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Mathematization of experts knowledge: example of part orientation in additive manufacturing
Journal of Intelligent Manufacturing ( IF 8.3 ) Pub Date : 2021-01-03 , DOI: 10.1007/s10845-020-01719-2
Mouhamadou Mansour Mbow , Christelle Grandvallet , Frederic Vignat , Philippe Rene Marin , Nicolas Perry , Franck Pourroy

The use of expert knowledge by manufacturing companies to support everyday activities has become an emerging practice thanks to the new knowledge management tools. A big set of knowledge is available in the organizations but its profitable use to solve problems and assist decision making is still a challenge. This is the case of CAM operations or preprocessing steps for which various works have been led to involve experts’ knowledge in the decision-making based on qualitative principles. However, so far, there is no methodology to the quantitative representation of that knowledge for more assistance. This paper introduces an approach for the conversion of knowledge into quantitative mathematical models. The main idea is to go from elicitation data in the form of action rules to simple unitary mathematical images; here desirability functions. The whole process carried out to extract the useful information that help building the desirability functions is exposed and different useful mathematical considerations are proposed. The resulting methodology identifies the categories of concepts in action rules and translate them into codified action rules. Then, through a mathematization process, the desirability functions are built. In short, this new approach allows evaluating the satisfaction level of the rules prescribed by the experts. As an illustration, the model is applied to action rules for CAM operations in additive manufacturing and more precisely to the definition of part orientation. This has shown the robustness of the methodology used and that it is possible to translate elicitation data into mathematical functions operable in computation algorithms.



中文翻译:

专家知识的数学化:增材制造中零件方向的示例

得益于新的知识管理工具,制造公司使用专家知识来支持日常活动已成为一种新兴实践。组织中可以使用大量知识,但是将其用于解决问题和协助决策仍然是一项挑战。CAM操作或预处理步骤就是这种情况,为此,已经进行了许多工作,以使专家的知识基于定性原理参与决策。但是,到目前为止,还没有方法可以定量表示该知识以获得更多帮助。本文介绍了一种将知识转换为定量数学模型的方法。主要思想是从动作规则形式的启发数据到简单的统一数学图像。这里期望功能。公开了提取有助于构建所需功能的有用信息的整个过程,并提出了各种有用的数学考虑。最终的方法论确定了动作规则中概念的类别,并将其转换为规则化的动作规则。然后,通过数学化过程,构建期望函数。简而言之,这种新方法可以评估专家规定的规则的满意度。作为说明,该模型应用于增材制造中CAM操作的动作规则,更确切地说,应用于零件方向的定义。这表明了所用方法的鲁棒性,并且有可能将启发数据转换为可在计算算法中操作的数学函数。最终的方法论确定了动作规则中概念的类别,并将其转换为规则化的动作规则。然后,通过数学化过程,构建期望函数。简而言之,这种新方法可以评估专家规定的规则的满意度。作为说明,该模型应用于增材制造中CAM操作的动作规则,更确切地说,应用于零件方向的定义。这表明了所用方法的鲁棒性,并且有可能将启发数据转换为可在计算算法中操作的数学函数。最终的方法论确定了行动规则中概念的类别,并将其转化为规则化的行动规则。然后,通过数学化过程,构建期望函数。简而言之,这种新方法可以评估专家规定的规则的满意度。作为说明,该模型应用于增材制造中CAM操作的动作规则,更确切地说,应用于零件方向的定义。这表明了所用方法的鲁棒性,并且有可能将启发数据转换为可在计算算法中操作的数学函数。这种新方法可以评估专家规定的规则的满意度。作为说明,该模型应用于增材制造中CAM操作的动作规则,更确切地说,应用于零件方向的定义。这表明了所用方法的鲁棒性,并且有可能将启发数据转换为可在计算算法中操作的数学函数。这种新方法可以评估专家规定的规则的满意度。作为说明,该模型应用于增材制造中CAM操作的动作规则,更确切地说,应用于零件方向的定义。这表明了所用方法的鲁棒性,并且有可能将启发数据转换为可在计算算法中操作的数学函数。

更新日期:2021-01-03
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