Singularity Issues in Fixture Fault Diagnosis for Multi-Station Assembly Processes

2004 ◽  
Vol 126 (1) ◽  
pp. 200-210 ◽  
Author(s):  
Yu Ding ◽  
Abhishek Gupta ◽  
Daniel W. Apley
Keyword(s):  
Variance Components ◽  
Manufacturing Systems ◽  
Singular Systems ◽  
Dimensional Accuracy ◽  
Propagation Model ◽  
Estimation Methods ◽  
Singularity Problem ◽  
Sample Covariance ◽  
Ls Estimator ◽  
Discrete Part

This paper presents a method of diagnosing variance components of process error sources in singular manufacturing systems. The singularity problem is studied and the cause examined in the context of fixture error diagnosis in multi-station assembly processes. The singularity problem results in nondiagnosable fixture errors when standard least-squares (LS) estimation methods are used. This paper suggests a reformulation of the original error propagation model into a covariance relation. The LS criterion is then applied directly to the sample covariance matrix to estimate the variance components. Diagnosability conditions for this variance LS estimator are derived, and it is demonstrated that certain singular systems that are not diagnosable using traditional LS methods become diagnosable with the variance LS estimator. Modified versions that improve the accuracy of the variance LS estimator are also presented. The various procedures are thoroughly contrasted, in terms of accuracy and diagnosability. The results are illustrated with examples from panel assembly, although the application of the approach and the conclusions extend to more general discrete-part manufacturing processes where fixtures are used to ensure dimensional accuracy of the final product.

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.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

Implementing the Transformation of Discrete Part Manufacturing Systems Into Smart Manufacturing Platforms

2018 ◽  
Author(s):  
Bhaskar Botcha ◽  
Zimo Wang ◽  
Sudarshan Rajan ◽  
Natarajan Gautam ◽  
Satish T. S. Bukkapatnam ◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Manufacturing Systems ◽  
Energy Savings ◽  
Advanced Materials ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Workflow System ◽  
Manufacturing Technologies ◽  
Discrete Part

Prior R&D efforts point to substantial performance enhancements and energy savings from adopting the Smart Manufacturing (SM) paradigm for process optimization and real-time quality assurance. Significant barriers and risks disincentivize the industry from investing in the adoption and training of SM component suites for discrete manufacturing applications. A diverse discrete part manufacturing enterprises, SM tools and platform vendors are yearning for a testbed reconfigurable to achieve three objectives of performance benchmarking, demonstration, and workforce training for a spectrum of their industrial scenarios and workflows. This paper presents the key ingredients towards the successful transformation of present machine tool and manufacturing environments into SM platform-integrated environments. The present implementation focuses on demonstration of the use of the Smart Manufacturing (SM) platform towards qualification of advanced materials and manufacturing technologies to meet an industry-specified functionality. This initial implementation uses Kepler workflow system residing as part of an Amazon Web Services environment to allow flexible workflows on multiple machines, each of which is integrated with an innovative sensor wrapper that integrates Commercial Off The Shelf (COTS) components from National Instruments (NI) to connect a legacy equipment to the SM platform. Here, an advanced analytics engine with modules customizable for both high-performance computing and shop floor environments was integrated into the commercial web service (from Amazon) to provide real-time monitoring and anomaly detection capability. This implementation indicates the potential of SM platform to achieve drastic reductions in the time and effort taken towards qualification of advanced materials and manufacturing technologies.

scholarly journals An optimized and virtual on-machine measurement planning model

2020 ◽  
Vol 48 (4) ◽  
pp. 745-752
Author(s):  
Slavenko Stojadinović ◽  
Numan Durakbasa ◽  
Saša Živanović
Keyword(s):  
Machine Tool ◽  
Manufacturing Systems ◽  
Dimensional Accuracy ◽  
Planning Model ◽  
Prismatic Parts ◽  
Custom Made ◽  
G Code ◽  
Measurement Planning ◽  
Measurement Path ◽  
Planning Methodology

