Evaluating the product quality level under multiple L-type quality characteristics

Chan, Weng Meng; Ibrahim, Raafat N; Lochert, Paul Bertram

doi:10.1007/s00170-004-2158-8

Cited by 18 publications

(4 citation statements)

References 15 publications

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“…However, the dependency between the parameters is not considered in . Extensions of the concept of QLF for considering three main categories of multiple quality characteristics including nominal‐the‐best, smaller‐the‐better, and larger‐the‐better cases are reported in where it is assumed that the quality characteristics follow multivariate distributions. To consider the dependency among quality characteristics, the Bessel function and a correlation coefficient were used to obtain the joint probability distribution of multiple quality characteristics when calculating the expected loss.…”

Section: The Loss Functionsmentioning

confidence: 99%

Loss functions and their applications in process safety assessment

Hashemi

Ahmed

Khan

2014

Process Safety Progress

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Process deviations, along with failure of control systems and protection layers, result in safety and quality loss in plant operations. This article proposes an operational risk‐based warning system design methodology based on overall system loss. Loss functions (LFs) are used to define the relationship between process deviations and system loss. For this purpose, properties associated with quadratic LF and a set of inverted probability LFs are investigated and compared. The results suggest that LFs can be used in a novel way to assess operational stability and system safety. The proposed consequence assessment methodology using LFs is then incorporated into a risk‐based warning system design model to analyze warnings associated with process deviations. A simulated case study is presented to demonstrate potential application of the proposed methodology; the study examines the response to a temperature surge for a reactor system. © 2014 American Institute of Chemical Engineers Process Saf Prog 33: 285–291, 2014

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Section: The Loss Functionsmentioning

confidence: 99%

Loss functions and their applications in process safety assessment

Hashemi

Ahmed

Khan

2014

Process Safety Progress

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“…Generally, three types of quality characteristics that have been researched are nominal-the-better (N-Type) (Kapur & Cho, 1996), larger-the-better (L-Type) (Chan, Ibrahim, & Lochert, 2005a) and smaller-the-better (S-Type) (Chan, Ibrahim, & Lochert, 2005b). N-Type means that as the quality measurement gets closer to its targeted mean value, the quality of this product increases.…”

Section: Density-based Product Inspection Metricmentioning

confidence: 99%

A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement

Tong

Ardakani

Siegel

et al. 2020

IJPHM

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Data-driven modeling and fault detection of multi-stage manufacturing processes remain challenging due to the increasing complexity of the manufacturing process, the lack of structural data, data multi-dimensionality, and the additional difficulty when dealing with large data sets. The implementation of add-on sensors and establishing data acquisition, transfer, storage and analysis has the potential to facilitate advanced data modeling techniques. However, besides the associated costs, dealing with high-volume multi-dimensional data sets can be a major challenge. This paper presents a novel methodology for early fault identification of multi-stage manufacturing processes using a statistical approach. The major advantage of the proposed methodology is its reliance on only the product quality measurements and basic product manufacturing records, given the presence of peer sets. This leads to a feasible faultidentification solution in a sensor-less environment without investing costly data collection systems. The developed methodology transforms the end-of-process quality measurements to a process performance metric based on a density-based statistical approach and a peer-to-peer comparison of the machines at one stage of the process. This approach allows one to be more proactive and identify the problematic machines that could be affecting product quality. A case study in an actual multi-stage manufacturing process is used to demonstrate the effectiveness of the developed methodology.

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“…The fourth term is the expected quality loss that is measured by the multivariate quality loss function, i.e. : Equation 5 where: Equation 6 Equation 7 Equation 8 Mathematical details of the multivariate loss functions can be found in Kapur and Cho (1996) and Chan et al (2004). To facilitate the analysis, it is assumed that the quality characteristics are of N‐type.…”

Section: The Bivariate Inventory‐planning Modelmentioning

confidence: 99%

Economic production quantity and process quality: a multivariate approach

Chan

Ibrahim²,

Lochert³

2005

International Journal of Quality &Amp; Reliability Management

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PurposeThe purpose of this paper is to study the interaction of economics of production with process quality, when multiple key quality characteristics are present. Specifically, the paper aims to analyse the possibility of investing in a production process to reduce its variances and the impact on a multivariate quality loss function.Design/methodology/approachA bivariate inventory‐planning model is developed, in which the optimal investment for reducing process variances and the optimal lot size are jointly determined. A case study with industrial data is presented to illustrate the possible solution procedures and the potential advantages of the proposed model.FindingsIt is found that by using the previous approaches to analyse the interaction between the economics of production and process quality, a company will underestimate the cost of quality, especially the expected external failure cost (quality loss), and ultimately invest less into the prevention activities to improve the process.Originality/valueThe proposed model can help managers to compare different production processes and also guide the managers towards better choices for process improvement. To the best of our knowledge, this paper is the first to integrate the economic production quantity (EPQ) problem with the process quality consideration for products with multiple quality characteristics.

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Evaluating the product quality level under multiple L-type quality characteristics

Cited by 18 publications

References 15 publications

Loss functions and their applications in process safety assessment

Loss functions and their applications in process safety assessment

A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement

Economic production quantity and process quality: a multivariate approach

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