Comparative Analysis of On-Site Free-Hand Chainsaw Milling and Fixed Site Mini-Bandsaw Milling of Smallholder Timber

Cedamon, Edwin; Harrison, Steve; Herbohn, John

doi:10.1007/s11842-012-9218-y

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…As found by this study, the factors affecting the operational performance were those generally reported in similar studies, describing significant dependence relations between the parameters characterizing the dimensions of the inputs and the time consumption (e.g., [42][43][44][45]), which in turn affect the variation in productivity and efficiency. Such model parameters estimated by this study are confirmed by their counterparts found for similar machine types and sizes (e.g., [42][43][44]). In this regard, however, one of the merits of this study is that it maps the relations between the inputs, resources used, and outputs, by a system-centered perspective, providing detailed models characterizing such dependence relations.…”

Section: Discussionsupporting

confidence: 66%

Performance of Small-Scale Sawmilling Operations: A Case Study on Time Consumption, Productivity and Main Ergonomics for a Manually Driven Bandsaw

Borz

Oghnoum

Marcu

et al. 2021

Forests

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Sawmilling operations represent one of the most important phases of the wood supply chain, because they connect the conversion flow of raw materials into finite products. In order to maintain a high volume of processed wood, sawmills usually adopt different processing strategies in terms of equipment and methods, which can increase the value or volume of the lumber produced from logs. In this study, the performance of small-scale sawmilling operations was monitored, whilst also evaluating the exposure of workers to harmful factors. An assessment of time consumption, productivity, and main ergonomics was conducted during the use of a manually driven bandsaw. In addition, the exposure to noise was investigated to complement the knowledge in this regard. The results indicated a rather high time utilization in productive tasks, which may come at the expense of exposure to noise and to poor working postures. The modelling approach resulted in statistically significant time consumption models for different phases (blade adjustment, effective sawing, returning, unloading lumber, and loading and fixing lumber). The exposure to noise was close to 92 dB (A) (8 h) and, therefore, the level of emitted noise is likely to depend on the condition of the used blades, species sawn and on the dimensional characteristics of the logs. In terms of ergonomic risks, the poorest postures were those related to tasks such as moving the logs, loading the logs, fixing the logs, rotating and removing the logs, as well as unloading the lumber.

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Section: Discussionsupporting

confidence: 66%

Performance of Small-Scale Sawmilling Operations: A Case Study on Time Consumption, Productivity and Main Ergonomics for a Manually Driven Bandsaw

Borz

Oghnoum

Marcu

et al. 2021

Forests

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“…An example of this is when farmers sell standing trees and logs to sawmillers. When timber volumes are low and tree farms are small and fragmented in the landscape, free-hand chainsaw milling is an operationally suitable technology (Cedamon et al 2013). Portable sawmills are another technological option for primary processing on site.…”

Section: Processingmentioning

confidence: 99%

“…These technologies have considerable advantages such as low capital outlays and mobility for millers. However, sawn timber recovery rates tend to be lower, and this is further aggravated by unskilled operators who are also under high risk of injury (Cedamon et al 2013;Dugan 2007). Hence, adequate human capital is crucial for processing, whether it be stationary mills or mobile technologies.…”

Section: Processingmentioning

confidence: 99%

Conditions that Enable Successful Participation of Smallholder Tree Growers in Timber Value Chains

Vega

Page

2023

Small-scale Forestry

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Recent research identified twenty conditions that enable the participation of smallholder tree growers in timber value chains. This paper builds on that work. It delves deeper into how and when during the plantation investment cycle these conditions facilitate sustained trade between small-scale timber producers, processors, buyers, and other service providers. It also identifies the stakeholders who are in the best position to influence these conditions. Conditions that affect access to smallholder timber such as biophysical characteristics of the land, characteristics of the plots where trees are planted, and infrastructure have important implications for trading partners. A buyer’s willingness to purchase smallholder timber also depends on timber quality and quantity, and reliability of supply. These are affected by conditions such as the availability of good genetic material, biophysical characteristics of land, and application of the right silvicultural practices. Building a viable value chain involving smallholder tree growers depends on matching the capabilities and aspirations of smallholders with the requirements of processors and buyers. Opportunities for smallholder tree growers to benefit from growing timber demand are on the rise, but successful engagement of small-scale timber producers with these markets will depend on how well all value chain agents connect and understand each other’s needs.

