Estimation of dynamic behaviors of hydraulic forging press machine in slow-motion manufacturing process

The vibration of cold orbital forging (COF) machines is detrimental to the quality of the forged part. Therefore, it is necessary to carry out kinematics and dynamics analyses, study the vibration of the COF machine, and provide effective methods for reducing the vibration. In terms of kinematics and dynamics analyses, the mathematical modeling and model solving processes are complicated, and it takes a long time to obtain the kinetic and vibration performances of the COF machine. In this study, the kinematics and dynamics models of the COF machine were investigated. Subsequently, automatic dynamic analysis of mechanical systems (ADAMS) was applied to conduct kinematics and dynamics simulations. The simulation process was convenient and time-saving, and the simulation of the COF machine was effectively verified by conducting an experiment. Based on the simulation results, the kinetic and vibration performances of the COF machine were studied. In addition, a structure optimization method for the COF machine is proposed. Based on the singularity stability theory, the Hopf bifurcation and chaos phenomena are less likely to occur on the optimized COF machine, and therefore the vibration of the optimized COF machine is stable. Meanwhile, the structure optimization method was proven to be effective in reducing the vibration of the COF machine, which improves the quality of the forged part.

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“…Subsequently, the characteristic equation ( 18) can be converted into characteristic equations (23,24).…”

Section: The Structure Optimization Of the Cof Machinementioning

confidence: 99%

Kinematics and dynamics simulations of cold orbital forging machines based on ADAMS

Chen

Zhang

Zhuang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Therefore, the input flow of the system is only related to the open area of the spool, which is determined by the control current of spool. Then the input and output flow can be described as [22]: (9) where I in denotes the control current, C d denotes the flow coefficient, and A 0 is the area of the orifice, which is related to the control current. P 2 , and P 0 are the pressure of the return oil and atmosphere, respectively; ρ is the density of hydraulic oil; and Q in , Q out are the input and output flow of the system.…”

Section: Model Of Hydraulic Steering Systemmentioning

confidence: 99%

Section: Model Of Hydraulic Steering Systemmentioning

confidence: 99%

Hybrid Model-Based Analysis of Underground Articulated Vehicles Steering Characteristics

et al. 2019

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Owing to the harsh environment of underground mines, autonomous underground articulated vehicles (UAVs) with precise control and positioning system are particularly important. However, the ambiguity of steering characteristics hinders the development of UAVs. This study presents a model-based method to uncover the steering characteristics of a UAV. Firstly, a hybrid model of UAV was established, which included a dynamic model of articulated frames and a model of the hydraulic power steering system. Secondly, a field test of a typical UAV, a load-haul-dump (LHD) with 4 m3 capacity, was carried out. In order to verify the correctness of the established model and the accuracy of the involved parameters, the field test results were used to verify the dynamic model in time and frequency domains. Then, the steering characteristics of the UAV were uncovered based on the verified hybrid model, and the results showed that the increased load would increase ‘oversteering’ under the same articulation angle and that the error of trajectory exceeded 0.3 m. In addition, the deviations of trajectories between the two frames were revealed during the transient steering process, and the maximum deviation reached 0.21 m when the velocity was 2 m/s and the articulation angle was 15°. The comprehensive results indicate that the steering characteristics of UAVs cannot be ignored in regard to precise autonomous control and positioning.

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“…The mechanism knowledge of the forging machine has been mastered based on the related principles such as fluid mechanics, dynamics, and machinery technology. For example, the dynamic behaviors of the forging machine were analyzed according to the mechanism model [16]. A focus of the mechanism model with known structure is to determine the parameters, which is often by the way of offline identification and online correction.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

Zhang

Du²

2021

Processes

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It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine.

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Estimation of dynamic behaviors of hydraulic forging press machine in slow-motion manufacturing process

Cited by 5 publications

References 43 publications

Kinematics and dynamics simulations of cold orbital forging machines based on ADAMS

Kinematics and dynamics simulations of cold orbital forging machines based on ADAMS

Hybrid Model-Based Analysis of Underground Articulated Vehicles Steering Characteristics

Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

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