Abstract:Multi-response optimization problems investigation is a crucial element in initiatives designed to enhance quality and overall productivity for manufacturing processes. Since no particular algorithm can be employed for all multi-response problems, defining the method that is utilized as a problem-solving technique is a vital step in the process factors optimization. Identifying a formal procedure of implementing the improvement approach in a multi-criteria decision-making problem is a critical need to ensure t… Show more
“…Therefore, an alternative optimization technique was required to optimize both responses simultaneously. Several techniques such as gray relational theory (Guldane & Dogan, 2022), desirability function approach (Chang et al, 2006), TOPSIS (Şimşek, 2019), artificial neural network (Hussein et al, 2022), and FL (Tanash et al, 2022) have been used in combination with TM to optimize multiple responses for different quality characteristics. In this study, TBFLM was utilized to optimize both response variables (FC and FS) simultaneously in model food foam.…”
In this study, the effects of soy protein isolate (SPI) concentration (0%, 50%, and 100%), sonication time (10, 20, and 30 min), and whipping time (5, 10, and 15 min) on the quality characteristics of the model food foam were investigated and optimized. In order to maximize both foaming capacity (FC) and foam stability (FS), it was aimed to select the optimum process parameters using a Taguchi‐based fuzzy logic method (TBFLM). The experiments were performed according to Taguchi L9 (33) orthogonal design, and the effects of process parameters on foam quality characteristics were investigated by analysis of variance. Optimization results revealed that optimum FC and FS values were obtained with 50% SPI, 30 min sonication, and 10 min whipping. SPI concentration was the most effective factor for the responses in Taguchi single‐response optimization. However, the whipping time has the greatest impact on the multiperformance characteristics index, accounting for 78.93% of the contribution to fuzzy logic multiresponse optimization. The verification experiments revealed that the TBFLM is a useful method for improving foam quality characteristics.
Practical applications
FC and FS are the most important properties, which represent the quality characteristics of model food foams. The Taguchi method failed in multiresponse optimization in the model food application. A Taguchi‐based Fuzzy logic optimization method was successfully applied to optimize the quality characteristics of model food foam, and the optimal conditions were determined as soy protein isolate of 50%, sonication time of 30 min, and whipping time of 10 min. The contribution of whipping time to the multiperformance characteristics index was found to be the highest.
“…Therefore, an alternative optimization technique was required to optimize both responses simultaneously. Several techniques such as gray relational theory (Guldane & Dogan, 2022), desirability function approach (Chang et al, 2006), TOPSIS (Şimşek, 2019), artificial neural network (Hussein et al, 2022), and FL (Tanash et al, 2022) have been used in combination with TM to optimize multiple responses for different quality characteristics. In this study, TBFLM was utilized to optimize both response variables (FC and FS) simultaneously in model food foam.…”
In this study, the effects of soy protein isolate (SPI) concentration (0%, 50%, and 100%), sonication time (10, 20, and 30 min), and whipping time (5, 10, and 15 min) on the quality characteristics of the model food foam were investigated and optimized. In order to maximize both foaming capacity (FC) and foam stability (FS), it was aimed to select the optimum process parameters using a Taguchi‐based fuzzy logic method (TBFLM). The experiments were performed according to Taguchi L9 (33) orthogonal design, and the effects of process parameters on foam quality characteristics were investigated by analysis of variance. Optimization results revealed that optimum FC and FS values were obtained with 50% SPI, 30 min sonication, and 10 min whipping. SPI concentration was the most effective factor for the responses in Taguchi single‐response optimization. However, the whipping time has the greatest impact on the multiperformance characteristics index, accounting for 78.93% of the contribution to fuzzy logic multiresponse optimization. The verification experiments revealed that the TBFLM is a useful method for improving foam quality characteristics.
Practical applications
FC and FS are the most important properties, which represent the quality characteristics of model food foams. The Taguchi method failed in multiresponse optimization in the model food application. A Taguchi‐based Fuzzy logic optimization method was successfully applied to optimize the quality characteristics of model food foam, and the optimal conditions were determined as soy protein isolate of 50%, sonication time of 30 min, and whipping time of 10 min. The contribution of whipping time to the multiperformance characteristics index was found to be the highest.
“…22 The PDCA cycle has four stages: Plan, Do, Check and Act. 23 The 'plan' stage refers to identifying the present situation and then developing the necessary objectives, functions and features according to the research objectives.…”
Section: App Development Methodologymentioning
confidence: 99%
“…22 The PDCA cycle has four stages: Plan, Do, Check and Act. 23 Figure 1.A Plan-Do-Check-Act (PDCA) cycle.…”
A majority of government and international organisations have chosen English as their official language since it is the only language in the world with the status of a global language and a world language. One of the key components of learning a language is oral communication; however, students frequently overlook the value of oral communication, which reduces the effectiveness of language acquisition. To enhance the motivation and effectiveness of non-native English speakers to learn and practise English, this study aims to develop an interactive self-learning and self-improvement approach. A well-designed and chatbot-assisted learning environment is designed, using a variety of concepts such as informal learning, language learning, mobile learning, and educational mobile application, and evaluated.
“…The basic concept is to obtain products of better quality with a minimized deviation from the best quality. (7) The following equation is defined for η, which stands for SNR.…”
This research was carried out to determine the appropriate range of sensing temperature and pressure of the injection mold system of the cooling fan for electronic chips of an electric vehicle. The advanced product quality planning (APQP) method and the Taguchi method were adopted following the measurement system analysis (MSA) of the International Organization for Standardization's (ISO) technical specification (TS) (ISO/TS16949) and a production part approval process (PPAP) considering the chemical and mechanical properties of PBT-4130 as the material for the production of the cooling fan. In molding the cooling fan, time, temperature, and pressure are important factors. Thus, melt temperature, holding pressure, holding time, volumepressure (V-P) point, melt injection, and cooling time in the injection mold process were investigated. Each factor was experimented with to find the optimal parameters at three different levels in the injection molding process. The result showed that the holding times of the melt at the holding pressures of 45, 55, and 65 MPa, melt temperature of 265 °C, and cooling time of 30 s were important for the production of the cooling fan. The precise and accurate sensing and control of temperatures and pressures were found to be critical for an injection mold system to manufacture high-quality products for machines or equipment. The results of this research provide important information and a basis for the appropriate use of sensors in designing an injection mold system for high-precision and high-quality plastic products such as cooling fans for electric vehicles.
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