title: Micro-mechanical predictive modelling as an aid to CAD based analysis of composite sporting equipment

authors: Ewart, Paul

subjects: CAD

abstract: The sport and leisure industry in New Zealand (NZ) has the potential to become a major user of composite materials. Given the size of NZ industry, design and manufacturing strategies based on virtual engineering should be developed to suit NZ requirements. Virtual methods use computer aided engineering capabilities to find faults, explore alternatives and optimise product performance before detailed design or prototyping. When doing computer aided simulation the required mechanical properties of individual reinforcement and matrix components are well documented. However, the mechanical properties of composite materials are not as simple to obtain. Micro-mechanical modelling could therefore be used to aid the design and development of composite equipment, where mechanical properties are unknown. In this study, solids modelling was used to produce an analog model of a composite, and it was found that it lead to reductions in file size and simulation time. Representing a composite with an analog model implies that the behavioural characteristics are modelled, but not the physical characteristics of the individual components. Three micro-mechanical models were developed to predict the flexural modulus of composite materials, based on perfect, partial and no adhesion. It was found that the partial adhesion model was both practical and consistently accurate. The partial adhesion model accounted for adhesion between components by considering an 'effective shear value' at the interface. Validation of the models was done by flexural testing injection moulded samples of glass, wood and carbon fibre reinforced polyethylene. It was shown that the adhesion coefficient range was 0.1 for carbon fibre, 0.5 for glass fibre and 0.9 for the wood fibre composites. It was concluded that the adhesion coefficient is crucial and it is recommended that further work is done to validate effective shear values by empirical means. The predicted flexural modulus values were used to enable finite element simulation of modelled analog beams as well as commercial kayak paddles. It was determined that accurate simulation is possible for composite equipment using the partial adhesion model.

text: Micro-mechanical predictive modelling as an aid to CAD based analysis of composite sporting equipment The sport and leisure industry in New Zealand (NZ) has the potential to become a major user of composite materials. Given the size of NZ industry, design and manufacturing strategies based on virtual engineering should be developed to suit NZ requirements. Virtual methods use computer aided engineering capabilities to find faults, explore alternatives and optimise product performance before detailed design or prototyping. When doing computer aided simulation the required mechanical properties of individual reinforcement and matrix components are well documented. However, the mechanical properties of composite materials are not as simple to obtain. Micro-mechanical modelling could therefore be used to aid the design and development of composite equipment, where mechanical properties are unknown. In this study, solids modelling was used to produce an analog model of a composite, and it was found that it lead to reductions in file size and simulation time. Representing a composite with an analog model implies that the behavioural characteristics are modelled, but not the physical characteristics of the individual components. Three micro-mechanical models were developed to predict the flexural modulus of composite materials, based on perfect, partial and no adhesion. It was found that the partial adhesion model was both practical and consistently accurate. The partial adhesion model accounted for adhesion between components by considering an 'effective shear value' at the interface. Validation of the models was done by flexural testing injection moulded samples of glass, wood and carbon fibre reinforced polyethylene. It was shown that the adhesion coefficient range was 0.1 for carbon fibre, 0.5 for glass fibre and 0.9 for the wood fibre composites. It was concluded that the adhesion coefficient is crucial and it is recommended that further work is done to validate effective shear values by empirical means. The predicted flexural modulus values were used to enable finite element simulation of modelled analog beams as well as commercial kayak paddles. It was determined that accurate simulation is possible for composite equipment using the partial adhesion model.

year: 2008

title: Naive Bayes for regression

authors: Frank, Eibe

subjects: computer science

abstract: Despite its simplicity, the naïve Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is categorical. It is therefore interesting to see how it performs in domains where the predicted value is numeric, because in this case, predictions are more sensitive to inaccurate probability estimates. 
This paper shows how to apply the naïve Bayes methodology to numeric prediction (i.e. regression) tasks, and compares it to linear regression, instance-based learning, and a method that produces “model trees” - decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naïve Bayes is the method of choice, on real-world datasets it is almost uniformly worse than model trees. The comparison with linear regression depends on the error measure: for one measure naïve Bayes performs similarly, for another it is worse. Compared to instance-based learning, it performs similarly with respect to both measures. These results indicate that the simplistic statistical assumption that naïve Bayes makes is indeed more restrictive for regression than for classification.

text: Naive Bayes for regression Despite its simplicity, the naïve Bayes learning scheme performs well on most classification tasks, and is often significantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is categorical. It is therefore interesting to see how it performs in domains where the predicted value is numeric, because in this case, predictions are more sensitive to inaccurate probability estimates. 
This paper shows how to apply the naïve Bayes methodology to numeric prediction (i.e. regression) tasks, and compares it to linear regression, instance-based learning, and a method that produces “model trees” - decision trees with linear regression functions at the leaves. Although we exhibit an artificial dataset for which naïve Bayes is the method of choice, on real-world datasets it is almost uniformly worse than model trees. The comparison with linear regression depends on the error measure: for one measure naïve Bayes performs similarly, for another it is worse. Compared to instance-based learning, it performs similarly with respect to both measures. These results indicate that the simplistic statistical assumption that naïve Bayes makes is indeed more restrictive for regression than for classification.

year: 1998-10

title: He aha te mea nui o te ao? He tāngata! (What is the most important thing in the world? It is people!)

authors: Masters-Awatere, Bridgette

subjects: chronic disease prevention

abstract: This paper highlights the importance of people as a central factor in improving health for M aori (Indigenous people of New Zealand). How wh anau (family) relationships, connections, values and inspiration are integral to achieving Indigenous health goals is explained. Descriptions of how community researchers, healthcare staff, consumers and academics worked together to design interventions for two health services (in the Waikato and Bay of Plenty regions) is included. Through highlighting the experiences of health consumers, the potential for future interventions to reduce the advancement of pre-diabetes among wh anau is described. Evidence from the study interviews reinforces the importance of wh anau and whakapapa (heritage) as enabling factors for Indigenous people to improve health. Specifically, the positive effect of wh anau enhancing activities that support peoples’ aspirations of tino rangatiratanga (self-determination) in their lives when engaging with health care has been observed. This study highlights the many positives that have emerged, and offers an opportunity for taking primary health to the next level by placing wh anau alongside Indigenous primary care providers at the centre of change strategies.

text: He aha te mea nui o te ao? He tāngata! (What is the most important thing in the world? It is people!) This paper highlights the importance of people as a central factor in improving health for M aori (Indigenous people of New Zealand). How wh anau (family) relationships, connections, values and inspiration are integral to achieving Indigenous health goals is explained. Descriptions of how community researchers, healthcare staff, consumers and academics worked together to design interventions for two health services (in the Waikato and Bay of Plenty regions) is included. Through highlighting the experiences of health consumers, the potential for future interventions to reduce the advancement of pre-diabetes among wh anau is described. Evidence from the study interviews reinforces the importance of wh anau and whakapapa (heritage) as enabling factors for Indigenous people to improve health. Specifically, the positive effect of wh anau enhancing activities that support peoples’ aspirations of tino rangatiratanga (self-determination) in their lives when engaging with health care has been observed. This study highlights the many positives that have emerged, and offers an opportunity for taking primary health to the next level by placing wh anau alongside Indigenous primary care providers at the centre of change strategies.

year: 2019