Woodworking Facilities: Driving Efficiency through Automation Applied to Major Process Steps

Gasparyan, G.D.; Kunickaya, Olga; Grigorev, Igor; Ivanov, Viktor; Burmistrova, Olga; Мануковский, А. Ю.; Zhuk, Artem; Hertz, Edward; Кремлева, Л. В.; Mueller, Oscar

doi:10.14419/ijet.v7i4.7.23032

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…To get immediate updates on the quality of chipped material and to automate a debarking process, participants of the scientific school "Innovations in Lumber Production and Forestry" have designed a new production line for the generation of high-quality wood chips (Grigoriev et al 2014;Gasparyan et al 2018).…”

Section: Methodsmentioning

confidence: 99%

Improving the quality of sorting wood chips by scanning and machine vision technology

Grigorev¹,

Shadrin²,

Katkov³

et al. 2021

J. For. Sci.

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Improving the quality of sorting wood waste is the main problem in the timber industry from the point of view of saving energy resources and preserving the environment, associated with the intensity of forest harvesting. Depending on the required quality characteristics, the sorting of wood chips makes it possible to determine their further use in production or utilization as a fuel. This paper presents the results of the development of a novel approach to sorting wood chips on a conveyor belt using machine learning and scanning technology. The proposed methodology includes functions to analyze the fractional size distribution among wood chips and rot detection. It shows that once a defective unit is detected, the quality control system will automatically remove it from the conveyor belt while it is moving. The minimization of wood waste will reduce logging intensity and increase the profitability of lumber enterprises.

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Section: Methodsmentioning

confidence: 99%

Improving the quality of sorting wood chips by scanning and machine vision technology

Grigorev¹,

Shadrin²,

Katkov³

et al. 2021

J. For. Sci.

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“…The output of a production line depends on the amount of waste that comes from each manufacturing operation. It can be measured by the following formula (Gasparyan et al 2018):…”

Section: Methodsmentioning

confidence: 99%

“…The output of a production line depends on the amount of waste that comes from each manufacturing operation. It can be measured by the following formula (Gasparyan et al 2018): where: Q ch 1 – first-chipping output; Q ch 2 – second-chipping output; К 1 and К 2 – first- and second-chipping output factors, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Given the large volumes of forest biomass passing through these locations and often long-term storage period, landing areas and yards are supported by high-performance specialized equipment like motion and laser sensors to ensure favourable conditions for complex mechanization and partial automation of processing operations (Gasparyan et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Russian sawmill modernization (a case study). Part 1: optimizing processes of low-grade timber debarking and wood chipping

Kunickaya

Burmistrova

Hertz

et al. 2020

International Wood Products Journal

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This paper offers a new technological solution for low-quality log chipping that enables sawmills to produce wood chips efficiently and with less waste. The proposed solution for sorting wood chips helps to solve a number of urgent problems crucial to log processing. Intellectualization of sorting operations not only minimizes wood residues, but also enables the maintenance of equipment. Modifications in the chipping system, namely laser sensors and a microcontroller, help operators detect debarking defects and perform the cutting tool wear monitoring. The results of the study show that a control system intellectualization allows an efficient use of equipment and thereby serves to improve production efficiency. This study primarily concentrates on coniferous logs. The technology developed in this study is applicable to log processing equipment and can be used in debarking machines at other mills.

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“…The technological cycle of wood production assumes a large amount of wood residues during main production (sawdust, cuttings, bark, limbs, etc.). The volume of wastes by logging and woodworking is much more than 50% of the total volume of harvested or processed wood [1]. By wood harvesting, main production wastes include the tops of trees, brushwood, branch wood, butts, and debris.…”

Section: Introductionmentioning

confidence: 99%

Reducing energy consumption of barkwood residue grinding on equipment with knife-based operational units

Kunickaya¹,

Zhuk²,

Nikiforova³

et al. 2020

J Appl Eng Science

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In the coming decades, wood waste management for biofuel production is regarded as a promising renewable energy source and a key factor in reducing carbon dioxide emissions. Mechanical grinding is seen as one of the main techniques in wood waste pre-treatment operations that increases the value of feedstock used for fuel. The application potential of the ground product highly depends on the energy effi ciency of the process.This work aimed to establish a consistent pattern for estimating the energy consumption required for grinding spruce and pine barking waste depending on the degree to which materials are ground and their relative moisture content. The energy consumption parameters at grinding were analyzed employing three grinding energy models of Rittinger, Kripichev-Kik, and Bond. The results of estimation showed that specifi c energy consumption is associated with relative moisture content and the grinding degree by nonlinear dependence according to the Kripichev-Kik grinding model for spruce and pine bark. It has been established that the specifi c energy consumptionat grinding spruce and pine barking waste at the optimum humidity of 25% and 27%, respectively, is proportional to the natural logarithm of the grinding degree. It was concluded that the wood waste grinding by 5-15 times requires higher energy consumption at optimum moisture content, which is 5-10% and 7-14% of the heating value for spruce and pine, respectively. The knowledge acquired through this research will contribute to developing possible approaches for wood waste recycling in a more energy-effi cient way.

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Woodworking Facilities: Driving Efficiency through Automation Applied to Major Process Steps

Cited by 7 publications

References 6 publications

Improving the quality of sorting wood chips by scanning and machine vision technology

Improving the quality of sorting wood chips by scanning and machine vision technology

Russian sawmill modernization (a case study). Part 1: optimizing processes of low-grade timber debarking and wood chipping

Reducing energy consumption of barkwood residue grinding on equipment with knife-based operational units

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