Innovation Design and Development of Emotional Ceramic Tableware Based on QFD

Quality Function Deployment (QFD) has long been used as a successful design methodology in the heavy industrial and automotive industries. QFD helps designers utilize the ‘voice of the customer’, or customer requirements, to determine which engineering metrics or product specifications are the most essential [1]. This prioritization helps designers know what part of the product or process is most beneficial to focus on during design, resulting in products that better meet customer requirements and generate increased commercial success. QFD and most other design methodologies have rarely been applied to MEMS products [2]. In the case of QFD, the structure of the most common format of the tool dictates that engineering metrics should be related to parts characteristics in the second step of applying QFD. This causes difficulties in using the tool for MEMS as most MEMS do not have physical ‘parts’ that are assembled into a final device. Rather, MEMS have product specifications and a manufacturing process used to create the product. Generally there is a tight link between product and process in MEMS. This link has been utilized in creating a modified version of QFD that relates engineering metrics to design concepts, including product conceptualization and manufacturing process. The modified QFD utilizes aspects of Pugh Concept Selection, and differs from typical QFD primarily in consideration of product idea and manufacturing process in the early phases of product definition. The modified QFD was applied to a MEMS project whose goal was to develop a handheld device that allows users to control the selection and release of a variety of stored scents. The technique was also applied to a microscale heat exchanger for integrated circuits. The scent dispenser and heat exchanger were designed and prototyped at Stanford University in 2005 and 2006, respectively. The modified version of QFD gave insight early in the product definition phase on which design concept to pursue to prototype. Use of this and other design methodologies in the MEMS field could shorten the time it takes to progress through product development to volume manufacturing, and increase confidence in the marketability of the chosen design and manufacturing process. A case study demonstrating the effects of using modified QFD Phase II to assist in finding a good fit between technical capabilities and market application was performed by the author on an acoustic sensor technology [3].

In a rapidly growing information-oriented society, people with disabilities and older people are faced with serious inconveniences in accessing IT products due to complicated use of technologies and poorly designed interfaces. To solve the problems, it is required for designers and engineers to find out and understand user needs, and then to figure out the functionalities and design characteristics to meet the needs. Figuring out the Engineering Characteristics (EC) of products from user needs for people with disabilities and older people who have problems using main stream products due to limited accessibility would take great efforts and time. We merged two important concepts of product design for people with disabilities and older people, accessibility and universal design, using an engineering design framework of QFD (Quality Function Deployment) to provide engineers and designers with a systematic methodology for universal product design. We adapted a technical report from the ISO/IEC JTC1 Special Working Group on Accessibility (SWG-A), the Information Technology – Accessibility Considerations for People with Disabilities – Part 1: User Needs Summary (ISO/IEC PDTR 99999-1), and mapped the detailed user accessibility needs to the guidelines of 7 universal design principles which are widely accepted. We explained how a process model was built for mapping relationship between the guidelines and user accessibility needs, and extracting the critical engineering characteristics for IT product design based on these two models. A model House of Quality(HOQ) was built for such procedures.KeywordsUniversal design processUser needsQuality Function Deployment (QFD)design guidelinesISO/IEC PDTR 99999-1

Up to date, workload and worker performance in Small Medium-sized Enterprise (SMEs) was assessed manually. KESAN (Kansei Engineering-based Sensor for Agroindustry) was developed as a tool to assess worker workload and performance. The latest prototype of KESAN was established. As the final step prior to the full-scale mass production, an industrial design was required and must be designed based on the validation to user needs. This research proposed an industrial design for mass production of KESAN using Kano model and Quality Function Deployment (QFD). The user needs was extracted from attributive analysis of Kano model. The matrix of House of Quality (HOQ) was utilized to connect the user needs and technical requirement. The research result validated Thirteen (13) user need attributes. The most important attribute was desktop application as an integrated decision support system. Fourteen (14) technical requirement attributes were identified to fulfil the user needs. Finally, a prototype was developed based on product final specification and prioritized technical requirements.

