Innovative design approach for product design based on TRIZ, AD, fuzzy and Grey relational analysis

Wu, Yanling; Zhou, Fei; Kong, Jizhou

doi:10.1016/j.cie.2020.106276

Cited by 59 publications

(33 citation statements)

References 42 publications

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“…In the WEDM process, the machining quality of the specimen is considerably characterized by the wire wear ratio due to its importance in evaluating the discharge energy of every pulse cycle. WWR can be calculated using the following Equation 1: [20,21]…”

Section: Methodsmentioning

confidence: 99%

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Enhancing the Surface Quality of Micro Titanium Alloy Specimen in WEDM Process by Adopting TGRA-Based Optimization

et al. 2020

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The surface measures of machined titanium alloys as dental materials can be enhanced by adopting a decision-making algorithm in the machining process. The surface quality is normally characterized by more than one quality parameter. Hence, it is very important to establish multi-criteria decision making to compute the optimal process factors. In the present study, Taguchi–Grey analysis-based criteria decision making has been applied to the input process factors in the wire EDM (electric discharge machining) process. The recast layer thickness, wire wear ratio and micro hardness have been chosen to evaluate the quality measures. It was found that the wire electrode selection was the most influential factor on the quality measures in the WEDM process, due to its significance in creating spark energy. The optimal arrangement of the input process parameters has been found using the proposed approach as gap voltage (70 V), discharge current (15 A) and duty factor (0.6). It was proved that the proposed method can enhance the efficacy of the process. Utilizing the computed combination of optimal process parameters in surface quality analysis has significantly contributed to improving the quality of machining surface.

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Section: Methodsmentioning

confidence: 99%

“…It has been found that the proposed approach can increase prediction accuracy [18,19]. Product design can be further enhanced by the TGRA method [20]. The detailed survey showed that only multi-criteria decision making (MCDM) can provide better process factors in machining processes.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Surface Quality of Micro Titanium Alloy Specimen in WEDM Process by Adopting TGRA-Based Optimization

et al. 2020

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“…, is a simple form to reconstruct the complicated pattern. The fractal interpolation method as Equations (13) and (14) is carried out to reconstruct the feature patterns with the data samples, and the resulting reconstructed feature patterns are self-affine in the same class, including normal (N) condition (25), Pt (34), E (15), In (28), M (25), P (46), F (25), and Ef (25), as seen in Fig. 4.…”

Section: Feature Patterns Reconstructionmentioning

confidence: 99%

“…The output of GRA as GG distributed between zero and one, as a "Gaussian function", can be defined as [28]:…”

Section: Multilayer Classifier Designmentioning

confidence: 99%

Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier

Lin

et al. 2020

IEEE Photonics J.

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Chest X-ray (CXR) images are usually used to identify the causes of patients' symptoms, including the classes of lung or heart disorders. In visualization examination, CXR imaging in anterior-posterior (A-P) views is a preliminary screening method used by clinicians or radiologists to diagnose possible lung abnormalities, such as pneumothorax (Pt), emphysema (E), infiltration (In), lung cancer (M), pneumonia (P), pulmonary fibrosis (F), and pleural effusion (Ef). However, the identification of the causes of multiple abnormalities associated with coexisting conditions presents a challenge. In ruling out a suspected lung disease, the signs and symptoms of physical conditions need to be identified to arrive at a definitive diagnosis. In addition, low contrast CXR images and manual inspection restrict automated screening applications. Hence, this study aims to propose an iterated function system (IFS) and a multilayer fractional-order machine learning classifier to rapidly screen the possible classes of lung diseases within regions of interest on CXR images and to improve screening accuracy. For digital image processes, a two-dimensional (2D) fractional-order convolution is used to enhance symptomatic features. The IFS with nonlinear interpolation functions is then used to reconstruct the 2D feature patterns. These reconstructed patterns are self-affine in the same class and thus help distinguish normal subjects from those with lung diseases. The accuracy rate is thus improved. Pooling is performed to reduce the dimensions of the feature patterns and speed up complex computations. A gray relational analysis-based classifier is used to identify the possible classes of the signs and symptoms of lung diseases. For digital CXR images in A-P view, the proposed multilayer machine learning classifier with k-fold cross-validation presents promising results in screening lung diseases and improving screening accuracy rate relative to traditional methods. The proposed classifier is evaluated in terms of recall (99.6%), precision (87.78%), accuracy (88.88%), and F1 score (0.9334).

