Durability prediction of an ultra-large mining truck tire using an enhanced finite element method

Ultra-class mining trucks used for material haulage in rugged surface mining terrains experience premature tire fatigue failure in operation. Typical failures include belt edge separation, ply turn-up separation, and tread base and sidewall cracking. The use of reinforcing fillers and processing aids in tire compounds result in the formation of microstructural in-homogeneities in the compounds. This article presents an application of the critical plane analysis technique for predicting the fatigue life of the belt package of an ultra-large mining truck (CAT 795F) tire of size 56/80R63 in a surface coal mine. Experimental data obtained from extracted specimens (sidewall, tread, and belt edge region) of the tire are used to characterize the stress—strain and fatigue behavior of the modeled tire. The tire\u27s duty cycle stresses and strains were obtained from finite element analysis of the rolling tire in Abaqus. Fatigue life calculations were performed in the rubber fatigue solver Endurica CL. Effects of inflation pressure, tire speed, and axle load on the fatigue life of the belt package under strain-crystallizing and non-crystallizing conditions of the belt compound are discussed. Specifically, the results show the belt edges to be critical regarding crack nucleation

Interventions for acute stroke management in Africa: a systematic review of the evidence

Abstract Background The past decades have witnessed a rapid evolution of research on evidence-based acute stroke care interventions worldwide. Nonetheless, the evidence-to-practice gap in acute stroke care remains variable with slow and inconsistent uptake in low-middle income countries (LMICs). This review aims to identify and compare evidence-based acute stroke management interventions with alternative care on overall patient mortality and morbidity outcomes, functional independence, and length of hospital stay across Africa. Methods This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. An electronic search was conducted in six databases comprising MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, Academic Search Complete and Cochrane Library for experimental and non-experimental studies. Eligible studies were abstracted into evidence tables and their methodological quality appraised using the Joanna Briggs Institute checklist. Data were analysed and presented narratively with reference to observed differences in patient outcomes, reporting p values and confidence intervals for any possible relationship. Results Initially, 1896 articles were identified and 37 fully screened. Four non-experimental studies (three cohort and one case series studies) were included in the final review. One study focused on the clinical efficacy of a stroke unit whilst the remaining three reported on thrombolytic therapy. The results demonstrated a reduction in patient deaths attributed to stroke unit care and thrombolytic therapy. Thrombolytic therapy was also associated with reductions in symptomatic intracerebral haemorrhage (SICH). However, the limited eligible studies and methodological limitations compromised definitive conclusions on the extent of and level of efficacy of evidence-based acute stroke care interventions across Africa. Conclusion Evidence from this review confirms the widespread assertion of low applicability and uptake of evidence-based acute stroke care in LMICs. Despite the limited eligible studies, the overall positive patient outcomes following such interventions demonstrate the applicability and value of evidence-based acute stroke care interventions in Africa. Health policy attention is thus required to ensure widespread applicability of such interventions for improved patients’ outcomes. The review findings also emphasises the need for further research to unravel the reasons for low uptake. Systematic review registration PROSPERO CRD4201605156

An integrated approach to panel width, fleet size, and change-out time optimization in room-and-pillar mines

Optimization of panels, haulage fleet, and waiting area involves deterministic and low-fidelity methods and experiential knowledge. The process is challenging because coal recovery and operational capabilities must be considered in the solution. The approach in this manuscript comprises the development of an integrated stochastic simulation model of a coal room-and-pillar system that addresses these challenges. The decision variables evaluated are panel width, number of shuttle cars, and change-out time (COT). The results show that the mine should implement the shortest possible COT, decreasing the cycle time and thereby increasing productivity and continuous miner (CM) utilization. The highest productivity and CM utilization for a fleet size of three shuttle cars is found in the 15-entry panel width. For the evaluated fleet sizes, the 19-entry panel width is optimal for the four and five shuttle cars. Among the three variables studied, panel width and fleet size had the most significant effects (5% increase) on the CM productivity, cycle time, and utilization. © 2022 South African Institute of Mining and Metallurgy. All rights reserved.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Optimization of Mine Ventilation Networks Using the Weighted Augmented Lagrange Method

This paper presents an improved method of mine ventilation network optimization in the form of a standard nonlinear programming problem and discusses how the solution speed and accuracy depends on a weight factor introduced in the objective function term of the Lagrangian function. The nonlinear ventilation network problem is posed as one with only equality constraints. This is achieved through the use of slack variables. The methodology adopted in this paper is capable of dealing with the nonlinear convex model with significant savings on computational efforts due to its use of only first derivatives. A MATLAB program has been developed based on the weighted augmented Lagrangian method to optimize a generalized mine ventilation network in order to reduce the overall power consumption of the system. The results obtained with this proposed method matches well with previously published results of the same network problem with significant savings on computational time. It has been shown that a carefully selected weight factor can greatly improve the solution speed and accuracy

Optimisation of Mine Ventilation Networks Using the Lagrangian Algorithm for Equality Constraints

This work presents a new method of mine ventilation network optimisation as standard non-linear programming problem and discusses the use of a novel first-order Lagrangian (FOL) algorithm for equality constraints as a solution tool for these problems. Slack variables have been defined to transform such inequality constraints into their corresponding equality forms. The problem is then converted to non-linear problem with equality constrains. The methodology adopted in this paper is capable of dealing with the non-linear convex model with significant savings on computational efforts due to its use of only first derivatives. A MATLAB programme has been developed based on the FOL method to solve a generalised mine ventilation network optimisation problem. To study the validity and the viability of the FOL programme, the programme has been applied to already published network problems and both results are identical

A Computer Vision System for Terrain Recognition and Object Detection Tasks in Mining and Construction Environments

Recent studies towards dragline excavation efficiency have focused on incrementally achieving automation of the entire excavation cycle. Initial efforts resulted in the development of an automated dragline swing system, which optimizes the swing phase time. However, the system still requires human operation for collision avoidance. For full dragline autonomy, a machine vision system is needed for collision prevention and big rock handling during the \u27swinging\u27 and \u27digging\u27 phases of the excavation operation. Previous attempts in this area focused on collision avoidance vision models which estimated the location of the bucket in space in real-time. However, these previous models use image segmentation methods that are neither scalable nor multi-purpose. In this study, a scalable and multi-purpose vision model has been developed for draglines using Convolutional Neural Networks. This vision system averages 82.6% classification accuracy and 91% detection in collision avoidance. It also achieves an 87.32% detection rate in bucket pose estimation tasks. In addition, it averages 80.9% precision and 91.3% recall performance across terrain recognition and oversized rock detection tasks. With minimal modification, the proposed vision system can be adjusted for other automated excavators