Optimization On Sheet Defect Segmentation And Cutting Problem

Sheet cutting is a classic optimization problem that is widely present in wood industrial production.However,natural wood often has defects that make it more difficult to process.An efficient method of segmenting defects and sheet cutting can play an important role in reducing wood consumption,saving production costs and reducing the waste of resources.In order to achieve rapid segmentation of sheet defects and improve the cutting efficiency of sheet,the nonlinear adaptive group gray wolf algorithm was proposed to optimize the solving process of two-dimensional maximum inter-class variance threshold segmentation algorithm(OTSU).In addition,an optimization algorithm of sheet cutting based on improved region variable neighborhood search and edge matching algorithm is proposed,which can effectively solve the problem of sheet cutting without and with defects at the same time,and can get a better sheet layout program in a short time.In order to solve the problem of slow and low efficiency of t wo-dimensional OTSU segmentation plate defect calculation,a nonlinear adaptive grouping gray wolf optimization algorithm was proposed to optimize the threshold solving process of two-dimensional OTSU algorithm.The algorithm adopts three improvement strategies,including CPM mapping(the Chebyshev mapping method combined with Piecewise mapping followed by Mod operation),an "S" type nonlinear control parameter and group position update,to solve the problems of uneven initial population distribution,inadequate balance between local exploitation and global search ability,and poor search ability of grey wolf optimization.Through the simulation of 12 test functions,it is found that the global optimal solution can be obtained only by short iterative algebra,and it has excellent solving performance.In the sheet defect segmentation,it is found that the computation time of the 2D OTSU algorithm optimized by the nonlinear adaptive grouping gray wolf optimization algorithm is reduced from more than 180 seconds to less than 2 seconds compared with the 2D OTSU exhaustive method,and the solution accuracy is significantly higher than that of the 2D OTSU algorithm optimized by the traditional grey wolf optimization algorithm,which can complete the sheet defect segmentation quickly and accurately.In order to improve the utilization rate and cutting efficiency of sheet,this paper proposes a cutting optimization algorithm based on region variable neighborhood search combined with edge matching degree evaluation.Since the traditional minimum horizontal line correlation algorithm cannot accurately select the best rectangular pieces suitable for the current layout,an edge matching degree evaluation function is proposed.This function can evaluate all the rectangular pi eces to be discharged,from which the rectangular piece with the greatest degree of overlap with the edge of the current layout is selected for packing.However,since it is not possible to confirm whether the rectangular pieces selected by edge matching e valuation are also optimal in the future,i.e.,whether they remain optimal after discharging other rectangular pieces.Therefore,a regional variable neighborhood search algorithm adapted to the undercutting problem is designed,which is able to evaluate whether the rectangular pieces are still optimal after discharging other rectangular pieces.The edge matching degree algorithm is combined with the variable neighborhood search algorithm to enhance its search capability.Through simulation experiments,it is found that the combined algorithm can effectively solve the sheet undercutting problem without(with)defects at the same time.The algorithm improves the sheet utilization by 2.8% to 13.52%compared to algorithms such as the lowest horizontal line and the genetic algorithm optimized lower left corner filling.And the calculation time of cutting is also significantly reduced,indicating the effectiveness of the algorithm.