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“…The structure of this combination is shown in Figure 2(a). A multilayer fully connecting network with a radial Bayesian network (RBN) [26][27] and gray relational analysis (GRA) as an adaptive algorithm [28][29][30][31][32] is employed to rapidly screen typical lung diseases. In experimental validations, anterior-posterior chest X-ray images from the National Institutes of Health (NIH) chest X-ray database (NIH Clinical Center) are enrolled [33].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Fractional-Order Machine Vision Classifier for Rapid Typical Lung Diseases Screening on Digital Chest X-Ray Images

Chen

Li³

et al. 2020

IEEE Access

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Lung diseases can result in acute breathing problems and prevent the human body from acquiring enough oxygen. These diseases, such as pneumonia (P), pleural effusion (Ef), lung cancer, pneumothorax (Pt), pulmonary fibrosis (F), infiltration (In) and emphysema (E), adversely affect airways, alveoli, blood vessels, pleura and other parts of the respiratory system. The death rates of P and lung cancer are higher than those of other typical lung diseases. In visualization examination, chest radiography, such as anterior-posterior or lateral image viewing, is a straightforward approach used by clinicians/radiologists to diagnose and locate possible lung abnormalities rapidly. However, a chest X-ray image of patients may show multiple abnormalities associated with coexisting conditions, such as P, E, F, Pt, atelectasis, lung cancer or surgical interventions, which further complicate diagnosis. In addition, poor-quality X-ray images and manual inspection have limitations in digital image-automated classification. Hence, this study intends to propose a multilayer machine vision classifier to automatically identify the possible class of lung diseases within a bounding region of interest (ROI) on a chest X-ray image. For digital image texture analysis, a two-dimensional (2D) fractional-order convolution (FOC) operation with a fractional-order parameter, v = 0.3-0.5, is used to enhance the symptomatic feature and remove unwanted noises. Then, maximum pooling is performed to reduce the dimensions of feature patterns and accelerate complex computations. A multilayer machine vision classifier with radial Bayesian network and gray relational analysis is used to screen subjects with typical lung diseases. Anterior-posterior chest X-ray images from the NIH chest X-ray database (NIH Clinical Center) are enrolled. For digital chest X-ray images, with K-fold cross-validation, the proposed multilayer machine vision classifier is applied to facilitate the diagnosis of typical lung diseases on specific bounding ROIs, as promising results with mean recall (%), mean precision (%), mean accuracy (%) and mean F1 score of 98.68%, 82.42%, 83.57% and 0.8981, respectively, for assessing the performance of proposed multilayer classifier for rapidly screening lung lesions on digital chest X-ray images.

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Innovative design approach for product design based on TRIZ, AD, fuzzy and Grey relational analysis

Cited by 59 publications

References 42 publications

Enhancing the Surface Quality of Micro Titanium Alloy Specimen in WEDM Process by Adopting TGRA-Based Optimization

Enhancing the Surface Quality of Micro Titanium Alloy Specimen in WEDM Process by Adopting TGRA-Based Optimization

Enhancement of Chest X-Ray Images to Improve Screening Accuracy Rate Using Iterated Function System and Multilayer Fractional-Order Machine Learning Classifier

Multilayer Fractional-Order Machine Vision Classifier for Rapid Typical Lung Diseases Screening on Digital Chest X-Ray Images

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