Compensability of errors in product quality in multistage manufacturing processes

Nowadays, advanced manufacturing models such as the Stream-of-Variation (SoV) model have been successfully applied to derive the complex relationships between fixturing, manufacturing and datum errors throughout a multi-stage machining process. However, the current development of the SoV model is still based on 3-2-1 fixturing schemes and, although some improvements have been done, e.g. N-2-1 fixtures, the effect of general workholding systems such as bench vices or 3-jaw chucks has not yet been included into the model. This paper presents the extension of the SoV model to include fixture and datum errors considering both bench vices and 3-jaw chucks as a fixturing devices in multi-stage machining processes. The model includes different workholding configurations and it is shown how to include the workholding accuracy to estimate part quality. The extended SoV model is validated in a 3-stage machining process by both machining experimentation and CAD simulations.

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“…5) and presents the values shown in Eq. (13). Please, refer to Appendix A for the derivation details of T 1 and T 2 .…”

Section: B Datum-induced Errorsmentioning

confidence: 99%

Extension of the Stream-of-Variation Model for General-Purpose Workholding Devices: Vices and Three-Jaw Chucks

Moliner-Heredia

Abellán-Nebot

Peñarrocha-Alós

2022

IEEE Trans. Automat. Sci. Eng.

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“…Therefore, analysis of the error transfer and accumulation of quality characteristics in a multistage manufacturing process may lead to erroneous results. Jiao and Djurdjanovic [ 13 ] presented the compensability of error in product quality to eliminate quality errors accumulated in a multistage manufacturing process. These methods provide some opportunities for the development of modeling and quality control in a multistage manufacturing process.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Evaluation for a Manufacturing Process Based on a Directed Weighted Network

Wang

Dai

Sun

et al. 2020

Entropy

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The quality of a manufacturing process can be represented by the complex coupling relationship between quality characteristics, which is defined by the directed weighted network to evaluate the risk of the manufacturing process. A multistage manufacturing process model is established to extract the quality information, and the quality characteristics of each process are mapped to nodes of the network. The mixed embedded partial conditional mutual information (PMIME) is used to analyze the causal effect between quality characteristics, wherein the causal relationships are mapped as the directed edges, while the magnitudes of the causal effects are defined as the weight of edges. The node centrality is measured based on information entropy theory, and the influence of a node is divided into two parts, which are local and indirect effects. Moreover, the entropy value of the directed weighted network is determined according to the weighted average of the centrality of the nodes, and this value is defined as the risk of the manufacturing process. Finally, the method is verified through a public dataset.

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“…Figure 1 is a simplified working principle diagram of manufacturing system, the output of the previous station is the input of the next station during manufacturing process, due to incidental factors and system factors, each station output product quality characteristic produces a certain deviation, this deviation then enters the next station, and thus the final quality characteristic deviation of product is the result of gradual accumulation of quality characteristic deviation of all previous stations [6]. Therefore, not only are the system's input and output variables considered, but also the system's real-time statuses are followed in the control of product quality.…”

Section: Fault Diagnosis Based On Quality Control Chartmentioning

confidence: 99%

Research on Fault Diagnosis of Flexible Material R2R Manufacturing System Based on Quality Control Chart and SoV

Deng

Zhou

Xiali³

et al. 2018

Mathematical Problems in Engineering

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The Stream of Variation (SoV) model and control chart are combined to study the fault diagnosis method of flexible materials R2R manufacturing system. Based on the analysis of the correlation between the fault source and product quality in the manufacturing process and also the statistical distribution rule of the processing quality characteristic vector and the fault source , SoV model under controlled or uncontrolled states and the mathematical model of the probability distribution of the statistic 2 , of the quality characteristic variable are deduced. And the calculation equation of the centerline, the upper limit, and the lower limit of the control chart are deduced. The experimental results show that, under controlled or uncontrolled condition, when the program runs to 500 steps, the Average Run Length (ARL) of the performance parameters tends to be stable; and when program reaches 1000 steps, the actual ARL value is almost the same as the theoretical value. The fault diagnosis experiment shows that, under the condition when the fault source is strongly correlated or the fault source correlation coefficient is the same, using the control chart established in this paper can simply and quickly determine the fault location in the system.

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Compensability of errors in product quality in multistage manufacturing processes

Cited by 15 publications

References 22 publications

Extension of the Stream-of-Variation Model for General-Purpose Workholding Devices: Vices and Three-Jaw Chucks

Extension of the Stream-of-Variation Model for General-Purpose Workholding Devices: Vices and Three-Jaw Chucks

Risk Evaluation for a Manufacturing Process Based on a Directed Weighted Network

Research on Fault Diagnosis of Flexible Material R2R Manufacturing System Based on Quality Control Chart and SoV

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