Optimization of wood machining parameters using artificial neural network in CNC router

Çakmak, Ali; Malkoçoğlu, Abdülkadir; Özşahin, Şükrü

doi:10.1080/02670836.2023.2180901

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…With the upgrading of computer hardware, especially the replacement of graphic processing unit (GPU)s, deep learning methods [12] and detection theories have shown great potential in wood measurement detection. Convolutional neural networks have been successfully applied in building wood measurement detection due to their significant feature extraction and image classification capabilities.…”

Section: Introductionmentioning

confidence: 99%

A fast detection method for building wood measurement based on interval type-2 fuzzy model

Wang,

Xu,

Gui

et al. 2023

Meas. Sci. Technol.

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Automatic measurement detection in the process of wood processing can effectively improve the production efficiency and yield of promote the upgrading of wood processing industry. Therefore, this article conducts research on visual detection technology for wood surface measurements and proposes a construction wood measurement detection method based on interval type II fuzzy theory. This method overcomes the problem of increasing heterogeneity and similarity of homogeneous areas on the wood surface, which increases the difficulty of measurement detection. Firstly, the Gaussian model is improved by establishing a wood measurement image feature model that considers rate neighborhood relationships as the basic model. Then, the basic model is fuzzified to construct an interval Type-2 fuzzy model (IT2FM) to characterize the uncertainty features of the modeling and achieve measurement detection of building wood. We conducted experiments using a wood measurement detection dataset to study the influence of the construction form of IT2FM on wood measurement detection, and evaluated the results using kappa values. The detection accuracy of this method is improved by about 10% compared to traditional machine learning and deep learning methods, and it only takes about 3-5 seconds to run the program. The experimental results show that the proposed method can effectively suppress the noise generated by wood measurement detection and prevent missed detection. It can achieve high-quality measurement detection of wood dead knots, loose knots, and cracked parts.

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Section: Introductionmentioning

confidence: 99%

A fast detection method for building wood measurement based on interval type-2 fuzzy model

Wang,

Xu,

Gui

et al. 2023

Meas. Sci. Technol.

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“…Demir et al [22] designed an ANN model to determine the CNC operating parameters (tool diameter, spindle speed, and feed) in order to attain the best surface quality for spruce and beech wood. Additionally, Cakmak et al [23] developed an artificial neural network model to predict the surface roughness and cutting power based on spindle speed, feed rate, depth of cut, and moisture content. Gürgen et al [24] developed an ANN model for prediction of surface roughness during processing of Scotch pine (Pinus sylvestris L.) on the CNC machine based on spindle speed, feed rate, depth of cut, and axial depth of cut.…”

Section: Introductionmentioning

confidence: 99%

Combining Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Operating Parameters of MDF Panels

Bedelean,

Ispas,

Răcășan

2023

Forests

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Most of the parts of furniture made of medium density fiberboards (MDF) require at least one hole to be assembled. The drilling technological parameters influence the quality of holes. Factors such as tip angle of the drill bit, feed rate, type and diameter of the drill bit, and spindle rotational speed could affect the drilling process. Therefore, the right choosing of drilling parameters is a mandatory condition to improve the drilling efficiency that is expressed through tool durability, cost, and quality of the drilling. Thus, in this work, we are proposed an approach that consists in combining two modelling techniques, which were successfully applied in various fields, namely artificial neural network (ANN) and response surface methodology (RSM), to analyze and optimize the drilling process of MDF boards. Four artificial neural network models with a reasonable accuracy were developed to predict the analyzed responses, namely delamination factor at inlet, delamination factor at outlet, thrust force, and drilling torque. These models were used to complete the experimental design that was requested by the RSM. The optimum values of the selected factors and their influence on the drilling process of the MDF boards were revealed. A part of optimum combinations among analyzed factors could be used both during the drilling of the MDF boards and prelaminated wood particleboards.

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A comprehensive assessment on surface quality of machined wooden products via Box-Behnken design method

Yaghoubi,

Rabiei,

Seidi

2023

Wood Material Science & Engineering

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Optimization of wood machining parameters using artificial neural network in CNC router

Cited by 6 publications

References 42 publications

A fast detection method for building wood measurement based on interval type-2 fuzzy model

A fast detection method for building wood measurement based on interval type-2 fuzzy model

Combining Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Operating Parameters of MDF Panels

A comprehensive assessment on surface quality of machined wooden products via Box-Behnken design method

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