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

State of science: refitting the human to nature

Environmental institutions are realising that the human-nature relationship is a tangible target for a sustainable future. Societal change of that relationship is a challenge involving modifications to both systems and human behaviours. We argue that as Human Factors and Ergonomics (HFE) focusses on relationships, interfaces and systems it is well placed to contribute. After introducing the state of HFE and nature connectedness science an analysis of areas of HFE and human-nature connectedness themes is used to consider current work and future opportunities. We conclude that despite decades-old calls to action, HFE is embedded in a dated paradigm and has had little positive contribution to the human-nature relationship. However, HFE is well placed to create sustainable communities, designed to create a new relationship with nature. To do this, HFE needs to recognise that it should move on from solely fitting the task to the human, to refitting the human to nature.Practitioner Summary: A more sustainable human-nature relationship can be achieved through applying HFE approaches. HFE expertise in human characteristics, systems, people and technology can be applied at differing scales with various social-economic and technical factors to address key themes in our failing relationship with nature.Abbreviations: HFE: Human Factors and Ergonomics; IPBES: Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; STAMP: System-Theoretic Accident Model and Processes; CWA: Cognitive Work Analysis; NET-HARMS: NETworked Hazard Analysis and Risk Management System; NbS: Nature-based System.

Biomechanical analysis of wood processing work in tropical forest regions: A study in Midwest Brazil

In the wood processing industry, working with machines exposes workers to occupational risks. This study evaluates the biomechanics of work activities carried out in a wood processing operation in tropical forest regions to identify risks associated with the development of musculoskeletal injuries. Data were collected in a wood processing industry from a tropical forest region in Midwest Brazil. Forty forest workers were evaluated, equivalent to 100% of the population of the company participating in the study. Biomechanical movements were measured using the Three-Dimensional Biomechanical Model for Predicting Stances and Static Forces. There was a risk of injury to the joints of the upper limbs (wrists and shoulders), as well as to the torso. When splitting and manually stacking wood, a risk of spinal injury was identified. All workers had a high risk of developing injuries in all joints evaluated in addition to a risk of serious spinal injury. In this way, the development of these activities without any ergonomic interventions makes the risk of developing musculoskeletal injuries in the workers involved imminent, with consequent early professional incapacity.

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

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.

Effect of Sandblasting on Static and Fatigue Strength of Flash Butt Welded 75Cr4 Bandsaw Blades

The aim of the research presented in this article is analysis of the effect of the surface treatment method on the static and fatigue strength of flash butt welded bandsaw blades. A 1-mm-thick 75Cr1 cold-work tool steel sheet used for bandsaw blades was used as the test material. Fractographic studies of the fatigue fractures and fractures formed in static tests were also carried out. The static strength tests showed sandblasting the weld surface had no significant effect on the load capacity of the joint. However, the sandblasted specimens showed a higher repeatability of the load capacity (lower standard deviation). In the case of both analyzed sample variants of specimens, sandblasted and non-sandblasted, the number of cycles at which the sample was damaged decreases with the percentage increase of the stress amplitude. When loading the samples with a stress amplitude value in the range between 400 and 690 MPa, sandblasting of the weld surface increased the average value of destructive cycles by about 10-86% (depending on the stress amplitude) compared to non-sandblasted joints. The sandblasting process introduces compressive stresses in the surface layer of the welds, therefore the variable tensile load acting on the sample requires a greater number of cycles before the fatigue cracks initiate and propagate. In the case of all specimens, a ductile fracture was observed. It was also found that, regardless of the variable stress amplitude, sandblasting has a positive effect on reducing the standard deviation of fatigue test results.