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Mathematization of experts knowledge: example of part orientation in additive manufacturing

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Abstract

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.

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Acknowledgements

This work benefited from the support of the Project COFFA ANR-17-CE10-0008 of the French National Research Agency (ANR) and DP Research Institute KAM Lab Project.

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Correspondence to Mouhamadou Mansour Mbow.

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Appendices

Appendices

Definition 1

Negation

This operation corresponds to the complementary of \( f_{A} \left( {\hat{y}} \right) \) defined as:

$$ \begin{array}{*{20}c} {\overline{{f_{A} \left( {\hat{y}} \right)}} = 1 - f_{A} \left( {\hat{y}} \right)} \\ \end{array} $$
(8)

Definition 2

Intersection

For a given scenario, the intersection \( C \) (associated with \( m_{c} \)) of two relational functions \( A \) and \( B \) defined by the functions \( f_{A} \left( {\hat{y}_{A} } \right) = m_{A} \) and \( f_{B} \left( {\hat{y}_{B} } \right) = m_{B} \) respectively, corresponds to the logical “and” operation. Its corresponding function is given by:

$$ \begin{array}{*{20}c} {C = A \cap B} \\ \end{array} $$
(9)
$$ \begin{array}{*{20}c} {m_{c} = Min\left[ {m_{A} ,m_{B} } \right]} \\ \end{array} $$
(10)

Definition 3

Union

For a given scenario, the union \( C \) (associated with \( m_{c} \)) of \( A \) and \( B \) defined by the relational functions \( f_{A} \left( {\hat{y}_{A} } \right) = m_{A} \) and \( f_{B} \left( {\hat{y}_{B} } \right) = m_{B} \) respectively, is the logical “or” operation for which the corresponding function is:

$$ \begin{array}{*{20}c} {C = A \cup B} \\ \end{array} $$
(11)
$$ \begin{array}{*{20}c} {m_{C} = Max\left[ {m_{A} ,m_{B} } \right]} \\ \end{array} $$
(12)

Definition 4

De Morgan Laws

$$ \begin{array}{*{20}c} {\overline{A \cup B} = \bar{A} \cap \bar{B}} \\ \end{array} $$
(13)
$$ \begin{array}{*{20}c} {\overline{A \cap B} = \bar{A} \cup \bar{B}} \\ \end{array} $$
(14)

Example of pseudocode for surface angle desirability computation:

figure f

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Mbow, M.M., Grandvallet, C., Vignat, F. et al. Mathematization of experts knowledge: example of part orientation in additive manufacturing. J Intell Manuf 33, 1209–1227 (2022). https://doi.org/10.1007/s10845-020-01719-2

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