scholarly journals Variation propagation of bench vises in multi-stage machining processes

2019 ◽  
Vol 41 ◽  
pp. 906-913
Author(s):  
José V. Abellán-Nebot ◽  
R. Moliner-Heredia ◽  
Gracia M. Bruscas ◽  
J. Serrano
Keyword(s):  
Machining Processes ◽  
Multi Stage ◽  
Variation Propagation

Quality Assured Setup Planning Based on the Stream-of-Variation Model for Multi-Stage Machining Processes

2006 ◽  
Author(s):  
Jian Liu ◽  
Jianjun Shi ◽  
S. Jack Hu
Keyword(s):  
Product Quality ◽  
Cost Effective ◽  
Machining Process ◽  
Manufacturing Processes ◽  
Setup Planning ◽  
Machining Processes ◽  
Multi Stage ◽  
Variation Propagation ◽  
Stream Of Variation ◽  
The Cost

Setup planning is a set of activities to arrange manufacturing features into an appropriate sequence for processing. As such, setup planning can significantly impact the product quality in terms of dimensional variation in the Key Product Characteristics (KPC’s). Current approaches in setup planning are experience-based and tend to be conservative by selecting unnecessarily precise machines and fixtures to ensure final product quality. This is especially true in multi-stage manufacturing processes because it has been difficult to predict the variation propagation and its impact on KPC quality. In this paper, a new methodology is proposed to realize cost-effective, quality ensured setup planning for multi-stage manufacturing processes. Setup planning is formulated as an optimization problem based on quantitative evaluation with the Stream-of-Variation (SoV) models. The optimal setup plan minimizes the cost related to process precision and satisfies the quality specifications. The effectiveness of the proposed approach is demonstrated through setup planning for a multi-stage machining process.

Multi-Operational Machining Processes Modeling for Sequential Root Cause Identification and Measurement Reduction

2004 ◽  
Author(s):  
Hui Wang ◽  
Qiang Huang ◽  
Reuven Katz
Keyword(s):  
Machine Tool ◽  
Propagation Model ◽  
Machining Processes ◽  
Error Sources ◽  
Variation Reduction ◽  
Measurement Reduction ◽  
Propagation Modeling ◽  
Root Cause ◽  
Variation Propagation ◽  
Root Cause Identification

Variation propagation modeling has been proved to be an effective way for variation reduction and design synthesis in multi-operational manufacturing processes (MMP). However, previously developed approaches for machining processes did not directly model the process physics regarding how fixture, and datum, and machine tool errors generate the same pattern on part features. Consequently, it is difficult to distinguish error sources at each operation. This paper formulates the variation propagation model using the proposed equivalent fixture error (EFE) concept. With this concept, datum error and machine tool error are transformed to equivalent fixture locator errors at each operation. As a result, error sources can be grouped and root cause identification can be conducted in a sequential manner. The case studies demonstrate the model validity through a real cutting experiment and model advantage in measurement reduction for root cause identification.

scholarly journals Simulation of Multi-Stage Fine Machining Processes at the Example of Valve Guide and Valve Seat

2018 ◽  
Vol 885 ◽  
pp. 255-266 ◽  
Author(s):  
Christian Bölling ◽  
Eberhard Abele
Keyword(s):  
Cylinder Head ◽  
Combustion Engine ◽  
Simulation Models ◽  
Manufacturing Processes ◽  
Series Production ◽  
Valve Seat ◽  
Machining Processes ◽  
Industrial Manufacturing ◽  
Multi Stage ◽  
Measure Of Uncertainty

Fine machining processes are of great importance in automotive series production, e.g. the machining of valve guide and seat in the cylinder head of a combustion engine. In industrial manufacturing processes, disturbances are inevitable and provide a measure of uncertainty in each production step. Increasingly, the influence of such uncertainties is being evaluated using simulation models. In this paper, a modeling approach for simulation of multi-stage fine machining processes with step tools is presented and investigations regarding influence of uncertainty caused by disturbances are performed.

Research on Quality Control Architecture for Multistage Machining Processes in Small Batch Mode

2011 ◽  
Vol 130-134 ◽  
pp. 2573-2576
Author(s):  
Yan Wang ◽  
Ping Yu Jiang
Keyword(s):  
Quality Control ◽  
Process Quality ◽  
Propagation Model ◽  
Quality Data ◽  
Quality Prediction ◽  
Machining Processes ◽  
Batch Mode ◽  
Online Quality Control ◽  
Variation Propagation ◽  
Key Enabling Technologies

This paper presents a type of architecture of multistage machining processes in small batch mode, named Small-batch Quality Control System (SQCS), through analyzing various process quality control methods. The SQCS integrates complex network, workpiece variation propagation model and process quality prediction. And then, the three key enabling technologies are discussed in detail. Sensor network could be used to acquire real-time quality data, which include workpieces’ physical and dimensional information. Based on the above mentioned ideas, a general model of stage flow in small batch mode is constructed in order to realize process-driven online quality control and improve product machining quality.

scholarly journals Variation propagation modelling in multistage machining processes using dual quaternions

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2987-2998
Author(s):  
Filmon Yacob ◽  
Daniel Semere
Keyword(s):  
Motion Vector ◽  
Manufacturing Processes ◽  
Machining Processes ◽  
Dual Quaternions ◽  
Multistage Manufacturing ◽  
Differential Motion ◽  
Variation Propagation ◽  
Stream Of Variation ◽  
Matrix Differential ◽  
Propagation Modelling

Abstract Variation propagation models play an important role in part quality prediction, variation source identification, and variation compensation in multistage manufacturing processes. These models often use homogenous transformation matrix, differential motion vector, and/or Jacobian matrix to represent and transform the part, tool and fixture coordinate systems and associated variations. However, the models end up with large matrices as the number features and functional element pairs increase. This work proposes a novel strategy for modelling of variation propagation in multistage machining processes using dual quaternions. The strategy includes representation of the fixture, part, and toolpath by dual quaternions, followed by projection locator points onto the features, which leads to a simplified model of a part-fixture assembly and machining. The proposed approach was validated against stream of variation models and experimental results reported in the literature. This paper aims to provide a new direction of research on variation propagation modelling of multistage manufacturing processes.

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