Browsing by Author "Wang, Yu-Cheng"

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A Collaborative and Ubiquitous System for Fabricating Dental Parts Using 3D Printing Technologies
(2019-09-01)
Wang, Yu-Cheng
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Chen, Toly
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Lin, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Three-dimensional (3D) printing has great potential for establishing a ubiquitous service in the medical industry. However, the planning, optimization, and control of a ubiquitous 3D printing network have not been sufficiently discussed. Therefore, this study established a collaborative and ubiquitous system for making dental parts using 3D printing. The collaborative and ubiquitous system split an order for the 3D printing facilities to fulfill the order collaboratively and forms a delivery plan to pick up the 3D objects. To optimize the performance of the two tasks, a mixed-integer linear programming (MILP) model and a mixed-integer quadratic programming (MIQP) model are proposed, respectively. In addition, slack information is derived and provided to each 3D printing facility so that it can determine the feasibility of resuming the same 3D printing process locally from the beginning without violating the optimality of the original printing and delivery plan. Further, more slack is gained by considering the chain effect between two successive 3D printing facilities. The effectiveness of the collaborative and ubiquitous system was validated using a regional experiment in Taichung City, Taiwan. Compared with two existing methods, the collaborative and ubiquitous 3D printing network reduced the manufacturing lead time by 45% on average. Furthermore, with the slack information, a 3D printing facility could make an independent decision about the feasibility of resuming the same 3D printing process locally from the beginning.
A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice
(2019-12-01)
Chen, Tin-Chih Toly
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Wang, Yu-Cheng
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Lin, Yu-Cheng
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Wu, Hsin-Chieh
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Lin, Hai-Fen
A fuzzy collaborative approach is proposed in this study to assess the suitability of a smart health practice, which is a challenging task, as the participating decision makers may not reach a consensus. In the fuzzy collaborative approach, each decision maker first applies the alpha-cut operations method to derive the fuzzy weights of the criteria. Then, fuzzy intersection is applied to aggregate the fuzzy weights derived by all decision makers to measure the prior consensus among them. The fuzzy intersection results are then presented to the decision makers so that they can subjectively modify the pairwise comparison results to bring them closer to the fuzzy intersection results. Thereafter, the consensus among decision makers is again measured. The collaboration process will stop when no more modifications are made by any decision maker. Finally, the fuzzy weighted mean-centroid defuzzification method is applied to assess the suitability of a smart health practice. The fuzzy collaborative approach and some existing methods have been applied to assess the suitabilities of eleven smart health practices for a comparison. Among the compared practices, only the fuzzy collaborative approach could guarantee the existence of a full consensus among decision makers after the collaboration process, i.e., that the assessment results were acceptable to all decision makers.
A fuzzy collaborative forecasting approach considering experts' unequal levels of authority
(2020-09-01)
Chen, Tin-Chih Toly
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Wang, Yu-Cheng
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Lin, Chi-Wei
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工業工程與管理學系
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Department of Industrial Engineering and Management
Experts typically have unequal authority levels in collaborative forecasting tasks. Most current fuzzy collaborative forecasting methods address this problem by applying a (fuzzy) weighted average to aggregate experts' fuzzy forecasts. However, the aggregation result may be unreasonable, hence fuzzy weighted intersection operators have been proposed for fuzzy collaborative forecasting. This paper proposes that unequal expert authority levels are considered when deriving the membership function rather than the aggregation value. Therefore, the membership of a value in the aggregation result cannot exceed those in experts' fuzzy forecasts. The proposed approach was applied to forecast the yield of a dynamic random access memory product to validate its effectiveness. Experimental results showed that the proposed methodology outperformed all current best-practice methods considered in every aspect, and in particular achieving 65% mean root mean square error reduction. Thus, a high expert authority level increased the likelihood for the forecast, which could not be satisfactorily addressed by simply applying a higher weight to the forecast. (C) 2020 Elsevier B.V. All rights reserved.
A fuzzy collaborative intelligence approach for estimating future yield with DRAM as an example
(2018-10-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
To increase the ecological sustainability of manufacturing, enhancing the yield of each product is a critical task that eliminates waste and increases profitability. An equally crucial task is to estimate the future yield of each product so that the majority of factory capacity can be allocated to products that are expected to have higher yields. To this end, a fuzzy collaborative intelligence (FCI) approach is proposed in this study. In this FCI approach, a group of domain experts is formed. Each expert constructs an artificial neural work (ANN) to fit an uncertain yield learning process for estimating the future yield with a fuzzy value; in past studies, however, uncertain yield learning processes were modeled only by solving mathematical programming problems. In this research, fuzzy yield estimates from different experts were aggregated using fuzzy intersection. Then, the aggregated result was defuzzified with another ANN. A real dynamic random access memory case was utilized to validate the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology outperformed five existing methods in improving the estimation accuracy, which was measured in terms of the mean absolute error and the mean absolute percentage error.
