Fast combination of scheduling chains under resource and time constraints

Scheduling chain combination is the core of chain-based scheduling algorithms, the speed of which determines the overall performance of corresponding scheduling algorithm. However, backtracking is used in general combination algorithms to traverse the whole search space which may introduce redundant operations, so performance of the combination algorithm is generally poor. A fast scheduling chain combination algorithm which avoids redundant operations by skipping “incompatible” steps of scheduling chains and using a stack to remember the scheduling state is presented in this paper to overcome the problem. Experimental results showed that it can improve the performance of scheduling algorithms by up to 15 times. By further omitting unnecessary operations, a fast algorithm of minimum combination length prediction is developed, which can improve the speed by up to 10 times.

Optimized shearing strategy for heavy plate based on contour recognition

The shearing line is the key to improve the quality and efficiency of heavy plates.A model of contour recognition and intelligent shearing strategy for the heavy plate was proposed.Firstly,multi-array binocular vision linear cameras were used to complete the image acquisition.Secondly,the total length of the steel plate after cooling was predicted by back propagation neural network algorithm according to the contour data.Finally,using the scanning line and a new camber description method,the shearing strategy including head/tail irregular shape length and rough dividing strategy was calculated.The practical application shows that the model and strategy can effectively solve the problems existing in the shearing process and can effectively improve the yield of steel plates.The maximum error of detection width,length,camber,and the length of the irregular deformation area at the head/tail of the plate are all less than 5 mm.The correlation coefficient of the length prediction model based on the back propagation neural network is very high.The reverse ratio result of edge cutting failure using the proposed rough dividing strategy is 1/401=0.2%,which is 2%higher than that by human.

Prediction of Maximum Section Flattening of Thin-walled Circular Steel Tube in Continuous Rotary Straightening Process

Cross-sectional ovalization of thin-walled circular steel tube because of large plastic bending,also known as the Brazier effect,usually occurs during the initial stage of tube′s continuous rotary straightening process.The amount of ovalization,defined as maximal cross section flattening,is an important technical parameter in tube′s straightening process to control tube′s bending deformation and prevent buckling.However,for the lack of special analytical model,the maximal section flattening was determined in accordance with the specified charts developed by experienced operators on the basis of experimental data;thus,it was inevitable that the localized buckling might occur during some actual straightening operations.New normal strain component formulas were derived based on the thin shell theory.Then,strain energy of thin-walled tube（per unit length）was obtained using the elastic-plastic theory.A rational model for predicting the maximal section flattening of the thin-walled circular steel tube under its straightening process was presented by the principle of minimum potential energy.The new model was validated by experiments and numerical simulations.The results show that the new model agrees well with the experiments and the numerical simulations with error of less than 10%.This new model was expected to find its potential application in thin-walled steel tube straightening machine design.