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Journal articles on the topic 'Quality loss function'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Marinković, Velibor. "A new family of quality loss functions." FME Transactions 50, no. 4 (2022): 701–14. http://dx.doi.org/10.5937/fme2204701m.

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Taguchi first developed the quality loss function to better estimate the economic losses incurred by manufacturers and customers caused by quality characteristics being off-target. The quality loss function measures the quality loss caused by a deviation of a quality characteristic from its defined target value. Several researchers have proposed different revised loss functions for overcoming some flaws of the Taguchi loss function. This paper recommends a new family of quality loss functions, which is very flexible, simple, and easy to implement. Three real case studies demonstrated the usabi

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2

Jin, Qiu, and Shao Gang Liu. "Research of Asymmetric Quality Loss Function with Triangular Distribution." Advanced Materials Research 655-657 (January 2013): 2331–34. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.2331.

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The asymmetric quality loss functions with the triangular distribution for determining the optimum process mean are studied. The condition of using the linear and quadratic asymmetric quality loss function in the model is considered. The eight mathematical models under an asymmetric quality loss function with the triangular distribution based on the analysis of the linear and quadratic asymmetric quality loss function are presented. Finally, the validity of models is verified by the examples.

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3

Chen, Chung-Ho. "Specification limit under a quality loss function." Journal of Applied Statistics 26, no. 8 (1999): 903–8. http://dx.doi.org/10.1080/02664769921918.

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4

Pedersen, Søren Nygaard, and Thomas Howard. "Data Acquisition for Quality Loss Function Modelling." Procedia CIRP 43 (2016): 112–17. http://dx.doi.org/10.1016/j.procir.2016.02.032.

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5

Cho, Byung-Rae, and Michael S. Leonard. "Identification and Extensions of Quasiconvex Quality Loss Functions." International Journal of Reliability, Quality and Safety Engineering 04, no. 02 (1997): 191–204. http://dx.doi.org/10.1142/s0218539397000138.

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This paper presents a set of related quasiconvex quality loss functions. Characteristics of quasiconvex functions that are desirable for modeling quality loss are noted. Three frequently used univariate quasiconvex quality loss functions are discussed. Bivariate and multivariate quasiconvex quality loss functions are developed. A set of necessary and sufficient conditions is established for the quasiconvexity of multivariate quality loss functions. An industrial product example is used to illustrate the development of a bivariate quadratic quality loss function.

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6

Shilpa, M., and N. V. R. Naidu. "Quantitative evaluation of quality loss for fraction defective case using Taguchi's quality loss function." International Journal of Logistics Systems and Management 18, no. 1 (2014): 126. http://dx.doi.org/10.1504/ijlsm.2014.062124.

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7

Siregar, Khawarita, Aulia Ishak, Farida Ariani, and Richard Spencer. "Quality Assessment Using Quality Loss Function Method in PT. QRS." IOP Conference Series: Materials Science and Engineering 1003 (December 29, 2020): 012104. http://dx.doi.org/10.1088/1757-899x/1003/1/012104.

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8

Perona, Marco. "Manufacturing conformity assessment through Taguchi’s quality loss function." International Journal of Quality & Reliability Management 15, no. 8/9 (1998): 931–46. http://dx.doi.org/10.1108/02656719810199024.

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9

Yacout, Soumaya, and Jacqueline Boudreau. "Assessment of quality activities using Taguchi's loss function." Computers & Industrial Engineering 35, no. 1-2 (1998): 229–32. http://dx.doi.org/10.1016/s0360-8352(98)00071-0.

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10

Zhang, Jun, Wei Li, Kaibo Wang, and Ran Jin. "Process adjustment with an asymmetric quality loss function." Journal of Manufacturing Systems 33, no. 1 (2014): 159–65. http://dx.doi.org/10.1016/j.jmsy.2013.10.001.

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11

Ohkubo, Masato, and Yasushi Nagata. "QUALITY DESIGN BASED ON TAGUCHI�S LOSS FUNCTION." International Journal of Business Research 18, no. 2 (2018): 5–14. http://dx.doi.org/10.18374/ijbr-18-2.1.

