EP0909995A2

A machine set up procedure using multivariate modeling and multiobjective optimization

Abstract

A method of setting up an electrostatographic printing machine having image quality attributes and parameters that control the attributes using multivariate modeling and multiobjective optimization. The method includes providing a discrete number of parameter settings and printing test patterns based upon the parameter settings. The test patterns are scanned to produce a set of image quality values (104). Using a multivariate adaptive regression splines technique, a model of the printing machine image quality is provided in response to the parameter settings and the image quality values (106). Optimum parameter settings for the printing machine are then determined from the discrete number of parameter settings to produce consistent image quality (108).

EP0909995A2, drawing sheet 1
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Projected expiry passed 5 October 2018, 8 years ago.

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8 claims: 2 independent, 6 dependent

  1. 1
    A method of setting up an electrostatographic printing machine having operating components with changeable set point parameters using multivariate modeling and multiobjective optimization, the method comprising the steps of:providing a discrete number of parameter settings and printing test patterns based upon said parameter settings, scanning the test patterns and producing a set of image quality values based upon the parameter settings, responding to the parameter settings and the image quality values and using a multivariate adaptive regression splines technique to provide a model of the printing machine image quality, and determining the optimum parameter settings for the printing machine from the discrete number of parameter settings to produce consistent image quality.
  2. 4
    A method of finding appropriate values of each parameter of an electrostatographic printing machine having image quality attributes denoted by a vector p = [p 1 ,p n, ..., p n ] and parameters used to setup the machine denoted by x= [x 1 ,x 2 ..., x m ], such that the image quality attributes attain desired values, the method comprising the steps of:identifying the m variables x that affect the n image quality attributes p under consideration, identifying the range of each variable x between which it can vary, providing a set of experiments by varying the variables x between respective ranges using orthogonal arrays, running the machine at different experimental value setting as defined by the orthogonal arrays and measuring the different image quality for each experimental setting, determining a functional model describing the relationship between each attribute p and the variables x, upon determining the relationships between image quality attributes and the parameters x, using a multiobjective optimization methodology to obtain a Pareto-optimal setpoint that gives a desired set of image quality attributes, and in response to a significant conflict in simultaneously obtaining desired image quality attributes, using an interactive multiobjective optimization technique for trading off one image quality attribute in a Pareto-optimal fashion with another until a desired Pareto-optimal solution has been obtained.
  3. 7
    The method of any of claims 4 to 6, wherein the step of determining a functional model describing the relationship between each attribute p and the variables x, is a simple linear regression model, a non-linear model, or a multivariate adaptive regression splines (MARS) model.
  4. 8
    The method of any of claims 4 to 7, wherein the step of using a multiobjective optimization methodology to obtain a Pareto-optimal setpoint that gives a desired set of image quality attributes includes the step of using one of a goal programming method of obtaining the Pareto-optimal setpoints, a linear gradient based search algorithm, and an adaptive simulated annealing algorithm.