The modeling of maraging steel phase transformation in grinding process is presented in this article. Specifically, heating rate and contact zone temperature are examined to quantitatively link material properties, wheel topography characteristics and process parameters to the kinetics of diffusion-controlled transformation and diffusionless transformation. Physics-based modeling and prediction for the volume fractions of phase transformation in continuous heating under anisothermal conditions are developed based upon the addition of volume fractions in sequential segmented isothermal processes of grinding. The predictive model is validated by 18Ni (250) maraging steel grinding experiments, X-ray diffraction measurements and regression analyses.Results are compared to the model predicted of martensite and ferrite phase volume fractions after grinding. The physics-based model is experimentally validated as viable to predict the occurrence and extent of phase transformation related to material properties, wheel topography and grinding thermal-mechanical loading. Finally, correlation analysis is used to quantify the importance of the input variables to both model-predicted and X-ray diffraction measured phase transformation results.
Job-shop scheduling is a difficult type of production planning problem, of which the primary characteristic is that the processing route of each job is different. Job shop scheduling belongs to the special class of NP-hard problems.Most of the algorithms used to optimize this class of problems have an exponential time; that is, the computation time increases exponentially with problem size. In most studies on job-shop scheduling problems, the objective is usually to determine the sequence of jobs to minimize the makes pan. The due date request of the key jobs, the availability of key machine, the average wait-time of the jobs and the similarities between jobs and so on are also the objectives to be considered synthetically in real manufacturing process. In this paper, the job shop scheduling problem with multi-objectives is analyzed and studied by using genetic algorithms based on the mechanics of genetics and natural selection. First, the description of this problem is given with its model. Then, the tactics of the coding and decoding and the design of the genetic operators, along with the description of the mathematic model of the multi-objective functions, are presented. Finally an illustrative example is given to testify the validity of this algorithm.
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