Chip geometry and cutting force prediction in gear hobbing

Azvar, Milad; Katz, Andrew; Dorp, Jacob Van; Erkorkmaz, Kaan

doi:10.1016/j.cirp.2021.04.082

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…Cutting Force Kistler Dynamometer [30][31][32] Tool holder [30][31][32] Cutting forces were determined using a CAD modelled system and a 2D numerical model.…”

Section: Parameter Sensor Placement Objectivementioning

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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“…Cutting Force Kistler Dynamometer [30][31][32] Tool holder [30][31][32] Cutting forces were determined using a CAD modelled system and a 2D numerical model.…”

Section: Parameter Sensor Placement Objectivementioning

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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“…To the best of our knowledge, this force simulation method has not been used for screw-rotor milling but, it has been used to simulate the forces in operations that closely resemble the kinematics of screw-rotor milling. For example, the forces in gear hubbing, shaping, and power skiving operations has been simulated by using multi-dexel method to determine the CWE geometry [11][12][13]. However, the graphics-based methods impose large and sometimes prohibiting computational and memory load to the force simulation process [11].The high computational load becomes even a more critical problem when force simulations are repeated over and over to optimize the design parameters of the rotor and its machining conditions.…”

Section: Introductionmentioning

confidence: 99%

Modelling forces in milling screw rotors

Wang¹,

Ahmadi

2022

Preprint

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The deflections of screw rotors under machining forces cause mismatch between the male and female rotors and, consequently, accelerated wear and suboptimal efficiency in their performance. Optimizing the machining process to minimize the generated forces and accounting for the resulting mismatch in the design of the rotor profile requires accurately computing the machining forces in computer simulations. Virtual machining systems combine graphics-based computation of the Cutter-Workpiece Engagement (CWE) with the physics-based models of machining mechanics to simulate the forces during complex machining processes. However, because of the high computational load of graphical simulations, virtual machining is not suitable for the repetitive force simulations that are required for optimizing the design and manufacturing of rotors. In this work, we present a new method that simulates screw milling forces based on the process kinematics instead of graphical simulations. The semi-analytical nature of the presented method allows for computing the forces with arbitrary resolution within a reasonable time. The accuracy and efficiency of the presented method is verified by comparing the simulated forces against a dexel-based virtual machining system.

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Modeling forces in milling screw rotors

Wang

Ahmadi

2022

Int J Adv Manuf Technol

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Chip geometry and cutting force prediction in gear hobbing

Cited by 13 publications

References 10 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

Modelling forces in milling screw rotors

Modeling forces in milling screw rotors

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