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“…On the opposite side, there are the small to medium-sized sawmilling facilities characterized by low processing capacities [2] and, typically, by the absence of automation [3,4]. With some exceptions, these use rather obsolete technology, are operated by a small number of workers and do not hold production planning and management capabilities [5][6][7][8]. Still, they support local economies by providing added value and by diversification in opportunities for employment, while also complementing the sawmilling capabilities of a region to valorize wood assortments which are less demanded by large processing facilities [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, concepts of using different kinds of sensors to automate the extraction of useful data were described for rather more complicated operations and equipment [15,16]. In relation to sawmilling operations, the beginning of operational monitoring was most likely characterized by the use of manual solutions based on the concepts of traditional time-and-motion studies [17,18], which aimed at characterizing the productive performance in small-to-medium sawmilling operations of various configurations [3,[5][6][7][8]19]. However, these became less practical to implement due to the resources spent to collect and process large amounts of data [20], the reluctance of operators when facing observation, and the capability limits of the observers [21]; in addition, manual data collection procedures hold a limited ability to capture the pattern in operational performance over a long term, restricting the applicability of the developed statistics in a range of variations from which they were built [18].…”

Section: Introductionmentioning

confidence: 99%

Development of a Robust Machine Learning Model to Monitor the Operational Performance of Fixed-Post Multi-Blade Vertical Sawing Machines

Borz

Forkuo

Oprea-Sorescu

et al. 2022

Forests

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Monitoring the operational performance of the sawmilling industry has become important for many applications including strategic and tactical planning. Small-scale sawmilling facilities do not hold automatic production management capabilities mainly due to using obsolete technology which is an effect of low financial capacity and focus their strategy on increasing value recovery and saving resources and energy. Based on triaxial acceleration data collected over five days at a sampling rate of 1 Hz, a robust machine learning model was developed with the purpose of using it to infer the operational events based on lower sampling rates adopted as a strategy to collect long-term data. Among its performance metrics, the model was characterized in its training phase by a very high overall classification accuracy (CA = 98.7%), F1 score (98.4%) and a very low error rate (LOG LOSS = 5.6%). For a three-class problem, it worked very well in classifying the main events related to the operation of the machine, with active work being characterized by an F1 score of 99.6% and an error of 3.6%. By accounting for the same metrics, the model was proven to be invariant to the sampling rates of up to 0.05 Hz (20 s) and produced even better results in the testing phase (CA = 98.9%, F1 = 98.6%, LOG LOSS = 5.5%, for a testing sample extracted at 0.05 Hz), while there were no differences in the share of class data irrespective of the sampling rate. The developed model not only preserves a high classification performance in the training and testing phases but it also seems to be invariant to lower sampling rates, making it useful for prediction over data collected at low sampling rates. In turn, this would enable the use of cheap data collectors to be operated for extended periods of time in various locations and will save human resources and money associated with data collection. Further tests would be required only for validation and they could be supported by collecting and feeding new data to the model to infer the long-term performance of similar sawmilling machines.

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Comparative Analysis of On-Site Free-Hand Chainsaw Milling and Fixed Site Mini-Bandsaw Milling of Smallholder Timber

Cited by 7 publications

References 9 publications

Performance of Small-Scale Sawmilling Operations: A Case Study on Time Consumption, Productivity and Main Ergonomics for a Manually Driven Bandsaw

Performance of Small-Scale Sawmilling Operations: A Case Study on Time Consumption, Productivity and Main Ergonomics for a Manually Driven Bandsaw

Conditions that Enable Successful Participation of Smallholder Tree Growers in Timber Value Chains

Development of a Robust Machine Learning Model to Monitor the Operational Performance of Fixed-Post Multi-Blade Vertical Sawing Machines

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