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12

Freiesleben, Johannes. "A proposal for an economic quality loss function." International Journal of Production Economics 113, no. 2 (2008): 1012–24. http://dx.doi.org/10.1016/j.ijpe.2007.12.005.

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13

Kapur, Kailash C., Shivakumar Raman, and P. Simin Pulat. "Methodology for tolerance design using quality loss function." Computers & Industrial Engineering 19, no. 1-4 (1990): 254–57. http://dx.doi.org/10.1016/0360-8352(90)90116-4.

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14

Li, Shuangshuang, Xintian Liu, Yansong Wang, and Xiaolan Wang. "A cubic quality loss function and its applications." Quality and Reliability Engineering International 35, no. 4 (2019): 1161–79. http://dx.doi.org/10.1002/qre.2451.

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15

Suhr, Richard, and Robert G. Batson. "CONSTRAINED MULTIVARIATE LOSS FUNCTION MINIMIZATION." Quality Engineering 13, no. 3 (2001): 475–83. http://dx.doi.org/10.1080/08982110108918676.

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Chan, W. M., R. N. Ibrahim, and P. B. Lochert. "Quality evaluation model using loss function for multiple S-type quality characteristics." International Journal of Advanced Manufacturing Technology 26, no. 1-2 (2004): 98–101. http://dx.doi.org/10.1007/s00170-003-1980-8.

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17

Sharma, Naresh K., and Elizabeth A. Cudney. "Quality loss function for bivariate response – unified methodology." International Journal of Quality Engineering and Technology 2, no. 3 (2011): 229. http://dx.doi.org/10.1504/ijqet.2011.041229.

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18

HUI, Y. V., and L. C. LEUNG. "OPTIMAL ECONOMIC TOOL REGRINDING WITH TAGUCHI'S QUALITY LOSS FUNCTION." Engineering Economist 39, no. 4 (1994): 313–31. http://dx.doi.org/10.1080/00137919408903132.

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19

Chen, C. H., C. Y. Chou, and K. W. Huang. "Determining the Optimum Process Mean Under Quality Loss Function." International Journal of Advanced Manufacturing Technology 20, no. 8 (2002): 598–602. http://dx.doi.org/10.1007/s001700200196.

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20

Yao, Yi Yong, Li Ping Zhao, Guo Qiang Shao, and Yong Tao Qin. "Method and Application of Multistage Machining Quality Control Based on Minimum Overall Mean Quality Loss." Advanced Materials Research 213 (February 2011): 557–61. http://dx.doi.org/10.4028/www.scientific.net/amr.213.557.

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To improve the quality control effect and decrease the manufacturing cost in the product’s multistage machining processes, based on the quality loss of single stage machining process, an overall mean quality loss model of multistage machining processes is established by the integration of Duncan cost model, Taguchi quality loss model, mean shift index and average time to signal (ATS). By using this quality loss model, the overall mean quality loss optimal function of multistage machining processes is constructed and the control parameters of this function are optimally solved. Besides, The She

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21

Zheng, Ao, Kaichao Liang, Li Zhang, and Yuxiang Xing. "A CT image feature space (CTIS) loss for restoration with deep learning-based methods." Physics in Medicine & Biology 67, no. 5 (2022): 055010. http://dx.doi.org/10.1088/1361-6560/ac556e.

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Abstract Objective. Deep learning-based methods have been widely used in medical imaging field such as detection, segmentation and image restoration. For supervised learning methods in CT image restoration, different loss functions will lead to different image qualities which may affect clinical diagnosis. In this paper, to compare commonly used loss functions and give a better alternative, we studied a widely generalizable framework for loss functions which are defined in the feature space extracted by neural networks. Approach. For the purpose of incorporating prior knowledge, a CT image fea

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22

Choi, Hoo-Gon R., Man-Hee Park, and Erik Salisbury. "Optimal Tolerance Allocation With Loss Functions." Journal of Manufacturing Science and Engineering 122, no. 3 (1999): 529–35. http://dx.doi.org/10.1115/1.1285918.

