Integrated optimization method for helical gear hobbing parameters considering machining efficiency, cost and precision

Wu, Dayuan; Yan, Ping; Guo, You; Zhou, Han; Yi, Runzhong

doi:10.1007/s00170-021-06616-3

Cited by 13 publications

(3 citation statements)

References 43 publications

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“…e precondition of fine blanking die wear is the friction between the die and the material working surface during fine blanking [12], but the severity of die working surface wear mainly depends on the normal force acting on the die surface, sliding speed, the state of the friction surface, and the hardness of the die surface according to the Archard wear model.…”

Section: Comparative Analysis Of Wear Amountmentioning

confidence: 99%

Numerical Simulation of Fine Blanking Die Wear and Die Performance Analysis

Li¹,

Zhao²,

Hao

2022

Journal of Chemistry

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In order to study the die wear law of rotary fine blanking helical cylindrical gear, a three-dimensional rigid plastic finite element model of rotary fine blanking helical cylindrical gear is established on the Deform-3D software platform. Based on the Archard wear model, this paper analyzes the die wear in the fine blanking process, obtains the wear distribution of each point on the working surface of the die, determines the maximum wear area, and makes a comparative analysis with the fine blanking die wear of spur gear. Through the single factor variable method, this paper studies the effects of reverse jacking force, blank holder force, blanking speed, punch and die clearance, die fillet radius, and die initial hardness on die wear. The test results show that when the punch reduction is the same, the wear of the female die of fine blanking helical cylindrical gear is greater than that of fine blanking spur cylindrical gear. When the counter jacking force increases from 100 kN to 200 kN, the maximum wear of the die gradually increases from 6.91 × 10–6 mm to 9.38 × 10–6 mm; when the blank holder force increases to 3/4 of the blanking force, the die wear decreases with the increase of blank holder force; it is ideal when the blanking clearance is 0.5% t (0.02 mm). Conclusion. The wear model can accurately predict the relationship between main process parameters and wear in the process of rotary fine blanking, and the measures to reduce die wear are put forward from the aspect of process design.

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Section: Comparative Analysis Of Wear Amountmentioning

confidence: 99%

Numerical Simulation of Fine Blanking Die Wear and Die Performance Analysis

Li¹,

Zhao²,

Hao

2022

Journal of Chemistry

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“…Some of these negatives include increased tool wear due to a higher cutting force and temperature. 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.…”

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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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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“…In the above expression, x → i (t) represents the optimum position determined after t iterations of the ith tooth, g → i,best represents the previous optimum position of the ith tooth, z → i,best represents the optimum location determined under the global prediction, F i,best represents the determined fitness value obtained at the g → i,best position, and Fx i represents the fitness value obtained at the x → i (t) position. e direction determined according to the prediction can be based on the current optimum position and the optimum position obtained when the gear teeth pass through t iterations [7,8].…”

Section: Determination Of the Global Prediction Directionmentioning

confidence: 99%

Prediction of Deviation Range of Gear Autonomous Machining Relying on Failure Sign Algorithm of Firefly Neural Network

Xie

2022

Mobile Information Systems

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In view of the problems of complex deviation sources in gear machining and low accuracy of empirical prediction, in order to improve the detection accuracy and the operation speed of gears after machining, the failure sign algorithm of the firefly neural network (FSAFNN) is applied to the prediction of the deviation range of gear autonomous machining, and a gear tooth direction detection method is designed based on this method. The failure sign algorithm of the firefly neural network is constructed. Taking the tooth profile deviation in the gear machining process as the research target, a prediction model of the gear autonomous machining deviation is constructed, which can be used to effectively obtain the different invalid modes of the gear profile machining. The firefly neural network is quantitatively analyzed to obtain the main reasons for the tooth profile machining deviation. The firefly neural network algorithm can be used to take advantage of its advantages in metal and reflective object prediction. The final research results show that the method used in this paper can meet the requirements of high-precision and nondestructive detection of gears in industrial design, realize the division of gear accuracy levels, which can be used in the field of high-precision detection of other metal reflective objects, and is practical in the prediction of the deviation range of gear autonomous machining.

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Integrated optimization method for helical gear hobbing parameters considering machining efficiency, cost and precision

Cited by 13 publications

References 43 publications

Numerical Simulation of Fine Blanking Die Wear and Die Performance Analysis

Numerical Simulation of Fine Blanking Die Wear and Die Performance Analysis

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

Prediction of Deviation Range of Gear Autonomous Machining Relying on Failure Sign Algorithm of Firefly Neural Network

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