A fuzzy mid-term capacity and production planning model for a manufacturing system with cloud-based capacity
(1970-01-01)
Chen, Tin-Chih Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Most of the past cloud manufacturing (CMfg) studies investigated the short-term production planning or job scheduling of a CMfg system, while the mid-term or long-term capacity and production planning of a CMfg system has rarely been addressed. In addition, most existing methods are suitable for CMfg systems comprising three-dimensional (3D) printers, computer numerical control (CNC) machines or robots, but ignore the coordination and transportation required for moving jobs across factories. To fill these gaps, a fuzzy mid-term capacity and production planning model for a manufacturer with cloud-based capacity is proposed in this study. The proposed methodology guides a manufacturer in choosing between non-cloud-based capacity and cloud-based capacity. It can be applied to factories utilizing machines with different degrees of automation including highly automatic equipment (such as 3D printers, CNC machines, and robots) and lowly automatic (legacy) machines, while existing methods assume that orders can be easily transferred between machines that are often highly automatic. In the proposed methodology, first, various types of capacity are unequally prioritized. Then, a fuzzy mixed-integer nonlinear programming model is formulated and optimized to make the mid-term or long-term capacity and production plan of a factory. The fuzzy capacity and production planning model is designed for factories with parallel machines. The proposed methodology has been applied to a case to illustrate its applicability. According to the experimental results, the proposed methodology successfully reduced total costs by up to 8%. The advantage of the proposed methodology over existing practices in fulfilling customers' demand was also obvious.
A Partial-Consensus Posterior-Aggregation FAHP Method-Supplier Selection Problem as an Example
(2019-02-01)
Wang, Yu-Cheng
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Chen, Tin-Chih Toly
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工業工程與管理學系
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Department of Industrial Engineering and Management
Existing fuzzy analytic hierarchy process (FAHP) methods usually aggregate the fuzzy pairwise comparison results produced by multiple decision-makers (DMs) rather than the fuzzy weights estimations. This is problematic because fuzzy pairwise comparison results are subject to uncertainty and lack consensus. To address this problem, a partial-consensus posterior-aggregation FAHP (PCPA-FAHP) approach is proposed in this study. The PCPA-FAHP approach seeks a partial consensus among most DMs instead of an overall consensus among all DMs, thereby increasing the possibility of reaching a consensus. Subsequently, the aggregation result is defuzzified using the prevalent center-of-gravity method. The PCPA-FAHP approach was applied to a supplier selection problem to validate its effectiveness. According to the experimental results, the PCPA-FAHP approach not only successfully found out the partial consensus among the DMs, but also shrunk the widths of the estimated fuzzy weights to enhance the precision of the FAHP analysis.
A type-II fuzzy collaborative forecasting approach for productivity forecasting under an uncertainty environment
(1970-01-01)
Chen, Toly
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Wang, Yu-Cheng
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Chiu, Min-Chi
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工業工程與管理學系
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Department of Industrial Engineering and Management
Forecasting factory productivity is a critical task. However, it is not easy owing to the uncertainty of productivity. Existing methods often forecast productivity using a fuzzy number. However, the range of a fuzzy productivity forecast is wide owing to the consideration of extreme cases. In this study, a fuzzy collaborative forecasting approach is proposed to forecast factory productivity using a type-II fuzzy number and by narrowing the forecast's range. The outer section of the type-II fuzzy number determines the range of productivity, while the inner section is defuzzified to derive the most likely value. Based on the experimental results, the proposed methodology surpassed existing methods in improving forecasting precision and accuracy, with a reduction in the mean absolute percentage error (MAPE) of up to 74%.
Advanced 3D printing technologies for the aircraft industry: a fuzzy systematic approach for assessing the critical factors
(2019-12-01)
Wang, Yu-Cheng
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Chen, Toly
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Yeh, Yung-Lan
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工業工程與管理學系
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Department of Industrial Engineering and Management
In the aircraft industry, three-dimensional (3D) printing can confer several benefits, such as shortened cycle times, reduced production costs, and lighter part weights. However, some concerns must be addressed for 3D-printing applications to be viable. This paper investigated these concerns by reviewing the current 3D printing practices in the aircraft industry. The literature review identified five factors critical to the applicability of advanced 3D printing technologies to the aircraft industry, and a fuzzy systematic approach was applied to assess the applicability and relative importance of the identified factors, combining fuzzy geometric mean and fuzzy analytical hierarchy process. The findings provide valuable input for countries or regions considering expanding 3D printing applications to their aircraft industries.