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The tolerance allocation problem is formulated as a nonlinear integer model under the constraints of process capability. The problem is to minimize the sum of machining cost and quality loss. When the statistical tolerance limits are used and Taguchi’s quadratic loss function is defined, the total cost function becomes a convex function for a given feature and process. A complex search method is used to solve the model and ensure the optimal tolerance allocation. Numerical examples are presented demonstrating successful model implementation for both linear and nonlinear design functions. [S108

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23

Hong, Hyeong Gwon, Yooshin Cho, Hanbyel Cho, Jaesung Ahn, and Junmo Kim. "Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 12473–81. http://dx.doi.org/10.1609/aaai.v38i11.29140.

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Gradient inversion attacks can leak data privacy when clients share weight updates with the server in federated learning (FL). Existing studies mainly use L2 or cosine distance as the loss function for gradient matching in the attack. Our empirical investigation shows that the vulnerability ranking varies with the loss function used. Gradient norm, which is commonly used as a vulnerability proxy for gradient inversion attack, cannot explain this as it remains constant regardless of the loss function for gradient matching. In this paper, we propose a loss-aware vulnerability proxy (LAVP) for th

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24

Bala, Mohan V., and Josephine Mauskopf. "ESTIMATING THE BAYESIAN LOSS FUNCTION." International Journal of Technology Assessment in Health Care 17, no. 1 (2001): 27–37. http://dx.doi.org/10.1017/s0266462301104046.

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Current health economic literature does not provide clear guidelines on how uncertainty around cost-effectiveness estimates should be incorporated into economic decision models. Bayesian analysis is a promising alternative to classical statistics for incorporating uncertainty in economic analysis. Estimating a loss function that relates outcomes to societal welfare is a key component of Bayesian decision analysis. Health economists commonly compute the loss function based on the quality-adjusted life-years associated with each outcome. However, if welfare economics is adopted as the theoretica

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25

Wang, Bo, Qikai Li, Chen Liu, Zihan Chen, and Xiangtian Nie. "Establishment and Application of a Grey Quality Gain–Loss Function Model." Processes 10, no. 3 (2022): 495. http://dx.doi.org/10.3390/pr10030495.

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Based on Grey System Theory and the inverted normal quality gain-loss function, the inverted normal grey quality gain-loss function model is put forward. According to the constant compensation and hyperbolic tangent compensation, the grey quality gain-loss function model with nominal-type characteristics, larger-the-better characteristics and smaller-the-better characteristics is built. A multivariate grey quality gain-loss function model with multiple sub quality indexes and the concept of grey quality gain-loss cost are proposed. Case analysis is applied to the quality control of dam concret

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26

Cao, Y., J. Mao, H. Ching, and J. Yang. "A robust tolerance optimization method based on fuzzy quality loss." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223, no. 11 (2009): 2647–53. http://dx.doi.org/10.1243/09544062jmes1451.

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Using the quality loss function developed by Taguchi, the manufacturing time and cost of a product can be reduced to improve the factory's competitiveness. However, the fuzziness in quality loss has not been considered in the Taguchi method. This article presents a fuzzy quality loss function model. First, fuzzy logic is used to describe the semantic of the quality, and the quality level is divided into several grades. Then the fuzzy quality loss function is developed utilizing the loss in monetary terms, which indicates the quality loss of each quality level and the normalized expected probab

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27

Le, Linh, Ying Xie, and Vijay V. Raghavan. "KNN Loss and Deep KNN." Fundamenta Informaticae 182, no. 2 (2021): 95–110. http://dx.doi.org/10.3233/fi-2021-2068.

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The k Nearest Neighbor (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we present two loss functions

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28

Vasseur, H., T. R. Kurfess, and J. Cagan. "Use of a Quality Loss Function to Select Statistical Tolerances." Journal of Manufacturing Science and Engineering 119, no. 3 (1997): 410–16. http://dx.doi.org/10.1115/1.2831121.

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In this paper, we present a method for the selection of processes to manufacture various parts of an assembly by establishing a compromise between product quality and part manufacturing cost. We quantify the impact the precision of a part characteristic has on the overall quality of a product by using a standard Taguchi loss function. Part manufacturing cost is modeled as a function of process precision (i.e., standard deviation of the output characteristic) as opposed to previous models where manufacturing cost is a function of part tolerance. This approach is more realistic and does not assu

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29

Kulisz, Monika, Grzegorz Kłosowski, Tomasz Rymarczyk, Jolanta Słoniec, Konrad Gauda, and Wiktor Cwynar. "Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors." Energies 17, no. 3 (2024): 681. http://dx.doi.org/10.3390/en17030681.

