A typical process route discovery method based on clustering analysis

Liu, Shunuan; Zhang, Zhenming; Tian, Xitian

doi:10.1007/s00170-006-0706-0

Cited by 28 publications

(18 citation statements)

References 12 publications

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“…5, AAG 1 1 and AAG 5 1 are expressed as a unique string Cy1PlPl_Pl. AAG 3 1 , AAG 4 1 , AAG, AAG 7 1 , AAG 8 1 , AAG 9 1 and AAG 1 0 1 are expressed as a unique string Pl_PlPl1CyCy_Cy. After all 3-D solids in GV1 are expressed as their corresponding strings, GV1 is transformed into a string collection that includes two strings, i.e., Cy1PlPl_Pl and Pl_PlPl1CyCy_Cy.…”

Section: String Representation Of Geometry Changementioning

confidence: 99%

Similarity Measurement of the Geometry Variation Sequence of Intermediate Process Model

2020

Preprint

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The reuse of machining process, by which the process for a new mechanical part is determined by referencing to the existing and matured processes, is an effective way of improving manufacturing and supporting innovation. To conduct the effective reuse it is necessary to express and retrieve a specific process. A kernel technique of the expression and the retrieval is to measure the similarity of part’s geometry variation during the machining. To address this problem, a general framework of measuring the similarity between parts is proposed in this work. The geometry variation sequence of intermediate process model is established, and then a method to measure its similarity is invented. Two case studies are rendered and the result reveals that the proposed method is effective and can provide the support for the process retrieval and reuse in industry.

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Section: String Representation Of Geometry Changementioning

confidence: 99%

Similarity Measurement of the Geometry Variation Sequence of Intermediate Process Model

2020

Preprint

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“…Research articles focusing on process planning in many cases focus on enhancing understanding and technology regarding specific planning problems, e.g. the automated selection of machining parameters [5], automated discovery of typical process routes [6] or selecting "optimal" process plans [7]. The researchers usually either loosely state their objectives or feature explicit, even multi-criteria, objective functions [8] with regard to the specific planning problem at hand.…”

Section: Deficiencies Of Existing Approachesmentioning

confidence: 99%

Process Planning Assessment Framework

Schuh

Prote

Hünnekes

2018

IFIP Advances in Information and Communication Technology

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In order to rationalize process planning activities or to improve process planning outcomes, various process planning approaches with different degrees of computerized support and automation have been established. Ranging from general to very specialized approaches to specific problems, these approaches have specific advantages and drawbacks. When designing new approaches as well as selecting existing ones for application in the field, their properties have to be considered comprehensively. However, existing research only selectively touches on the objectives regarding the planning process or the quality of information generally requested from process plans. This paper presents the synthesis of a comprehensive process planning assessment framework, including planning process and outcome related criteria, based on an extensive literature review.

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“…Liu et al (2007) employed the Euclidean distance to calculate the similarity between two process routes, and applied the agglomerative hierarchical clustering method to form the clusters of similar process routes. Zhang et al (2013) proposed a multi-level comprehensive method for the similarity calculation between two process routes, and used the Particle Swarm Optimization (PSO) to realize the intelligent clustering of analogous process routes.…”

Section: Introductionmentioning

confidence: 99%