The greenhouse gas (GHG) emissions of the global ceramic production is estimated at more than 400 Mt CO2/year, which have increased steadily from economic growth. Among ceramic industries, ceramic tableware industry (CTI) is a highly energy-intensive and high GHG emissions industry. Thailand was the fourth highest ranking ceramic tableware exporting country in the world. However, information on GHG emission from this industry was limited. This research aimed to investigate the carbon dioxide(CO2) intensity of CTI in Thailand and the annual projections of GHG emission during 2017–2050 with different GDP growths. Then, the energy saving potentials and GHG mitigation measures with their GHG abatement cost for small and large-scale CTI were proposed. The results indicated that the average CO2 intensity of Thailand CTI was 1.75 kg CO2e/kg of product. The projections for GHG emissions of ceramic tableware production with gross domestic production (GDP) growth rates of 1.5, 3.5 (BAU), and 5.5%, reached their maximum emissions at 220,500 t CO2 in 2029, 2022, and 2020, respectively. Under a BAU scenario, ceramic tableware production in 2022 would emit GHG at a rate approximately 1.37 times greater compared to the emissions in 2016. The maximum GHG reduction (100% implementation) was 48,902 t CO2e, accounting for 22% of GHG emissions in 2030. The average mitigation cost was 6.64 USD/t CO2e reduction. This study provided a guideline for the assessment of CO2 intensity and the technical information for long-term GHG emission projection in CTI which could be applied in worldwide.

QFD (Quality Function Deployment) has been used to reflect the needs of customers in products, while TRIZ (a Russian acronym for Theory of Inventive Problem Solving) was developed to assist engineers in finding innovative solutions to technical problems in product development processes. However, no method has been proposed to integrate QFD with TRIZ effectively. This report describes a new method, named the Innovative Product Development Process (IPDP), which systematically integrates QFD with TRIZ and enables the effective and systematic creation of technical innovation for new products. In IPDP, the target products' functions and mechanisms are deployed in parallel into hierarchical structures, and the mechanism that most requires technical innovation is specified from an analysis of customers' needs by calculating a mechanism weight. Then, the technical problems to be solved are defined by considering the relationship between the specified mechanism and corresponding functions or quality characteristics, and technical innovation is executed by applying TRIZ. The effectiveness of IPDP was confirmed by applying it to the technical innovation of a washing machine.

Emotional design is one of the most important methods in modern product design. It was highlighted after Donald A. Norman published his book Emotional design: why we love (or hate) everyday things. Emotional design addresses people’s needs and hopes which is in the center of the development of product design and product technology. Due to the lack of strong theoretical basis and quantitative analysis, many products can’t reach the goal of their designers with Emotional feelings, nor can consumer’s emotional understandings be similar to the designers’ emotional expectations. This paper analyzes the concept of emotional design and some difficulties which designers must to be faced in their emotional designing works, discusses on product shape design according to emotional design. On this basis, the Kansei engineering and emotional design methods are described in details.

Quality function deployment (QFD) is a systematic approach to transform customer requirements (CRs) into product engineering characteristics (ECs). Traditional QFD relies on market research or customer questionnaires to collect a series of ambiguous and uncertain CRs. As a result, evaluating the weighting of CRs and determining the design matrix between CRs and ECs have become the focus and difficulty of QFD. This paper proposes the grey system theory in artificial intelligence technology combined with QFD to develop grey-QFD to solve the issues mentioned before. First, collect the average evaluation values between the aesthetic images and customer satisfaction of representative products. The grey prediction GM (1, N) model is used to obtain the weight of aesthetic needs relative to customer satisfaction and import it into the left QFD. Second, the domain experts decomposed the product form into a morphological analysis table, and fuzzy Delphi screened key ECs and imported them into the ceiling of QFD. Finally, grey relationship analysis established the aesthetic product design matrix between CRs and ECs, and calculated and ranked the final weights of each ECs by using grey relationship degree. The research uses the security camera in the smart home as an experimental object. After operating the proposed grey-QFD, the aesthetic quality of the target product (lively, intelligent, friendly, personalized, and fashionable) and the optimization of the corresponding product ECs are obtained. The result provides a theoretical reference for designers and significantly improves customer aesthetic satisfaction.

Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient (MFCC) of the speech signal is used as the input of the improved long and short-term memory network (ILSTM). To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed.

Rough handling in the slaughtering operation can cause animal’s stress resulting in the poor quality of meat because of the depleting muscle reserves to the lowest level. This condition will reduce the muscle lactic acid and increase the potential hydrogen on the meat above the normal level allowing a higher possibility for bacterial growth. Moreover, the rough slaughtering operation using the conventional technique may lead to slaughterer’s accident because of the cow’s mutiny. Thus, it is crucial to use the slaughtering tool that is safer and more comfortable to operate. This study aims to design the innovative slaughtering tool ergonomically and satisfying the user requirement. A survey was conducted to identify user criteria of the design. Theory of Inventive Problem Solving (TRIZ) was implemented as a fundamental basis to determine the inventive principle of the design, and the Quality Function Deployment was applied to develop the design specification. Non-parametric statistical analysis was done to test the hypothesis. This study reveals some identified inventive principles including dynamic quality, parameter changes, combination, and equipotentiality. Thus, the proposed design is valid to satisfy user need at 5% of significance level as it is proven to be safer, stronger, more practical, and more durable.

As a useful product development technique, quality function deployment (QFD) can convert customer requirements into suitable engineering characteristics for maximum customer satisfaction. Nonetheless, the traditional QFD has been extensively criticized as having many defects in practical applications, which include the crisp evaluation of relationships between customer requirements and engineering characteristics, and the non-robust ranking of engineering characteristics. In this paper, we put forward a new method using double hierarchy hesitant linguistic term sets (DHHLTSs) and axiomatic design approach to enhance the performance of the classical QFD. Firs, the DHHLTSs are employed to evaluate the relationships between customer requirements and engineering characteristics. Then, the axiomatic design theory is extended to derive the prioritization of engineering characteristics in QFD. Additionally, a maximizing consensus method is used to determine the weights of experts considering inconsistency among their opinions. Finally, an empirical case of a shared car manufacturer is provided with a comparison analysis to demonstrate the feasibility and effectiveness of our proposed QFD approach.

Reuse-oriented redesign method of used products based on axiomatic design theory and QFD

Virtual fitting applications have brought new vitality and immersive experiences to the fashion industry. As users adapt to the new fashion consumption experience enabled by virtual fitting, their demands are becoming increasingly diverse and complex. Therefore, it’s essential to deeply understand real user demands and use design resources effectively to meet these demands. This study assesses virtual fitting products on the market using smart data analysis techniques. Through panel group interviews, we’ve identified and preliminarily assessed user demands. We’ve categorized and prioritized these needs using the Kano model and transformed them into concrete design requirements using the Quality Function Deployment (QFD) method, establishing a system of design requirements. These requirements fall into three categories: visual design, interactive design, and emotional design. Each category includes core, secondary core, and peripheral core requirements. Eight core design requirements, such as personal image customization, extensive accumulation of apparel model libraries, and brand fan engagement were identified as top priorities for implementation. Eight secondary core design requirements, including realistic digital fabric rendering effects, real-time updates of the latest apparel models, and sharing of virtual fitting images should be integrated into design practices as resources allow. Nine peripheral core design requirements, like apparel aligning with mainstream aesthetic preferences, customizable avatar body dimensions, and minimalist user interface design can serve as references for inclusion in design practices. This research provides essential theoretical support for resource allocation in the design and iteration of virtual fitting applications, offering design solutions to meet the multifaceted real user demands in the rapidly changing field of digital fashion.

