Automation the Processes of Wood Processing by Drilling, Due to the Development a Mathematical Apparatus for Accounting the Interrelated Feed Drive and Drive of Cutting

Lapshin, V. P.; Туркин, И. А.; Omelechko, V. U.

doi:10.1007/978-3-030-94202-1_2

Cited by 1 publication

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“…In addition, the artificial neural network (ANN) modeling technique was, also, applied to predict the various outputs of the drilling process. Nowadays, ANN models are included in various decision support systems that are developed for the forest industry [10].…”

Section: Introductionmentioning

confidence: 99%

Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood

Bedelean,

Ispas,

Răcășan

2023

Applied Sciences

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Plywood is a wood-based composite with many applications in construction, shipbuilding, and furniture production. One of the basic plywood processing and mandatory operations is drilling. Up to now, considerable and very diverse thematic research has been recently carried out on drilling, but little of that deals with modeling of the drilling process of plywood. Therefore, in this work, the artificial neural network modeling technique and response surface methodology were applied to model and optimize the drilling process of plywood. Two artificial neural network models were developed to predict the thrust force and the drilling torque based on drill tip angle, tooth bite, and drill type. The developed ANN models were used to complete the value of responses in the experimental design, which was requested by the response surface methodology. The trust force during the drilling of plywood is significantly influenced by the drill type (helical or flat). The most significant factor that affects the drilling torque during the drilling of plywood is the tooth bite. A helical drill assures a lower minimum thrust force and drilling torque than a flat drill. The proposed method could be used as an optimization tool during the design phase of the furniture manufacturing process.

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Section: Introductionmentioning

confidence: 99%