Advanced aircraft manufacturing and maintenance using three-dimensional printing
(1970-01-01)
Chen, Tin-Chih Toly
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Choi, Seung-Kyum
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Moon, Seung Ki
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Wang, Yu-Cheng
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工業工程與管理學系
An advanced fuzzy approach for modeling the yield improvement of making aircraft parts using 3D printing
(2019-12-01)
Chen, Toly
;
Wang, Yu-Cheng
;
工業工程與管理學系
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Department of Industrial Engineering and Management
Forecasting the yield is critical for making aircraft parts using three-dimensional (3D) printing. However, the existing methods for the yield forecasting exhibit a common problem: they employ the logarithmic or log-sigmoid value, rather than the original value, of the yield to simplify the computation. To address this problem, an advanced fuzzy approach was proposed in this study. The focus of this study is to investigate the effectiveness of the advanced fuzzy approach for forecasting the yield of a 3D-printed aircraft part. The advanced fuzzy approach derived the direct-solution (DS) versions of the existing fuzzy yield learning models. These DS versions use the original yield value directly, thereby optimizing the forecasting performance. The proposed methodology was applied to the process of making an aircraft part using 3D printing to evaluate its effectiveness. Experimental results revealed significant improvements in the forecasting accuracy of the proposed methodology compared with the aforementioned methods. Furthermore, when the proposed methodology was applied to various fuzzy yield learning models, different improvements were obtained.
An advanced IoT system for assisting ubiquitous manufacturing with 3D printing
(2019-08-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
An advanced Internet-of-things (IoT) system for assisting ubiquitous manufacturing with three-dimensional (3D) printing was designed. The system receives orders from customers on the move online and then distributes the required pieces to nearby 3D printing facilities. After the printing is completed, a freight truck visits the printing facilities sequentially to collect the printed pieces. To minimize the cycle time for delivering the order, an optimization approach that combines workload balancing and finding the shortest delivery path was proposed in this study. However, the optimization problem remained difficult; to solve this, an algorithm was developed. The effectiveness of the proposed methodology was assessed through an experiment conducted in Taichung City, Taiwan. According to the experimental results, the proposed methodology outperforms two existing methods by reducing the cycle times by an average of 33%. It also successfully balances the workloads of the 3D printing facilities, incentivizing these facilities to join the system.
An evolving fuzzy planning mechanism for a ubiquitous manufacturing system
(2020-06-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Existing ubiquitous manufacturing (UM) systems usually plan production and logistics simultaneously to enhance efficiency. A UM system can be further benefited by pursuing economical fabrication, improving product quality, and enhancing partner relationships, which involves possibly conflicting goals, but has rarely been investigated. To address this, an evolving fuzzy approach is proposed in this study by hybridizing diversifying fuzzy analytic hierarchy process (DFAHP), genetic algorithm (GA), and fuzzy technique for order preference by similarity to the ideal solution (FTOPSIS). The evolving fuzzy approach has been applied to a UM system. The superiority of the evolving fuzzy approach over existing methods in meeting various goals was successfully demonstrated by the experimental results.
An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach
(2020-04-01)
Chen, Tin-Chih Toly
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Wang, Yu-Cheng
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Huang, Chin-Hau
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工業工程與管理學系
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Department of Industrial Engineering and Management
Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.
Enhancement of Polar Phases in PVDF by Forming PVDF/SiC Nanowire Composite
(2016-12)
Huang, Jie-Fang
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Han, Song-Jia
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Chen, Hui-Jiuan
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Liu, Gui-Shi
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Li, Gong-Tan
Different contents of silicon carbide (SiC) nanowires were mixed with Poly(vinylidene fluoride) (PVDF) to facilitate the polar phase crystallization. It was shown that the annealing temperature and SiC content affected on the phase and crystalline structures of PVDF/SiC samples. Furthermore, the addition of SiC nanowire enhanced the transformation of non -polar a phase to polar phases and increased the relative fraction of 13 phase in PVDF. Due to the nucleating agent mechanism of SiC nanowires, the ion dipole interaction between the negatively charged surface of SiC nanowires and the positive CH2 groups in PVDF facilitated the formation of polar phases in PVDF.
