Abstract
A typical process route essentially represents the commonly used process planning-related knowledge and can be modified to generate new process routes easily. Hence, its quality directly affects the performance of newly generated process routes and thereby the goodness of products. To effectively discover typical process route knowledge, a reasonable similarity measure and a clustering method specifically for process routes are required. However, existing operation sequence similarity coefficients often assign coarse-grained similarities, which leads to inaccurate clustering results. For the clustering problem, most researchers have not considered the practical constraints during typical process route discovery. In this paper, an operation sequence similarity-based discovery method is presented. First, the characteristics and information requirements of the operation sequence similarity problem are analysed, and a novel comprehensive similarity coefficient combined with a modified pseudo-longest-common-subsequence (pseudo-LCS) and Jaccard similarity coefficient is proposed based on this analysis. This coefficient considers the precedence relationship, the number of common operations, and the operation similarity simultaneously to handle all the potential similarity situations. Second, two soft constraints, namely, quantity constraint and size constraint, are introduced in the traditional process route clustering problem to ensure the quality and validity of the discovered typical process routes. To solve this more practical problem and achieve a balance between these two conflicting constraints, the K-medoids method is improved with an adjustment mechanism to generate valid results under these two soft constraints. Finally, numerical illustrations are presented to verify the effectiveness of the proposed methods. The results show that compared with existing similarity coefficients, the proposed comprehensive similarity coefficient is more sensitive and much better at distinguishing the tiny difference between the process routes. In addition, the modified K-medoids method can perform much better than existing methods on process route discovery data sets under two conflicting soft constraints.
Highlights
As customer demand becomes more personalized, modern manufacturing prefers the production mode with multiple varieties and small batches
Because the goal of this paper is to find the typical process routes, the sum of the similarities between process routes and their exemplars and the penalties of two soft constraints are chosen as the performance measure as shown in Eq (6)
The process route plays an important role in manufacturing systems, which directly affects the quality of products, the performance of the system, and other aspects
Summary
As customer demand becomes more personalized, modern manufacturing prefers the production mode with multiple varieties and small batches. (2) Based on the analysis above, a novel similarity coefficient is presented to consider the precedence constraint of the process routes and the similarity between the operations and the number of common operations simultaneously. The numerical illustration based on the generated process route sets shows that the proposed algorithm can effectively handle the typical process route discovery problem considering two conflict soft constraints and obtain appropriate clustering results with better performance. (2) The traditional K-medoids method has been improved in this paper under the consideration of both quantity constraint and size constraint, which are seen as soft constraints In this manner, the manually designed constraint-related parameters have less influence on the clustering result.
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