The specific needs of customers set requirements like flexibility and custom-made products, as well as quick placement of products on the markets. Mass customization responds to these requirements and imposes new demands inside manufacturing systems such as optimization and virtualization of machining and measurement processes. A contribution in that direction is presented in this paper, pertaining to development and verification an on-machine measurement planning model. The aim of the verification is to visualize collision check between the measuring head placed in the tool holder and the workpiece on the machine tool working table. The virtual on-machine measurement was realized on the configured virtual machine tool LOLA HBG80 in the CAD environment. The measurement path is generated by a new planning methodology, then optimized using ants colony, programed and verified by simulations through few examples of standard forms of tolerance. The output of the simulation process is the G-code for real on-machine measurement for prismatic parts of medium and rough dimensional accuracy.

scholarly journals The Estimation of Heritability of Weekly Body Weights in Japanese Quails with MINQUE(Minimum Quadratic Unbiased Estimation) Method

2016 ◽  
Vol 4 (1) ◽  
pp. 58
Author(s):  
Gökhan Tamer Kayaalp ◽  
Mikail Baylan ◽  
Sibel Canoğulları
Keyword(s):  
Variance Components ◽  
Estimation Method ◽  
Estimation Methods ◽  
Unbiased Estimation ◽  
Coturnix Coturnix ◽  
Japanese Quails ◽  
Body Weights

In this study the heritability of body weights of Japanese quails (Coturnix coturnix Japanica) were estimated by using MINQUE (Minimum Quadratic Unbiased Estimation) methods. Firstly the variance components were estimated by using MINQUE method which were later estimated the heritability for weekly body weights. The estimation of heritability of body weights are following: for third week : 0.302±0.018; for fourth week: 0.70±0.15; for fifth week : 0.30±0.067

Software Patterns for Modelling Discrete-Part Manufacturing Systems Using Objects

2003 ◽  
Vol 23 (1) ◽  
pp. 29-42 ◽  
Author(s):  
S. Narayanan ◽  
D. Bodner ◽  
U. Sreekanth ◽  
T. Govindaraj ◽  
L. McGinnis ◽  
...  
Keyword(s):  
Manufacturing Systems ◽  
Software Patterns ◽  
Discrete Part

Tool Deflection Error of Three-Axis Computer Numerical Control Milling Machines, Monitoring and Minimizing by a Virtual Machining System

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Mohsen Soori ◽  
Behrooz Arezoo ◽  
Mohsen Habibi
Keyword(s):  
Manufacturing Systems ◽  
Optimization Technique ◽  
Dimensional Accuracy ◽  
Virtual Manufacturing ◽  
Numerical Control ◽  
Free Form ◽  
Machining Parameters ◽  
Tool Deflection ◽  
Virtual Machining ◽  
Machining System

Virtual manufacturing systems carry out the simulation of manufacturing processes in digital environment in order to increase accuracy as well as productivity in part production. There are different error sources in machine tools, such as tool deflection, geometrical deviations of moving axis, and thermal distortions of machine tool structures. The errors due to tool deflection are caused by cutting forces and have direct effects on dimensional accuracy, surface roughness of the parts, and efficient life of the cutting tool, holder, and spindle. This paper presents an application of virtual machining systems in order to improve the accuracy and productivity of part manufacturing by monitoring and minimizing the tool deflection error. The tool deflection error along machining paths is monitored to present a useful methodology in controlling the produced parts with regard to desired tolerances. Suitable tool and spindle can also be selected due to the ability of error monitoring. In order to minimize the error, optimization technique based on genetic algorithms is used to determine optimized machining parameters. Free-form profile of virtual and real machined parts with tool deflection error is compared in order to validate reliability as well as accuracy of the software.

Management Strategies for the Design, Control and Operation of Flexible Manufacturing Systems

1983 ◽  
Vol 27 (4) ◽  
pp. 297-301 ◽  
Author(s):  
Sheue-Ling Hwang ◽  
Joseph Sharit ◽  
Gavriel Salvendy
Keyword(s):  
Process Control ◽  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Optimal Allocation ◽  
Management Strategies ◽  
Decision Processes ◽  
Research Strategies ◽  
Discrete Part

The roles of the supervisor in computerized process control and computerized discrete part manufacturing are compared. Research strategies aimed at understanding the decision processes of the operator in supervising flexible manufacturing systems are outlined and methodologies for assessing the optimal allocation of functions between the human and computer in flexible manufacturing systems are discussed.

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