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.

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.

scholarly journals Variation propagation modeling in multistage machining processes considering form errors and N-2-1 fixture layouts

2021 ◽  
Author(s):  
Filmon Yacob ◽  
Daniel Semere ◽  
Nabil Anwer
Keyword(s):  
Machining Process ◽  
Quality Prediction ◽  
Machining Processes ◽  
Variation Reduction ◽  
Propagation Modeling ◽  
Form Errors ◽  
Part Quality ◽  
Fixture Layout ◽  
Variation Propagation ◽  
Cad Cam

AbstractVariation propagation modeling of multistage machining processes enables variation reduction by making an accurate prediction on the quality of a part. Part quality prediction through variation propagation models, such as stream of variation and Jacobian-Torsor models, often focus on a 3-2-1 fixture layout and do not consider form errors. This paper derives a mathematical model based on dual quaternion for part quality prediction given parts with form errors and fixtures with N-2-1 (N>3) layout. The method uses techniques of Skin Model Shapes and dual quaternions for a virtual assembling of a part on a fixture, as well as conducting machining and measurement. To validate the method, a part with form errors produced in a two-stationed machining process with a 12-2-1 fixture layout was considered. The prediction made following the proposed method was within 0.4% of the prediction made using a CAD/CAM simulation when form errors were not considered. These results validate the method when form errors are neglected and partially validated when considered.

Singularity Issue in Dimensional Variation Diagnosis of Multi-Station Assembly Processes

Manufacturing ◽  
2003 ◽  
Author(s):  
Yu Ding ◽  
Abhishek Gupta ◽  
Daniel W. Apley
Keyword(s):  
Least Squares ◽  
Singular Systems ◽  
Dimensional Accuracy ◽  
Complete Information ◽  
Propagation Model ◽  
Error Sources ◽  
Process Error ◽  
Dimensional Variation ◽  
Discrete Part ◽  
Traditional Approaches

This paper presents a new method of diagnosing variation components of process error sources in a manufacturing system. Quite often in a complex multi-station assembly system only limited numbers of sensors are present due to which complete information of fixture errors is unavailable. This makes the system of variance components singular and not solvable by using regular least-squares estimation. The method suggests reformulation of the original error propagation model into a variation relation by using a matrix transformation. With the development of a new variation estimator and its diagnosability condition, some singular systems that are not diagnosable using traditional least squares methods become diagnosable. Difference between the new approach and the traditional approaches has been elaborated. Modified procedures of the new estimator are also presented to enhance its estimation performance. The idea is presented in the specific context of panel assembly processes, but the application of the idea should not be limited therein. Conclusions can be extended to general discrete-part manufacturing processes where fixtures are extensively used to ensure dimensional accuracy of the final product.

Dimensional variation propagation modeling and analysis for single-station assembly based on multiple constraints graph

2016 ◽  
Vol 36 (3) ◽  
pp. 308-317 ◽  
Author(s):  
Jian-feng Yu ◽  
Wen-Bin Tang ◽  
Yuan Li ◽  
Jie Zhang
Keyword(s):  
Geometric Constraints ◽  
Multiple Constraints ◽  
Content Type ◽  
Modeling And Analysis ◽  
Variation Reduction ◽  
Propagation Modeling ◽  
Variation Propagation ◽  
Single Station ◽  
Accumulation Mechanism ◽  
Dimensional Variation

Purpose Modeling and analysis of dimensional variation propagation is a crucial support technology for variation reduction, product/process design evaluation and recognition of variation source. However, owing to the multi-deviation (i.e. part deviations and fixture deviations) and multi-interaction (i.e. part-to-part interaction, part-to-fixture interaction and station-to-station interaction) in assembly processes, it is difficult for designers to describe or understand the variation propagation (or accumulation) mechanism clearly. The purpose of this paper is to propose a variation propagation modeling and analysis (VPMA) method based on multiple constraints aiming at a single station. Design/methodology/approach Initially, part-to-part constraints (PPCs) and part-to-fixture constraints (PFCs) are applied for the multi-interaction of assembly, and multiple constraints graph (MCG) model is proposed for expressing PPCs, PFCs, parts, as well as the variation propagation relation among them. Then, locating points (LPs) are adopted for representing the deviations in constraints, and formulas for calculating the deviations of LPs are derived. On that basis, a linearized relation between LPs’ deviations and part’s locating deviations is derived. Finally, a wing box is presented to validate the proposed method, and the results indicate the methodology’s feasibility. Findings MCG is an effective tool for dimensional VPMA, which is shown as an example of this paper. Originality/value Functions of geometric constraints in dimensional variation propagation are revealed, and MCG is proposed to formulize dimensional variation propagation.

Part Dimensional Error and Its Propagation Modeling in Multi-Operational Machining Processes

2003 ◽  
Vol 125 (2) ◽  
pp. 255-262 ◽  
Author(s):  
Qiang Huang ◽  
Jianjun Shi ◽  
Jingxia Yuan
Keyword(s):  
State Space Model ◽  
Machining Process ◽  
Kinematic Modeling ◽  
Machining Processes ◽  
Dimensional Error ◽  
Physical Relationship ◽  
Modeling And Control ◽  
Propagation Modeling ◽  
Variation Propagation ◽  
Machining Operations

In a multi-operational machining process (MMP), the final product variation is an accumulation or stack-up of variation from all machining operations. Modeling and control of the variation propagation is essential to improve product dimensional quality. This paper presents a state space model and its modeling strategies to describe the variation stack-up in MMPs. The physical relationship is explored between part variation and operational errors. By using the homogeneous transformation approach, kinematic modeling of setup and machining operations are developed. A case study with real machined parts is presented in the model validation.

A modification of DMVs based state space model of variation propagation for multistage machining processes

2017 ◽  
Vol 37 (4) ◽  
pp. 381-390 ◽  
Author(s):  
Fuyong Yang ◽  
Sun Jin ◽  
Zhimin Li
Keyword(s):  
Current Model ◽  
Propagation Model ◽  
Machining Processes ◽  
Motion Vectors ◽  
Content Type ◽  
Model Based ◽  
Differential Motion ◽  
Variation Propagation ◽  
Current Variation ◽  
Differential Motion Vectors

Purpose Complicated workpiece, such as an engine block, has special rough locating datum features (i.e. six independent datum features) due to its complex structure. This locating datum error cannot be handled by current variation propagation model based on differential motion vectors. To extend variation prediction fields, this paper aims to solve the unaddressed variation sources to modify current model for multistage machining processes. Design/methodology/approach To overcome the limitation of current variation propagation model based on differential motion vectors caused by the unaddressed variation sources, this paper will extend the current model by handling the unaddressed datum-induced variation and its corresponding fixture variation. Findings The measurement results of the rear face with respect to the rough datum W and the pan face with respect to the hole Q by coordinate measuring machine (CMM) are −0.006 mm and 0.031 mm. The variation results for rear face and pan face predicted by the modified model are −0.009 mm and 0.025 mm, respectively. The discrepancy of model prediction and measurement is very small. Originality/value This paper modifies the variation propagation model based on differential motion vectors by solving the unaddressed variation sources, which can extend the variation prediction fields for some complicated workpiece and is useful in the future work for many fields, such as process monitoring, fault diagnosis, quality-assured setup planning and process-oriented tolerancing.

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