Enhancement of Polar Phases in PVDF by Forming PVDF/SiC Nanowire Composite
(2016-12)
Huang, Jie-Fang
;
Han, Song-Jia
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Chen, Hui-Jiuan
;
Liu, Gui-Shi
;
Li, Gong-Tan
Different contents of silicon carbide (SiC) nanowires were mixed with Poly(vinylidene fluoride) (PVDF) to facilitate the polar phase crystallization. It was shown that the annealing temperature and SiC content affected on the phase and crystalline structures of PVDF/SiC samples. Furthermore, the addition of SiC nanowire enhanced the transformation of non -polar a phase to polar phases and increased the relative fraction of 13 phase in PVDF. Due to the nucleating agent mechanism of SiC nanowires, the ion dipole interaction between the negatively charged surface of SiC nanowires and the positive CH2 groups in PVDF facilitated the formation of polar phases in PVDF.
Evaluating the Suitability of a Smart Technology Application for Fall Detection Using a Fuzzy Collaborative Intelligence Approach
(2019-11-01)
Lin, Yu-Cheng
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Wang, Yu-Cheng
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Chen, Tin-Chih Toly
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Lin, Hai-Fen
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工業工程與管理學系
Fall detection is a critical task in an aging society. To fulfill this task, smart technology applications have great potential. However, it is not easy to choose a suitable smart technology application for fall detection. To address this issue, a fuzzy collaborative intelligence approach is proposed in this study. In the fuzzy collaborative intelligence approach, alpha-cut operations are applied to derive the fuzzy weights of criteria for each decision maker. Then, fuzzy intersection is applied to aggregate the fuzzy weights derived by all decision makers. Subsequently, the fuzzy technique for order preference by similarity to the ideal solution is applied to assess the suitability of a smart technology application for fall detection. The fuzzy collaborative intelligence approach is a posterior-aggregation method that guarantees a consensus exists among decision makers. After applying the fuzzy collaborative intelligence approach to assess the suitabilities of four existing smart technology applications for fall detection, the most and least suitable smart technology applications were smart carpet and smart cane, respectively. In addition, the ranking result using the proposed methodology was somewhat different from those using three existing methods.
Interval fuzzy number-based approach for modeling an uncertain fuzzy yield learning process
(2020-03-01)
Chen, Toly
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Wang, Yu-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
An interval fuzzy number-based approach was proposed in this study to model an uncertain yield learning process. The study aimed to overcome the limitations of present methods, wherein the lower and upper bounds of the yield are generally determined by few extreme cases, thus resulting in an unacceptable widening of the yield range. In the proposed interval fuzzy number-based approach, the range of yield was divided into two sections, namely inner and outer sections, which corresponded with the lower and upper membership functions of a fuzzy yield forecast based on interval fuzzy numbers, respectively. To fulfill different managerial objectives, in this approach, all actual values are included in the outer section, whereas most of these values fall within the inner section. To derive the values of parameters in a fuzzy yield learning model based on interval fuzzy numbers, a mixed binary nonlinear programming model was proposed and optimized. The interval fuzzy number-based approach was applied to two real-time cases for evaluating its effectiveness. According to experimental results, the performance of the proposed method was superior to that of several existing methods, particularly in terms of forecasting precision for the average range. Forecasting accuracy obtained using the interval fuzzy number-based approach was satisfactory.
Near-infrared Spectroscopy System for Determining Brain Hemoglobin Level
(2013)
Wu, Pei-Jung
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Chang, Ming-Hsien
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Huang, Cheng-Yang
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Wang, Yu-Cheng
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Kuo, Jinn-Rung
Traumatic brain injury (TBI) usually results from brain shaking or impact. It can affect the normal function of the brain and even cause people become disabled and death. However, there is lack of studies for the physiological changes of humans or animals under brain injury. In order to obtain the information of physiological state change, we designed and enforced a non-invasive, wireless multi-channel near-infrared spectroscopy (NIRS) for monitoring the concentration change of oxy-hemoglobin (HbO(2)), deoxy-hemoglobin (HbR) and total-hemoglobin (HbT) continuously during and after TBI. The experimental results indicated that the concentration change of HbO(2) and HbT is significantly related to the impact strength and infarction volume. Thus, this system is easily used and stable for TBI study.
Transfer printing for fabrication of flexible RGB color e-paper
(2017-06-01)
Wang, Li
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Liu, Gui-Shi
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Wang, Yu-Cheng
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Su, Jing
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Wang, Xi-Du
Electrophoretic display (EPD) has been a prevailing paper-like display technology for years, owing to its advantages of flexibility, low-power consumption, and good sunlight readability. However, it is regrettable that the black/white EPD still dominates the majority of the market while a commercially sold full-color EPD is still unavailable. In this paper, we proposed a facile yet feasible method to fabricate a color EPD by a tape-assisted transfer method, which is the first demonstration using transfer method to achieve color micro-encapsulated EPD.