<正>The final product quality is determined by cumulation,coupling and propagation of prod- uct quality variations from all stations in multi-stage manufacturing systems(MMSs).Modeling and control of variation propagation is essential to improve product quality.However,the current stream of variations(SOV) theory can only solve the problem that a single SOV affects the product quality. Due to the existence of multiple variation streams,limited research has been done on the quality con- trol in serial-parallel hybrid multi-stage manufacturing systems(SPH-MMSs).A state space model and its modeling strategies are developed to describe the multiple variation streams stack-up in an SPH-MMS.The SOV theory is extended to SPH-MMS.The dimensions of system model are reduced to the production-reality level,and the effect and feasibility of the model is validated by a machining case.
展开▼
机译:Researchers at Shanghai Jiao Tong University Release New Data on Support Vector Machines (Study On Model Evolution Method Based On the Hybrid Modeling Technology With Support Vector Machine for an Sofc-gt System)
机译:New Machine Learning Study Findings Reported from Imam Khomeini International University (A Novel Approach for Reducing Feature Space Dimensionality and Developing a Universal Machine Learning Model for Coated Tubes in Cross-Flow Heat Exchangers)
机译:Study Results from George Mason University Broaden Understanding of Machine Learning (Modeling and Mitigating Human Annotation Errors To Design Efficient Stream Processing Systems With Human-in-the-loop Machine Learning)