Development of hardfaced crusher jaws using ferro-alloy hardfacing inserts and low carbon steel substrate

Procedures for producing hardfaced jaws using ferro-alloy hardfacing inserts and low carbon steel substrate have been established. This research was prompted by the need to provide an economical substitute to the high manganese austenitic steel that has dominated the mining and minerals industries since its invention by Sir Robert Hadfield in 1882. In this research, ferro-alloy hardfacing inserts of composition 72.80 %Fe. 12.32 %Mn, 7.38 %Cr were produced by sand casting. The inserts were welded to low carbon steel substrate by manual metal arc-welding to form the hardfaced jaws. Welding was carried out intermittently using E6013 gauge 10 oerlikon electrode and a current of 100 A. The fixed and movable jaws lost 0.3 and 0.1 kg, respectively, after crushing 400 kg of granite in the jaw crusher. Hardness values and wear volumes of the hardfacing insert and workhardened Hadfield steel were 653 HV, 0.0069 cm3 and 517 HV, 0.017 cm3, respectively. Cost of producing the hardfaced jaws was five times cheaper than the estimated cost of producing Hadfield steel jaws. Consequently, the hardfaced jaws can economically substitute the Hadfield steel jaws in jaw crushers

Studies of the properties of heat treated rolled medium carbon steel

Investigations were carried out to study critically the effects of heat treatment on the properties of rolled medium carbon steel. Representative samples of as-rolled medium carbon steel were subjected to heat treatment processes which are; Quenching, Lamellae Formation and Tempering in the following order (Q + Q + L + T), (Q + L + T) and (L + T). The steel was heated to the austenizing temperature of 830 ºC and water quenched. The quenched steel was subjected to lamellae formation by reheating it to the ferrite-austenite dual-phase region at a temperature of 745 ºC below the effective A C3 point and then rapidly quenched in water. The lamellae formed was tempered at 480 ºC to provide an alloy containing strong, tough and lath martensite in a soft and ductile ferrite matrix. Mechanical tests were carried out on the samples and the results shows that the steel developed has excellent combination of tensile strength, hardness and impact strength which is very good for structural applications. The corrosion behaviour of the samples; heat treated rolled medium carbon steel and as-rolled medium carbon steel in sodium chloride medium were also investigated from where it was also confirmed that improved corrosion resistance is achievable by the treatment

Development of hardfaced crusher jaws using ferro-alloy hardfacing inserts and low carbon steel substrate

The dynamic behavior of a rotordynamic system is greatly affected by the dynamic performance of aerodynamic bearing and the performance of bearing is characterized by the stiffness and damping coefficient of bearing. In the present work, stiffness and damping coefficient of bearing are computed and the performance of these bearing is greatly changed with the change in bearing air film profile. In the present work, the effect of lobe offset factor on the transient performance of aerodynamic bearing is presented. Bifurcation and Poincare diagram of two lobe journal bearing has been presented for different offset factor. A bearing designer can judge a bearing performance on the basis of bifurcation diagrams

A review on the influence of natural-synthetic fibre hybrid reinforced polymer composites for bulletproof and ballistic applications

The past two decades have witnessed increased research in natural fibre polymer composites due to their low cost and environmental friendliness over synthetic counterparts. This has been further advanced by the global circular economy drive stressing on materials sustainability in production process. Hybridization technique has proved successful in enhancing the functional performance of natural fibre composites for advanced bulletproof and ballistic body armors applications. Laminate thickness, layering sequence, fibre loading and weaving architecture influence the ballistic performance of natural/synthetic fibre hybrid composites. Literature shows an increasing trend in research studies in natural/synthetic hybrid composites in the last twenty years to address the challenge of fibre/matrix incompatibility causing lower properties. Appropriate stacking sequence and incorporation of small quantities of nanofillers improve ballistic properties of natural fibre-based composites equivalent to synthetic-based counterparts. This paper reviews the influence of this novel class of composite materials for ballistic and bulletproof applications from 2001 to 2021

Fabrication of animal shell and sugarcane bagasse particulate hybrid reinforced epoxy composites for structural applications

This study investigated the effects of using egg and snail shells, along with sugarcane bagasse, on various properties of hybrid reinforced epoxy composites for structural applications. The particulate shells and sugarcane bagasse serve as reinforcements while the matrix consists of epoxy resin and hardener. The composites were produced using the hand lay-up technique, and the mechanical, wear and physical properties of the prepared samples were evaluated. The fractured surfaces of the samples were examined using a scanning electron microscope. The results revealed that the source of the shell had an impact on the properties of the composites as eggshell-sugarcane bagasse particulate reinforced epoxy composites exhibited improved strengths, while snail shell-sugarcane bagasse particulate reinforced epoxy composites showed improved moduli. Optimal values were obtained for flexural and tensile strengths at 15 and 18 wt%, respectively, while flexural and tensile moduli were optimal at 12 and 15 wt%, respectively. Eggshell-sugarcane bagasse particulate reinforced epoxy composites demonstrated an optimal impact strength value of 21.81 J/m2, while snail shell-sugarcane bagasse particulate reinforced epoxy composites showed optimal results in all other properties mostly at 20 wt%. Conclusively, the use of snail shell-sugarcane bagasse particles was found to be more effective than eggshell-sugarcane bagasse particles for enhancing the properties of epoxy-based composites for structural applications while particulate reinforcement content within the range of 12–20 wt% are responsible for optimum performances

Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended

Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended