6 research outputs found
Tolerance analysis β Form defects modeling and simulation by modal decomposition and optimization
Tolerance analysis aims on checking whether specified tolerances enable functional and assembly requirements. The tolerance analysis approaches discussed in literature are generally assumed without the consideration of partsβ form defects. This paper presents a new model to consider the form defects in an assembly simulation. A Metric Modal Decomposition (MMD) method is henceforth, developed to model the form defects of various parts in a mechanism. The assemblies including form defects are further assessed using mathematical optimization. The optimization involves two models of surfaces: real model and difference surface-base method, and introduces the concept of signed distance. The optimization algorithms are then compared in terms of time consumption and accuracy. To illustrate the methods and their respective applications, a simplified over-constrained industrial mechanism in three dimensions is also used as a case study
Probabilistic-based approach using Kernel Density Estimation for gap modeling in a statistical tolerance analysis
The statistical tolerance analysis has become a key element used in the design stage to reduce the manufacturing cost, the rejection rate and to have high quality products. One of the frequently used methods is the Monte Carlo simulation, employed to compute the non-conformity rate due to its efficiency in handling the tolerance analysis of over-constrained mechanical systems. However, this simulation technique requires excessive numerical efforts. The goal of this paper is to improve this method by proposing a probabilistic model of gaps in fixed and sliding contacts and involved in the tolerance analysis of an assembly. The probabilistic model is carried out on the clearance components of the sliding and fixed contacts for their assembly feasibility considering all the imperfections on the surfaces. The kernel density estimation method is used to deal with the probabilistic model. The proposed method is applied to an over-constrained mechanical system and compared to the classical method regarding their computation time
ΠΡΡΠΎΠΊΠΈΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π½Π°ΡΠΊΠ΅ ΠΈ ΡΠ΅Ρ Π½ΠΈΠΊΠ΅ (ΠΠ’Π‘ΠΠ’-2017): ΡΠ±ΠΎΡΠ½ΠΈΠΊ Π½Π°ΡΡΠ½ΡΡ ΡΡΡΠ΄ΠΎΠ² VI ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ Π½Π°ΡΡΠ½ΠΎ-ΡΠ΅Ρ Π½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ ΡΡΠ΅Π½ΡΡ , Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ², Π³. Π’ΠΎΠΌΡΠΊ, 27β29 Π½ΠΎΡΠ±ΡΡ 2017 Π³.
Π ΡΠ±ΠΎΡΠ½ΠΈΠΊΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΏΠΎ ΡΠ»Π΅Π΄ΡΡΡΠΈΠΌ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ: ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² Π½ΠΎΠ²ΡΡ
ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠΉ ΠΈ Π½Π°Π½ΠΎΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ²; ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ; ΠΎΡΠ³Π°Π½ΠΈΡΠ΅ΡΠΊΠ°Ρ Ρ
ΠΈΠΌΠΈΡ ΠΈ Π±ΠΈΠΎΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ; ΡΠ°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠΈΠ½Π΅ΡΠ°Π»ΡΠ½ΡΡ
ΠΈ Π²ΠΎΠ΄Π½ΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ²; ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΠΈ ΠΌΠ°ΡΠΈΠ½ΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΈ ΠΌΠ°ΡΠΈΠ½ΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ; ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΈΠ·ΠΈΠΊΠΎ-Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΡ
. Π¦Π΅Π»ΡΡ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ ΠΠ’Π‘ΠΠ’-2017 ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΠΊΠΎΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΈ Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΡ
ΠΌΠΎΠ»ΠΎΠ΄ΡΡ
ΡΡΠ΅Π½ΡΡ
ΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² Π² ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Π²ΡΡΠΎΠΊΠΈΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ. Π‘Π±ΠΎΡΠ½ΠΈΠΊ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ Π΄Π»Ρ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ
ΡΡΠ΅Π½ΡΡ
- ΡΠΈΠ·ΠΈΠΊΠΎΠ² ΠΈ Ρ
ΠΈΠΌΠΈΠΊΠΎΠ², ΠΈΠ½ΡΠ΅ΡΠ΅ΡΡΡΡΠΈΡ
ΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΠΌΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ, Π½ΠΎΠ²ΡΡ
ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΠΎΠ² ΠΈ ΡΠ°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈΡΠΎΠ΄Π½ΠΎΠ³ΠΎ ΡΡΡΡΡ