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.

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.

A novel error compensation method for multistage machining processes based on differential motion vector sets of multiple contour points

2020 ◽  
pp. 1-23
Author(s):  
Mengrui Zhu ◽  
Guangyan Ge ◽  
Xiaobing Feng ◽  
Zhengchun Du ◽  
Jianguo Yang
Keyword(s):  
Error Compensation ◽  
Tool Path ◽  
Motion Vector ◽  
Zero Point ◽  
Compensation Method ◽  
Machining Processes ◽  
Machined Surface ◽  
Differential Motion ◽  
Variation Propagation ◽  
Workpiece Setup

Abstract Modeling the variation propagation based on the stream of variation (SoV) methodology for multistage machining processes (MMPs) has been investigated intensively in the past two decades, however little research is conducted on the variation reduction and the existing work fails to be applied to irregular features caused by the machining-induced variation varying with the positions of the contour points on the machined surface. This paper proposes a novel error compensation method for MMPs through modifying the tool path to reduce variation for general features. The method based on differential motion vector (DMV) sets of multiple contour points is presented to represent the deviation of the irregular feature. Then the conventional SoV model is further extended to more accurately describe variation propagation for irregular features considering the actual datum-induced variations and the varying machining-induced variations, especially the deformation errors for the low stiffness workpiece. Based on the extended SoV model and error equivalence mechanism, the datum error and fixture error are transformed to the equivalent tool path error. Then the original tool path is modified through shifting the machine zero point of machine tools with no need for changing the original G code and workpiece setup. A real cutting experiment validates the effectiveness of the proposed error compensation method for MMPs with an average precision improvement of over 60%. The application of the extended SoV model significantly contributes to compensating more complex error sources for MMPs, such as the clamp force, the internal residual stress, etc.

State Space Modeling of Variation Propagation in Multistation Machining Processes Considering Machining-Induced Variations

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
José V. Abellan-Nebot ◽  
Jian Liu ◽  
Fernando Romero Subirón ◽  
Jianjun Shi
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
Tolerance Allocation ◽  
Machining Processes ◽  
Process Diagnosis ◽  
Cutting Tool Wear ◽  
Differential Motion ◽  
Variation Propagation ◽  
Space Modeling ◽  
Stream Of Variation

In spite of the success of the stream of variation (SoV) approach to modeling variation propagation in multistation machining processes (MMPs), the absence of machining-induced variations could be an important factor that limits its application in accurate variation prediction. Such machining-induced variations are caused by geometric-thermal effects, cutting-tool wear, etc. In this paper, a generic framework for machining-induced variation representation based on differential motion vectors is presented. Based on this representation framework, machining-induced variations can be explicitly incorporated in the SoV model. An experimentation is designed and implemented to estimate the model coefficients related to spindle thermal-induced variations and cutting-tool wear-induced variations. The proposed model is compared with the conventional SoV model resulting in an average improvement on quality prediction of 67%. This result verifies the advantage of the proposed extended SoV model. The application of the new model can significantly extend the capability of SoV-model-based methodologies in solving more complex quality improvement problems for MMPs, such as process diagnosis and process tolerance allocation, etc.

Diagnosability Analysis of Multi-Station Manufacturing Processes

2001 ◽  
Vol 124 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Yu Ding ◽  
Jianjun Shi ◽  
Dariusz Ceglarek
Keyword(s):  
Manufacturing Process ◽  
State Space Model ◽  
Manufacturing Processes ◽  
Assembly Process ◽  
Space Model ◽  
Distribution Strategy ◽  
Variation Propagation ◽  
Distribution Scheme ◽  
Stream Of Variation ◽  
Comprehensive Study

Variation propagation in a multi-station manufacturing process (MMP) is described by the theory of “Stream of Variation.” Given that the measurements are obtained via certain sensor distribution scheme, the problem of whether the stream of variation of an MMP is diagnosable is of great interest to both academia and industry. We present a comprehensive study of the diagnosability of MMPs in this paper. It is based on the state space model and is parallel to the concept of observability in control theory. Analogous to the observability matrix and index, the diagnosability matrix and index are first defined and then derived for MMP systems. The result of diagnosability study is applied to the evaluation of sensor distribution strategy. It can also be used as the basis to develop an optimal sensor distribution algorithm. An example of a three-station assembly process with multi-fixture layouts is presented to illustrate the methodology.

Diagnosability Analysis of Multi-Station Manufacturing Processes

Manufacturing ◽  
2002 ◽  
Author(s):  
Yu Ding ◽  
Jianjun Shi ◽  
Dariusz Ceglarek
Keyword(s):  
Manufacturing Process ◽  
State Space Model ◽  
Manufacturing Processes ◽  
Assembly Process ◽  
Space Model ◽  
Distribution Strategy ◽  
Variation Propagation ◽  
Distribution Scheme ◽  
Stream Of Variation ◽  
Comprehensive Study

Variation propagation in a multi-station manufacturing process (MMP) is described by the theory of “Stream of Variation.” Given that the measurements are obtained via certain sensor distribution scheme, the problem of whether the stream of variation of an MMP is diagnosable is of great interest to both academia and industry. We present a comprehensive study of the diagnosability of MMPs in this paper. It is based on the state space model and is parallel to the concept of observability in control theory. Analogous to the observability matrix and index, the diagnosability matrix and index are first defined and then derived for MMP systems. The result of diagnosability study is applied to the evaluation of sensor distribution strategy. It can also be used as the basis to develop an optimal sensor distribution algorithm. An example of a three-station assembly process with multi-fixture layouts is presented to illustrate the methodology.

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