Simulation and experimental study of tool wear in high-speed dry gear hobbing

Cheng, Yaonan; Ma, Chunjie; Zhang, Jianyu; Zhou, Huan; Xin, Liming; Wang, Xinyu

doi:10.1007/s00170-021-08421-4

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Generally, human operators have the least concern about the tool's life and run the machines at their maximum capacity. Ignoring the optimum conditions affects the induced vibrations, and high rising temperatures during cutting cause noticeable tool wear at the edges [19]. The tool that may not be monitored by the human operators at a certain condition or at a particular time could cause tool wear.…”

Section: Background and Motivational Statementmentioning

confidence: 99%

“…Other works proposed developing a model for optimisation of the gear finish in the hobbing process [42][43][44] and the use of machine learning models to predict the RUL [4,17]. Cheng et al [19] provided a mechanism to test how high-speed dry gear hobbing can affect tool wear. The work demonstrated a theoretical basis for working out how much cutting force and tool wear to expect using the undeformed geometry chip.…”

Section: Other Workmentioning

confidence: 99%

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An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Hameed,

Junejo,

Amin

et al. 2023

Energies

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Industry 4.0 has been an impactful and much-needed revolution that has not only influenced different aspects of life but has also changed the course of manufacturing processes. The main purpose of the manufacturing industry is to increase productivity, reduce manufacturing costs, and improve the quality of the product. This has helped to drive economic growth and improve people’s standards. The gear-hobbing industry, being the most efficient one, has not received much attention in terms of Industry 4.0. In prior works, simulation-based approaches with individual parameters, e.g., temperature, current, and vibration, or a few of these parameters, were considered with different approaches, This work presents a real-time experimental approach that involves raw data collection on three different parameters together, i.e., temperature, current, and vibration, using sensors placed on an industrial machine during gear hobbing process manufacturing. The data are preprocessed and then utilised for training an artificial neural network (ANN) to predict the remaininguseful life (RUL) of a tool. It is demonstrated that an ANN with multiple hidden layers can predict the RUL of the tool with high accuracy. The compared results show that tool wear prediction using an ANN with multiple layers has better prediction accuracy during worm gear hobbing.

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Section: Background and Motivational Statementmentioning

confidence: 99%

Section: Other Workmentioning

confidence: 99%

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Hameed,

Junejo,

Amin

et al. 2023

Energies

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“…As is known to all, when grinding the spiral groove hob, the grinding wheel with straight bus will grind the central convex front tool face, resulting in the radial error of the front tool face. In order to reduce this error, the grinding wheel must be trimmed according to a certain curve [3][4] [5] [6]. How to dress the grinding wheel accurately and conveniently according to a certain curve, so that the grinding wheel can grind the front cutter face of the hob correctly, is an important problem to be solved in the actual production and processing.…”

Section: Introductionmentioning

confidence: 99%

Influence of Hob Front Angle on Grinding Wheel Curved Shapes in Open Edge Grinding

Yang

Wang

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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When calculating the grinding wheel curved shapes, the hob front angle of hob is usually zero by default. In the actual production and processing, there are many hobs whose hob front angle is not zero. In this paper, when calculating the grinding wheel curved shapes, the parameter hob front angle is added to analyze its influence on the grinding wheel curved shapes according to the grinding wheel curved shapes calculated by different hob front angles.

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Wear mechanisms of coated carbide tools during high-speed face milling of Ti2AlNb intermetallic alloys

Wang,

Zhao,

Ding

et al. 2023

Int J Adv Manuf Technol

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Simulation and experimental study of tool wear in high-speed dry gear hobbing

Cited by 7 publications

References 14 publications

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

An Intelligent Deep Learning Technique for Predicting Hobbing Tool Wear Based on Gear Hobbing Using Real-Time Monitoring Data

Influence of Hob Front Angle on Grinding Wheel Curved Shapes in Open Edge Grinding

Wear mechanisms of coated carbide tools during high-speed face milling of Ti2AlNb intermetallic alloys

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