prediction Modelmentioning

confidence: 99%

Analysis and Prediction of the Machining Force Depending on the Parameters of Trochoidal Milling of Hardened Steel

et al. 2020

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Current demands on quality are the engine of searching for new progressive materials which should ensure enough durability in real conditions. Due to their mechanical properties, however, they cannot be applied to conventional machining methods. In respect to productivity, one of the methods is the finding of such machining technologies which allow achieving an acceptable lifetime of cutting tools with an acceptable quality of a machined surface. One of the mentioned technologies is trochoidal milling. Based on our previous research, where the effect of changing cutting conditions (cutting speed, feed per tooth, depth of cut) on tool lifetime was analysed, next, we continued with research on the influences of trochoid parameters on total machining force (step and engagement angle) as parameters adjustable in the CAM (computer-aided machining) system. The main contribution of this research was to create a mathematical-statistical model for the prediction of cutting force. This model allows setting up the trochoid parameters to optimize force load and potentially extend the lifetime of the cutting tool.

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Section: Analysis Of Results For the Computation Of The Prediction Modelmentioning

confidence: 99%

Analysis and Prediction of the Machining Force Depending on the Parameters of Trochoidal Milling of Hardened Steel

et al. 2020

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“…Finally, it is desired to integrate process dynamics and mechanics into machine learning scheme [37] for enhancing the accuracy of prediction over the pure data driven approach. With other current investigated facility such as wireless communication and edge computation based smart factory data decision system ready [38,39], it is possible to feedback the analyze information to control the operation of machines for enhancing system reliabilities and for improving stability in milling and tunning processes [40,41].…”

Section: Focused Future Workmentioning

confidence: 99%

Evaluation of Transducer and Signature Selections on the Performance of Artificial Intelligence Machine Tool Wear Prognosis

Sun

Cheng

Chen

2021

Preprint

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The qualities of machined products are largely depended on the status of machines in various aspects. Thus, appropriate condition monitoring would be essential for both quality control and longevity assessment. Recently, with the advance in artificial intelligence and computational power, status monitoring and prognosis based on data driven approach becomes more practical. However, unlike machine vision and image processing, where data types are fixed and the performance index has already well defined, sensor selection and index for machine tools are versatile and not standardized at this moment. Without supporting of appropriate domain knowledge for selecting appropriate sensors and adequate performance index, pure data driven approach might suffer from unsatisfied prediction accuracy and needing of excessive training data, as well as the possibility of misjudgment. This would be a key obstacle for promoting data driven based prognosis in general intelligent manufacturing field. In this work, the status monitoring and prediction of a cutter wear problem is investigated to address the above concerns and to demonstrate the possible solutions by hiring a 5-axis machine center equipped with milling cutters of different wear levels. Transducers including accelerometers, microphones, current transformer, and acoustic emission sensors are mounted on the spindle, fixture, and nearby structures to monitor the milling process. The collected data are processed to extract various signatures and the key dominated indexes are identified. Finally, three multilayer perception (MLP) artificial neural network models are established. These models trained by different input features are compared to examine the influence of selected sensors and indexes on the prediction accuracy. The results show that with appropriate sensors and signatures, even with less amount of experimental data, the model can indeed achieve a better prediction. Therefore, a proper selection of indexes guided by physical knowledge based experiment or theoretical investigation would be critical.

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“…However, despite the existence of systems to support designers/technologists and operators, so far, no solution has been developed to copy/manufacture a part without specialist knowledge including part design, development of machining technology, and manufacturing the part using a CNC machine. Currently, due to the development of the idea of Industry 4.0 and the Internet of Things, systems combining the tasks of a designer, a technologist and an operator are the object of increased research [ 18 , 19 ]. The popularity of these systems will continue to grow.…”

Section: Introductionmentioning

confidence: 99%

Remanufacturing System with Chatter Suppression for CNC Turning

Miądlicki

Jasiewicz

Gołaszewski

et al. 2020

Sensors

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The paper presents the concept of a support system for the manufacture of machine spare parts. The operation of the system is based on a reverse engineering module enabling feature recognition based on a 3D parts scan. Then, a CAD geometrical model is generated, on the basis of which a machining strategy using the CAM system is developed. In parallel, based on the geometric model, a finite element model is built, which facilitates defining technological parameters, allowing one to minimize the risk of vibrations during machining. These parameters constitute input information to the CAM module. The operation of the described system is presented on the example of machining parts of the shaft class. The result is a replacement part, the accuracy of which was compared by means of the iterative closest point algorithm obtaining the RMSE at the level of scanner accuracy.

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Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

Cited by 33 publications

References 109 publications

Analysis and Prediction of the Machining Force Depending on the Parameters of Trochoidal Milling of Hardened Steel

Analysis and Prediction of the Machining Force Depending on the Parameters of Trochoidal Milling of Hardened Steel

Evaluation of Transducer and Signature Selections on the Performance of Artificial Intelligence Machine Tool Wear Prognosis

Remanufacturing System with Chatter Suppression for CNC Turning

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