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This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model’s loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better recon

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30

Yoon, Y. J., H. Kim, and T. J. Yoo. "Modified Loss Function for the Quality Management in Service Industry." Advanced Science Letters 23, no. 10 (2017): 9403–6. http://dx.doi.org/10.1166/asl.2017.9712.

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31

Mak, T. K., and F. Nebebe. "Minimizing a general loss function in off-line quality control." Applied Stochastic Models in Business and Industry 18, no. 1 (2002): 75–85. http://dx.doi.org/10.1002/asmb.455.

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32

Lee, Geon Woo, and Hong Kook Kim. "Cluster-Based Pairwise Contrastive Loss for Noise-Robust Speech Recognition." Sensors 24, no. 8 (2024): 2573. http://dx.doi.org/10.3390/s24082573.

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This paper addresses a joint training approach applied to a pipeline comprising speech enhancement (SE) and automatic speech recognition (ASR) models, where an acoustic tokenizer is included in the pipeline to leverage the linguistic information from the ASR model to the SE model. The acoustic tokenizer takes the outputs of the ASR encoder and provides a pseudo-label through K-means clustering. To transfer the linguistic information, represented by pseudo-labels, from the acoustic tokenizer to the SE model, a cluster-based pairwise contrastive (CBPC) loss function is proposed, which is a self-

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33

Bakır, S., N. Penbegül, R. Gün, et al. "Relationship between hearing loss and sexual dysfunction." Journal of Laryngology & Otology 127, no. 2 (2012): 142–47. http://dx.doi.org/10.1017/s0022215112002952.

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AbstractObjective:Deafness may be one of the factors that leads to a change in sexual function. This study aimed to assess sexual function, in particular erectile dysfunction, in male patients with hearing loss.Materials and methods:We studied two groups: (1) adult men with acquired, bilateral, sensorineural hearing loss, and (2) healthy, adult, married men demonstrated to have normal hearing levels, as the control group. Sexual function was assessed using the International Index of Erectile Functions questionnaire, and quality of life using the 36-Item Short-Form Health Survey.Results:There w

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34

Arquam, Haris, Vibek Kumar Sharma, Ajit Bahadur Verma, and Jaidev Kumbhakar. "An overview on Taguchi's method employed for product quality improvement and its control." Radius: Journal of Science and Technology 1, no. 1 (2024): 241001. https://doi.org/10.5281/zenodo.14900600.

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Genich Taguchi, a Japanese engineer and statistician, developed a methodology for improving and controlling the quality of produced goods. His robust optimization technique is widely used in the field of quality improvement and experimental design. Taguchi's approach aims to improve the quality of products and processes by minimizing variation and reducing the sensitivity of a system to various factors. The statistical approach for quality is valid for the various areas of engineering such as product design and development, life-sciences, and management fields (basically advertising and market

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35

Guo, Cheng, Xinyuan Zhang, Mi He, Linling Wang, and Xuanming Yang. "Research on Voltage Sag Loss Assessment Based on a Two-Stage Taguchi Quality Perspective Method." Symmetry 16, no. 3 (2024): 328. http://dx.doi.org/10.3390/sym16030328.

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Voltage sags resulting from symmetrical or asymmetrical faults pose a significant threat to power quality. In response to this challenge, a voltage sag loss assessment method based on a two-stage Taguchi quality perspective approach is proposed to address the quantitative analysis of voltage sag economic losses. Initially, using the Taguchi quality perspective method, single-index quality loss functions are separately established for voltage sag magnitude and fault duration. Subsequently, by introducing a comprehensive load tolerance curve, sensitivity parameters within the quality loss functi

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36

Wang, Bo, Zhiyong Li, Junyan Gao, and Heap Vaso. "Critical Quality Source Diagnosis for Dam Concrete Construction Based on Quality Gain - loss Function." Journal of Engineering Science and Technology Review 7, no. 2 (2014): 137–51. http://dx.doi.org/10.25103/jestr.072.22.