ABSTRACTThe potential for improved weapon load outs on advanced aircraft was recognized by the Wright Laboratories, Munitions Division as a key enabling technology for dramatic improvements in the system performance and affordability. The dilemma faced by the Laboratory was the plethora of enabling technologies, and design concepts. Their combinations were staggering. Given recent weapon technology investments and varying strongly held professional opinions on the best aircraft integration approach, the way ahead was not obvious. Over 1000 potentially feasible combinations of options were available.The laboratory was interested in demonstrating feasible technologies to reduce risk and increase the technical readiness of compressed weapons technologies.The overall goal was to increase the sortie effectiveness of small, F‐16 class fighters by over an order of magnitude using new smaller more accurate munitions currently in development. Resources were available for further development of a limited number of compressed weapons technologies and aircraft carriage technologies. The way we chose to rapidly and accurately identify the high payoff concepts was the use of Quality Function Deployment (QFD) as a formal systems engineering tool in an Integrated Product Team environment. The use of QFD enabled the IPT to select high payoff technologies based upon “system requirements” and a high degree of professional knowledge and experience. The QFD process enabled us to eliminate biases built in from the different organizational views of the team which was made up of government/industry, weapon/aircraft, primes and suppliers and to identify synergistic concepts worthy of additional design and development.

The innovative design strategy of intelligent infusion set was studied from the perspective of user needs. Firstly, the user experience map is used to analyze the user’s behavior process, emotional process and thinking process during infusion, so as to find opportunities and sort out needs. Then KANO model is used to analyze user needs and classify the quantitative attributes of each need. Then, the Better-Worse index is calculated according to the frequency statistics, and the design key points are summarized according to the results, and the design strategy is output. This method can help to sort out and determine the opportunity points of product innovation design, and provide a widely used framework reference to the product innovation design.

This study identifies vital gas turbine (GT) parameters and quantifies their influence in meeting the DOE Turbine Program overall Integrated Gasification Combined Cycle (IGCC) plant goals of 50% net HHV efficiency, $1000/kW capital cost, and low emissions. The project analytically evaluates GE advanced F class air cooled technology level gas turbine conceptual cycle designs and determines their influence on IGCC plant level performance including impact of Carbon capture. This report summarizes the work accomplished in each of the following six Tasks. Task 1.0--Overall IGCC Plant Level Requirements Identification: Plant level requirements were identified, and compared with DOE's IGCC Goal of achieving 50% Net HHV Efficiency and $1000/KW by the Year 2008, through use of a Six Sigma Quality Functional Deployment (QFD) Tool. This analysis resulted in 7 GT System Level Parameters as the most significant. Task 2.0--Requirements Prioritization/Flow-Down to GT Subsystem Level: GT requirements were identified, analyzed and prioritized relative to achieving plant level goals, and compared with the flow down of power island goals through use of a Six Sigma QFD Tool. This analysis resulted in 11 GT Cycle Design Parameters being selected as the most significant. Task 3.0--IGCC Conceptual System Analysis: A Baseline IGCC Plant configuration was chosen, and an IGCC simulation analysis model was constructed, validated against published performance data and then optimized by including air extraction heat recovery and GE steam turbine model. Baseline IGCC based on GE 207FA+e gas turbine combined cycle has net HHV efficiency of 40.5% and net output nominally of 526 Megawatts at NOx emission level of 15 ppmvd{at}15% corrected O2. 18 advanced F technology GT cycle design options were developed to provide performance targets with increased output and/or efficiency with low NOx emissions. Task 4.0--Gas Turbine Cycle Options vs. Requirements Evaluation: Influence coefficients on 4 key IGCC plant level parameters (IGCC Net Efficiency, IGCC Net Output, GT Output, NOx Emissions) of 11 GT identified cycle parameters were determined. Results indicate that IGCC net efficiency HHV gains up to 2.8 pts (40.5% to 43.3%) and IGCC net output gains up to 35% are possible due to improvements in GT technology alone with single digit NOx emission levels. Task 5.0--Recommendations for GT Technical Improvements: A trade off analysis was conducted utilizing the performance results of 18 gas turbine (GT) conceptual designs, and three most promising GT candidates are recommended. A roadmap for turbine technology development is proposed for future coal based IGCC power plants. Task 6.0--Determine Carbon Capture Impact on IGCC Plant Level Performance: A gas turbine performance model for high Hydrogen fuel gas turbine was created and integrated to an IGCC system performance model, which also included newly created models for moisturized syngas, gas shift and CO2 removal subsystems. This performance model was analyzed for two gas turbine technology based subsystems each with two Carbon removal design options of 85% and 88% respectively. The results show larger IGCC performance penalty for gas turbine designs with higher firing temperature and higher Carbon removal.