Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites

Wei, Weihua; Shang, Yunyue; Peng, You; Cong, Rui

doi:10.3390/ma15113838

Cited by 7 publications

(3 citation statements)

References 28 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The potential outcome of each optimization problem in the algorithm is a particle in the search space, where all particles have a fitness value determined via the optimized function, and each particle has a velocity that determines the direction and distance of its "flight". Then, the particle follows the current optimal particle in the solution space to complete the search [34,[39][40][41].…”

Section: Pso Algorithmmentioning

confidence: 99%

Optimization of Aluminum Alloy Formwork Geometry Parameters Based on a PSO-BP Neural Network

et al. 2023

View full text Add to dashboard Cite

To assist in addressing the problem where an aluminum alloy formwork (AAF) deforms more greatly under the action of lateral pressure and therefore does not meet the requirements of plaster-free engineering, we propose a method for determining the geometric parameters of this formwork based on a PSO algorithm and BP neural network with ABAQUS as the platform. The influence of six geometric parameters of the formwork on the maximum deflection value of the panel under the action of lateral pressure is studied using finite element analysis. The maximum deflection value of the panel is used as the index, and the influence of each factor is analyzed with an orthogonal test, and a set of optimal geometric parameters is obtained via extreme difference analysis and analysis of variance. The sample data are obtained via finite element simulation, and the PSO-BP neural network model is established using the six factors of the orthogonal test as input values and the maximum deflection of the panel as the output value, and the optimal geometric parameters are optimized using the PSO algorithm. The results indicate that the maximum deflection for the panel in the orthogonal scheme is 1.446 mm. The PSO-BP neural network prediction model demonstrates greater accuracy and a 31.74% reduction in running time compared to the BP neural network prediction model. The optimized PSO-BP neural network prediction model scheme reveals a maximum panel deflection of 1.296 mm, a 10.37% decrease compared to the orthogonal solution. These findings offer technical guidance and a foundation for optimizing AAF designs, presenting practical applications.

show abstract

Section: Pso Algorithmmentioning

confidence: 99%

Optimization of Aluminum Alloy Formwork Geometry Parameters Based on a PSO-BP Neural Network

et al. 2023

View full text Add to dashboard Cite

show abstract

“…For this research, the self-propelled electric tiller has the characteristics of small size, flexibility, low vibration, and a relatively flat working environment. The detection method of a single-angle displacement sensor is adopted because of its economic benefits and other factors [6,21] . The angular displacement sensor is placed at the hinge between the triangular support and the microtiller body to detect the lifting angle of the rotary tiller tool, establish the geometric relationship between the tillage depth and the angle, and finally build the tillage depth detection model.…”

Section: Automatic Detection Model Of Tillage Depthmentioning

confidence: 99%

Design and experiment of fuzzy-PID based tillage depth control system for a self-propelled electric tiller

Xiao,

Ma,

Wang

et al. 2023

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

The research on the self-propelled electric tiller is vital for further improving the quality and efficiency of greenhouse rotary tillage operation, reducing the work intensity and operation risk of operators, and achieving environmentally friendly characteristics. Most of the existing self-propelled tillers rely on manual adjustment of the tillage depth. Moreover, the consistency and stability of the tillage depth are difficult to guarantee. In this study, the automatic control method of tillage depth of a self-propelled electric tiller is investigated. A method of applying the fuzzy PID (Proportional Integral Derivative) control method to the tillage depth adjustment system of a tiller is also proposed to realize automatic control. The system uses the real-time detection of the resistance sensor and angle sensor. The controller runs the electronically controlled hydraulic system to adjust the force and position comprehensively. The fuzzy control algorithm is used in the operation error control to realize the double-parameter control of the tillage depth. The simulation and experimental verification of the system are conducted. Results show that the control system applying fuzzy PID can improve the soil breaking rate by 3% in the operation process based on reducing the stability variation of tillage depth by 24%. The control strategy can reach the set value of tillage depth quickly and accurately. It can also meet the requirement of tillage depth consistency during the operation.

show abstract

“…Many current authors deal with classic machining technologies, which are oriented on selection of tool geometry/material, study of suitable conditions of machining technologies in relation to the emerging surface quality, the emergence of cutting forces during machining, etc. [43][44][45][46][47][48][49][50][51]. However, they encounter a fundamental problem-melting the thermoplastic matrix and adhesion to the tool surfaces during the engagement (functional surfaces).…”

Section: Applied Technologiesmentioning

confidence: 99%

Machining of Wood Plastic Composite Using AWJ Technology with Controlled Output Quality

et al. 2022

View full text Add to dashboard Cite

The paper deals with the application of abrasive water-jet cutting to composite material containing natural reinforcement—wood plastic composite. The specimens were cut through the application of four flows of different abrasive mass: 150, 200, 250, and 300 g·min−1, respectively, and under different traverse speeds required to achieve the (expected) quality level Q1–Q5 (according to the SN 214001: 2010 standard). The output quality of Q1–Q5 was set in the CNC cutting programs and the real traverse speed values were calculated by machine control system according to change in the flow of the abrasive mass. The quality of surface topography was assessed using a tester (contact roughness) and an Inspectis digital zoom microscope. The results of topography–surface roughness parameters Ra presented here are compared with the values normalized for individual samples sets. The applied technology, i.e., the AWJ, eliminated the problem of tool wear and adhesion of the thermoplastic matrix to tool surfaces (compared to standard machining).

show abstract

Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites

Cited by 7 publications

References 28 publications

Optimization of Aluminum Alloy Formwork Geometry Parameters Based on a PSO-BP Neural Network

Optimization of Aluminum Alloy Formwork Geometry Parameters Based on a PSO-BP Neural Network

Design and experiment of fuzzy-PID based tillage depth control system for a self-propelled electric tiller

Machining of Wood Plastic Composite Using AWJ Technology with Controlled Output Quality

Contact Info

Product

Resources

About