Use of Data Mining to Support the Development of Knowledge Intensive CAD

In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.

Prediction of Henry Constants and Adsorption Mechanism of Volatile Organic Compounds on Multi-Walled Carbon Nanotubes by Using Support Vector Regression

Support vector regression （SVR） combined with particle swarm optimization for its parameter optimization is employed to establish a model for predicting the Henry constants of multi-walled carbon nanotubes （MWNTs） for adsorption of volatile organic compounds （VOCs）. The prediction performance of SVR is compared with those of the model of theoretical linear salvation energy relationship （TLSER）. By using leave-one-out cross validation of SVR test Henry constants for adsorption of 35 VOCs on MWNTs, the root mean square error is 0.080, the mean absolute percentage error is only 1.19~, and the correlation coefficient （R2） is as high as 0.997. Compared with the results of the TLSER model, it is shown that the estimated errors by SVR are ali smaller than those achieved by TLSER. It reveals that the generalization ability of SVR is superior to that of the TLSER model Meanwhile, multifactor analysis is adopted for investigation of the influences of each molecular structure descriptor on the Henry constants. According to the TLSER model, the adsorption mechanism of adsorption of carbon nanotubes of VOCs is mainly a result of van der Waals and interactions of hydrogen bonds. These can provide the theoretical support for the application of carbon nanotube adsorption of VOCs and can make up for the lack of experimental data.

Strength failure of spatial reticulated structures under multi-support excitation

Under strong earthquakes, long-span spatial latticed structures may collapse due to dynamic instability or strength failure. The elasto-plastic dynamic behaviors of three spatial latticed structures, including two double-layer cylindrical shells and one spherical shell constructed for the 2008 Olympic Games in Beijing, were quantitatively examined under multi-support excitation （MSE） and uniform support excitation （USE）. In the numerical analyses, several important parameters were investigated such as the peak acceleration and displacement responses at key joints, the number and distribution of plastic members, and the deformation of the shell at the moment of collapse. Analysis results reveal the features and the failure mechanism of the spatial latticed structures under MSE and USE. In both scenarios, the double-layer reticulated shell collapses in the ＂overflow＂ mode, and the collapse is governed by the number of invalid plastic members rather than the total number of plastic members, beginning with damage to some of the local regions near the supports. By comparing the numbers and distributions of the plastic members under MSE to those under USE, it was observed that the plastic members spread more sufficiently and the internal forces are more uniform under MSE, especially in cases of lower apparent velocities in soils. Due to the effects of pseudo-static displacement, the stresses in the members near the supports under MSE are higher than those under USE.

Monitoring the Heavy Element of Cr in Agricultural Soils Using a Mobile Laser-Induced Breakdown Spectroscopy System with Support Vector Machine

Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method： one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression （SVR） model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.

An Architecture of Computer Aided Process Planning System Integrated with Scheduling Using Decision Support System

Process planning and scheduling are two major plann in g and control activities that consume significant part of the lead-time, theref ore all attempts are being made to reduce lead-time by automating them. Compute r Aided Process Planning (CAPP) is a step in this direction. Most of the existin g CAPP systems do not consider scheduling while generating a process plan. Sched uling is done separately after the process plan has been generated and therefore , it is possible that a process plan so generated is either not optimal or feasi ble from scheduling point of view. As process plans are generated without consid eration of job shop status, many problems arise within the manufacturing environ ment. Investigations have shown that 20%～30% of all process plans generated are not valid and have to be altered or suffer production delays when production sta rts. There is thus a major need for integration of scheduling with computer aide d process planning for generating more realistic process plans. In doing so, eff iciency of the manufacturing system as a whole is expected to improve. Decision support system performs many functions such as selection of machine too ls, cutting tools, sequencing of operations, determination of optimum cutting pa rameters and checking availability of machine tool before allocating any operati on to a machine tool. The process of transforming component data, process capabi lity and decision rules into computer readable format is still a major obstacle. This paper proposes architecture of a system, which integrates computer aided p rocess-planning system with scheduling using decision support system. A decisio n support system can be defined as " an interactive system that provides the use rs with easy access to decision models in order to support semi-structured or u nstructured decision making tasks".

SYNTHESIS OF A HEXACYCLOPETIDE ON SOLID SUPPORT USING BOP REAGENT

The linear hexapeptide(His-Gly-Asp) was synthesized on solid support.FM group was used for a carboxyl protection group at Aspl.Peptide was cyclizsed by BOP reagent while still attached to the solid support.BOP proceeded more rapidly and gave purer product than DCC/HOBt method.