Kinematic Analysis of Dimensional Variation Propagation for Multistage Machining Processes With General Fixture Layouts

2007 ◽  
Vol 4 (2) ◽  
pp. 141-152 ◽  
Author(s):  
Jean-Philippe Loose ◽  
Shiyu Zhou ◽  
Dariusz Ceglarek
Keyword(s):  
Kinematic Analysis ◽  
Machining Processes ◽  
Variation Propagation ◽  
Dimensional Variation

A Quality Prediction Framework for Multistage Machining Processes Driven by an Engineering Model and Variation Propagation Model

2007 ◽  
Vol 129 (6) ◽  
pp. 1088-1100 ◽  
Author(s):  
Jianming Li ◽  
Theodor Freiheit ◽  
S. Jack Hu ◽  
Yoram Koren
Keyword(s):  
Dimensional Accuracy ◽  
Software Tool ◽  
Propagation Model ◽  
System Level ◽  
Quality Prediction ◽  
Machining Processes ◽  
Propagation Modeling ◽  
Variation Propagation ◽  
Quality Modeling ◽  
Dimensional Variation

This paper proposes a comprehensive quality prediction framework for multistage machining processes, connecting engineering design with the activities of quality modeling, variation propagation modeling and calculation, dimensional variation evaluation, dimensional variation analysis, and quality feedback. Presented is an integrated information model utilizing a hybrid (feature/point-based) dimensional accuracy and variation quality modeling approach that incorporates Monte Carlo simulation, variation propagation, and regression modeling algorithms. Two important variations (kinematic and static) for the workpiece, machine tool, fixture, and machining processes are considered. The objective of the framework is to support the development of a quality prediction and analysis software tool that is efficient in predicting part dimensional quality in a multistage machining system (serial, parallel, or hybrid) from station level to system level.

Modelling of Variation Propagation for Multi-Operational Machining Processes Based on Perturbation Vectors

2010 ◽  
Vol 36 ◽  
pp. 120-128 ◽  
Author(s):  
Z.J. Wen ◽  
Ping Yu Zhu ◽  
X.P. Zhang ◽  
H.C. Liu
Keyword(s):  
State Space ◽  
State Space Model ◽  
Machining Process ◽  
Machining Processes ◽  
Machining Error ◽  
Space Model ◽  
Proposed Model ◽  
Variation Propagation ◽  
Dimensional Variation

A new state space model of multi-operational machining processes is presented for dimensional variation propagation, transformation and accumulation based on perturbation vectors (PV). Taking perturbation vectors (PV) for state vectors of part geometric variaton and the fixture variations for input vectors, the perturbation homogeneous transformation (PHT) is applied to analyze and derivate datum-induced deviation, re-location deviation, fixture error and machining error, and a state space model of variation propagation in multi-operational complicated machining processes is developed. Furthermore, a three-operation machining process of cylinder is given to illustrate the method presented. With the results of calculation and simulation, it is verified that the proposed model is effective and useful.

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.

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.

Product Tolerance Allocation in Compliant Multistation Assembly Through Variation Propagation and Analytical Target Cascading

2004 ◽  
Author(s):  
Zhijun Li ◽  
Jianpeng Yue ◽  
Michael Kokkolaras ◽  
Jaime Camelio ◽  
Panos Y. Papalambros ◽  
...  
Keyword(s):  
Linear Process ◽  
Tolerance Allocation ◽  
Vehicle Body ◽  
Analytical Target Cascading ◽  
Variation Propagation ◽  
Target Cascading ◽  
Dimensional Variation ◽  
Product Variation ◽  
Multistation Assembly ◽  
Sheet Metal Assembly

Compliant sheet metal assembly is a hierarchical manufacturing process that plays a significant role in automotive product development. Parts are joined in different stations to form the final product (e.g., the vehicle body structure). Dimensional variation is a product attribute of major importance that characterizes quality, and is mainly affected by the variability of parts, fixtures, and joining methods at each of the multiple stations. The propagation of dimensional variation through the multistation assembly system is modeled as a linear process, where all three aforementioned sources of variability are taken into account at each station using finite element models. In this article we apply the analytical target cascading process to the tolerance allocation problem in multistation assembly systems. Specifically, we translate final product variation targets to tolerance specifications for subassemblies and incoming parts. We demonstrate the methodology by means of a vehicle side frame assembly example.

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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