Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.

昆虫肠道微生物分离培养策略及研究进展

昆虫肠道作为一种特殊生境,生存着多种多样的共生微生物,并且肠道内的很多微生物与自然界其他生境的微生物种类显著不同。基于对纯培养微生物的研究,科学家们发现,肠道微生物与昆虫营养、生长发育及免疫等功能密切相关。因此,分离培养是发现微生物新种类、新基因和新功能的基础。然而,自然界可培养的微生物大约只占总数的1%。为了能够对更多的微生物进行分离和培养,近二十年来,微生物学家们发展了诸多新的培养技术和策略并利用它们从昆虫肠道分离出了很多新的难培养微生物。这些新的微生物种类极大地丰富了我们对肠道共生微生物生理作用与功能的认识。以此为基础,本文综述了昆虫肠道微生物分离培养的策略及研究进展,并对未来该领域的发展进行了展望。