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.

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.

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.

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.

Eliminating Redundant Operations While Improving Part Quality by Modeling Machining Errors

2005 ◽  
Author(s):  
John Agapiou ◽  
Eric Steinhilper ◽  
Pulak Bandyopadhyay ◽  
Jeffrey Q. Xie
Keyword(s):  
Main Axis ◽  
Machining Process ◽  
Single Pass ◽  
Form Errors ◽  
Part Quality ◽  
Machining Errors ◽  
Transmission Case ◽  
System Errors

A methodology to predict part quality was applied to the perpendicularity quality of the bell face and main axis of a transmission case. By modeling the quality of different processing sequences, we were able to show that the quality of the part - perpendicularity of critical features - does not improve significantly by performing two-pass machining process instead of a single-pass. This application of our quality methodology required the modeling of additional system errors which were not developed in the earlier version and which were needed to predict certain types of form errors. In addition to improved part quality, changing the existing line to a single-pass process eliminated a bothersome job-setting procedure and tooling costs at the second-pass and increased productivity of a rebalanced line.

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.

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.

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.

scholarly journals A multilayer shallow learning approach to variation prediction and variation source identification in multistage machining processes

2020 ◽  
Author(s):  
Filmon Yacob ◽  
Daniel Semere
Keyword(s):  
Neural Network ◽  
Source Identification ◽  
Point Cloud ◽  
Machining Process ◽  
Skin Model ◽  
Machining Processes ◽  
Cloud Data ◽  
Actual Surface ◽  
Variation Propagation ◽  
Analytical Approaches

Abstract Variation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source identification techniques using Skin Model Shapes is unclear. This paper proposes a multilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.

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