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37

Antony, J. "Simultaneous Optimisation of Multiple Quality Characteristics in Manufacturing Processes Using Taguchi's Quality Loss Function." International Journal of Advanced Manufacturing Technology 17, no. 2 (2001): 134–38. http://dx.doi.org/10.1007/s001700170201.

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38

Landge, Aniket, Sakshi Avhad, Pratiksha Patil, Tanvi Dube, and Shivaji Vasekar. "Image Style Transfer Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2856–65. http://dx.doi.org/10.22214/ijraset.2022.42788.

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Abstract: In order to increase the quality of the composite image throughout the image style transfer process. This study presents an improved style loss function-based image style transfer method: the improved Gram matrix calculates the inner product of the feature map and the spatial transformation map, and then the new style loss function. At the same time, the weighted algebraic sum of the two loss functions is utilized as the neural network's total loss function, which is merged with the content loss function. To generate the style-transferred image, the gradient descent algorithm is empl

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39

ZEYBEK, Melis. "Process capability: A New Criterion for Loss Function–Based Quality Improvement." Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 22, Special (2018): 470. http://dx.doi.org/10.19113/sdufbed.73367.

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40

Chou, Chao-Yu. "Set the Optimum Process Parameters Based on Asymmetric Quality Loss Function." Quality & Quantity 38, no. 1 (2004): 75–79. http://dx.doi.org/10.1023/b:ququ.0000013248.33983.25.

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41

Kim, Jungdae. "A Design of Economic CUSUM Control Chart Incorporating Quality Loss Function." Journal of Society of Korea Industrial and Systems Engineering 41, no. 4 (2018): 203–12. http://dx.doi.org/10.11627/jkise.2018.41.4.203.

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42

Köksoy, Onur, and Shu-Kai S. Fan. "An upside-down normal loss function-based method for quality improvement." Engineering Optimization 44, no. 8 (2012): 935–45. http://dx.doi.org/10.1080/0305215x.2011.620101.

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43

Bhamare, Sunil S., Om Prakash Yadav, and Ajay Rathore. "A Hybrid Quality Loss Function–Based Multi-Objective Design Optimization Approach." Quality Engineering 21, no. 3 (2009): 277–89. http://dx.doi.org/10.1080/08982110902762626.

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44

Chen, Chung-Ho. "Optimal process mean setting based on asymmetric linear quality loss function." Journal of Information and Optimization Sciences 40, no. 1 (2018): 37–41. http://dx.doi.org/10.1080/02522667.2017.1406582.

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45

Cermak, Gregory W. "Multimedia Quality as a Function of Bandwidth, Packet Loss, and Latency." International Journal of Speech Technology 8, no. 3 (2005): 259–70. http://dx.doi.org/10.1007/s10772-006-6368-3.

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46

Luo, Z. J., and D. Liu. "Evaluation of IN718 disk-forging processes using the quality-loss function." Journal of Materials Processing Technology 59, no. 4 (1996): 381–85. http://dx.doi.org/10.1016/0924-0136(95)02166-3.

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Chakraborty, Shankar, and Ankan Mitra. "A Multivariate Quality Loss Function Approach for Optimization of Spinning Processes." Journal of The Institution of Engineers (India): Series E 99, no. 1 (2018): 101–9. http://dx.doi.org/10.1007/s40034-018-0119-2.

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Cheng, Bor-Wen, and Saeed Maghsoodloo. "Optimization of mechanical assembly tolerances by incorporating Taguchi's quality loss function." Journal of Manufacturing Systems 14, no. 4 (1995): 264–76. http://dx.doi.org/10.1016/0278-6125(95)98879-b.

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Zhang, Yueyi, Lixiang Li, Mingshun Song, and Ronghua Yi. "Optimal tolerance design of hierarchical products based on quality loss function." Journal of Intelligent Manufacturing 30, no. 1 (2016): 185–92. http://dx.doi.org/10.1007/s10845-016-1238-6.

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Uluskan, Meryem. "Artificial Neural Networks as a quality loss function for Six Sigma." Total Quality Management & Business Excellence 31, no. 15-16 (2018): 1811–28. http://dx.doi.org/10.1080/14783363.2018.1520597.

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