An effective sensor for tool wear monitoring in face milling : acoustic emmision

Acoustic Emission (AE) has been widely used for monitoring manufacturing processes particularly those involving metal cutting. Monitoring the condition of the cutting tool in the machining process is very important since tool condition will affect the part size, quality and an unexpected tool failure may damage the tool, work-piece and sometimes the machine tool itself. AE can be effectively used for tool condition monitoring applications because the emissions from process changes like tool wear, chip formation i.e. plastic deformation, etc. can be directly related to the mechanics of the process. Also AE can very effectively respond to changes like tool fracture, tool chipping, etc. when compared to cutting force and since the frequency range is much higher than that of machine vibrations and environmental noises, a relatively uncontaminated signal can be obtained. AE signal analysis was applied for sensing tool wear in face milling operations. Cutting tests were carried out on a vertical milling machine. Tests were carried out for a given cutting condition, using single insert, two inserts (adjacent and opposite) and three inserts in the cutter. AE signal parameters like ring down count and rms voltage were measured and were correlated with flank wear values (VB max). The results of this investigation indicate that AE can be effectively used for monitoring tool wear in face milling operations.Fundação para a Ciência e a Tecnologia (FCT

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model

Exploring the Link between Teachers’ Complementary Teaching Attributes and Students’ Academic Performance

Teachers’ pedagogical attributes have affected learning achievement and performance. This study examined the relationship between teachers’ complementary teaching attributes i.e. teaching styles and multiple bits of intelligence and the academic performance of the students leading to a basis for a purposive integration of pedagogical innovation in teacher education curriculum. The descriptive- correlational type of research was utilized in this study. The universal sampling technique was employed for the student- respondents while simple random sampling was used in the selection of the teacher-respondents. Data were gathered through the use of standardized questionnaires. The academic performance was determined by looking at the students’ term grade-point average. Based on the result, four (4) of the teaching styles significantly correlate with the academic performance of the students namely expert, formal authority, facilitator, and delegator. However, a negative correlation was noted between the academic performance and three of the teaching styles, i.e., expert, formal authority, and delegator. All of the multiple intelligence significantly correlates with students’ academic performance. Those above further suggest the appropriate inclusion of teaching styles in pedagogical platform integrating teachers multiple bits of intelligence to achieve an effective educational process.   Keywords - Education, teaching styles, multiple bits of intelligence, academic performance, descriptive – correlational research, Philippines</jats:p

Genetic Incorporation of a Small, Environmentally Sensitive, Fluorescent Probe into Proteins in <i>Saccharomyces cerevisiae</i>

Here, we report that the fluorescent amino acid, 3-(6-acetylnaphthalen-2-ylamino)-2-aminopropanoic acid (Anap), can be genetically incorporated into proteins in yeast with excellent selectivity and efficiency by means of an orthogonal tRNA/aminoacyl-tRNA synthetase pair. This small, environmentally sensitive fluorophore was site-specifically incorporated into Escherichia coli glutamine binding protein and used to directly probe local structural changes caused by ligand binding. The small size of Anap and the ability to introduce it by simple mutagenesis at defined sites in the proteome make it a useful local probe of protein structure, molecular interactions, protein folding, and localization