9 research outputs found
Material design factors in the additive manufacturing of Carbon Fiber Reinforced Plastic Composites:A state-of-the-art review
Finite element model of fiber volume effect on the mechanical performance of additively manufactured carbon fiber reinforced plastic composites
Mechanical characterisation and crashworthiness performance of additively manufactured polymer-based honeycomb structures under in-plane quasi-static loading
ABSTRACTAdditive manufacturing technology is suitable for producing energy-absorbing devices with tunable mechanical properties and improved crashworthiness performance. In this study, the mechanical properties and macrostructural crushing behaviour of five additively manufactured polymer-based honeycomb structures (HS) are investigated. Subjected to in-plane loading, the experimental results of the HS are compared with numerical findings and theoretical predictions. Results indicate that deformation modes and overall crushing performance are influenced by utilising different parent materials. The polymer HS made from polyethylene terephthalate glycol gives the best overall crushing performance over the other polymers and polymer-fibre reinforcement HS. However, the crush force efficiency of HS made from polylactic acid is the least promising. The polymer-fibre reinforced HS outperforms some of the pure polymer-based ones in terms of specific energy absorption and shows a characteristic lightweight advantage. Hence, spotting it as a promising energy absorber utilised for crashworthiness application especially where ultra-lightweight property is highly desired
A machine learning approach to characterise fabrication porosity effects on the mechanical properties of additively manufactured thermoplastic composites
The investigation of the mechanical properties of additively manufactured (AM) composite has been the focus of several research over the past decades. However, testing constraints of time and cost have encouraged the exploration of more pragmatic methods such as machine learning (ML) for predicting these characteristics. This study builds on experimental investigations of the flexural, tensile, compressive, porosity, and hardness properties of 3D printed carbon fibre-reinforced polyamide (CF-PA) and carbon fibre-reinforced acrylonitrile butadiene styrene (CF-ABS) composites, proposing the application of ML for predicting these mechanical properties. A comprehensive comparative analysis of various machine learning approaches was executed, with a resultant accuracy ranging between 80 and 99%. The results unveiled the superior predictive performance of ensemble tree learners and the K-NN regressor algorithms when temperature and porosity are selected (based on correlation analysis) as predictors for material hardness and strength in tension, compression, and flexion. In particular, the model built on the extra-tree regressor algorithm demonstrated a remarkably robust fit, with R-squared evaluation scores of 0.9993 and 0.9996 for CF-PA and CF-ABS, respectively. This work develops a ML model that relates porosity to the other mechanical properties of AM composites and the prediction models’ exceptional accuracy, along with their precise alignment with experimental data, provide invaluable insights for the autonomous control and data-driven optimization of the structures
GENDER ANALYSIS: INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) ADOPTION AMONG RURAL FARM DWELLERS IN OYO STATE, NIGERIA
The high illiteracy level in most rural communities of Nigeria is not representative of positive impacts that Information and Communication Technologies (ICTs) have had on Nigerian rural agriculture in terms of productivity, growth and development. The study investigated the adoption of ICTs among rural farm dwellers in Iseyin Local Government Area of Oyo state, Nigeria, using gender analysis. A well-structured interview schedule was employed to elicit quantitative information from 60 male and 60 female rural farm dwellers from Serafu, Ado-awaye and Osoogun in Iseyin Local Government Areas, using the two stage random sampling procedure. Results showed that 55% of the male respondents and 46.7% of the female respondents adopted various selected ICTs in general. Analysis of Variance (ANOVA) at <0.05 level of significance resulted that a significant difference exists in the adoption of the ICTs of both male and female respondents (F= 4.198). The findings revealed that at <0.05 level of significance, significant difference existed in the level of income of the male and female respondents (F= 4.079). The study revealed that male farmers adopted ICTs more than their female counterparts, and there were significant gender gaps in ICT adoption among rural farm dwellers in Nigeria. Therefore, in order to bridge the gender gap in the level of adoption of ICTs, among rural dwellers, it was recommended that various ICTs should be made available for rural dwellers at subsidized rate because larger percentage of them are aware of the importance and benefits of ICTs but lack the financial capacity to acquire them. The government should formulate policy that will enhance continuous development, dissemination, and utilization of gender responsive ICTs for farming activities and for development of rural livelihood, in Nigeria
Entrepreneurial Opportunities For Additive Manufacturing Technologies in Sub-Saharan Africa
Birth Preparedness and Complication Readiness among Women Attending Antenatal Clinics in Ogbomoso, South West, Nigeria
Background: Information on factors associated with birth preparedness and complication readiness (BP/CR) is central in designing cost effective programs for reducing maternal deaths among women. This study assessed factors influencing BP/CR among pregnant women attending antenatal clinic in Ogbomoso, South West Nigeria.
Methodology: This is a cross-sectional study conducted between January and April, 2015. Systematic sampling technique was employed to recruit 400 women attending antenatal clinic at Bowen University Teaching Hospital, Ogbomoso, Nigeria. A pre-tested questionnaire was used for data collection and data analysis was done using SPSS version 21. Chi-square test was used for bivariate analysis while binary logistic regression was used for multivariate analysis. Statistical significance was set at p <0.05.
Results: More than half (51.3%) of our respondents were in the 30-39 age category. Only 40.3% of these respondents were reported well prepared for births and were complication ready. The proportion of women who had BP/CR was significantly higher among those in the middle socio-economic group (51.6%, p<0.05), those who practiced Christianity (76.4%, p<0.05) and those from Yoruba ethnic group (80.1%, p<0.05). Respondents in lower socio-economic group were 42% less likely to have prepared for birth compared to women in the high socio-economic class (OR: 0.58, 95% CI: 0.34-0.99).
Conclusion and Global Health Implications: The proportion of Nigerian women in our sample who were well-prepared for birth and its complication was below average. There is need for more awareness programs on BP/CR; such programs should target all women especially the vulnerable grou