A novel collaborative manufacturing model requiring both geometric and physical evaluations of spiral bevel gears by design for six sigma. (March 2019)

• Record Type: Journal Article
• Authors: Hu, Zehua; Ding, Han; Peng, Shandong; Tang, Jinyuan; Tang, Yi
• Is Part Of: Mechanism and machine theory. Volume 133(2019)
• Page Start: 625
• Page End: 645
• Publication Date: 2019-03
• Subjects: Collaborative manufacturing -- Spiral bevel gears -- Design for six sigma (dfss) -- Data-driven decision and optimization -- Multi-objective optimization (moo) modification; Machine theory -- Periodicals; Machinery -- Periodicals; Machines -- Périodiques; Génie mécanique -- Périodiques; Machine theory; Machinery; Periodicals; 621.81
• DOI: 10.1016/j.mechmachtheory.2018.10.022
• Languages: English
• ISSNs: 0094-114X

Abstract

Highlights: Formulation of loaded transmission error (LTE) considering the tooth flank contact deformation, shear deformation especially shaft deformation under different bearing support. Synthesis and analysis of single-stage spiral bevel gear transmission system by simulated loaded tooth contact analysis (SLTCA). Comparison and analysis between the numerical formulation and SLTCA method. Abstract: Collaborative manufacturing requiring both geometric and physical evaluations has been an increasingly important trend in the industrial applications of spiral bevel gears. It is an innovative attempt that design for six sigma (DFSS) is introduced to establish a model of above collaborative manufacturing system. In the DFSS-based modeling, in addition to establishing the basic framework, the related data-driven decision and optimization are proposed, respectively. The house of quality (HOQ) is firstly established to convert the voice of customer (VOC) into the critical to qualities (CTQs) and determine the geometric and physical evaluations. Then, the conventional machine setting modification is extended to the multi-objective optimization (MOO) modification considering both geometric and physical evaluations. The whole system is divided into the three data-driven sub-problems for their respective decision and optimization. First, the accurate measurement and compensation is used to operate the noise factors in the actual manufacturing. Second, a prescribed ease-off topography is identified as the target flank by MOO of the physical evaluations. An improved high-order modification of the optimal machine setting is performed to optimize the geometric performance. A case of study on measurement-modification-machining (3 M) collaborative manufacturing system is provided to verify the proposed model.