A method of constructing an inspiration library driven by user-perceived preference evaluation data for biologically inspired design

Biologically inspired design (BID) is one of the common methods for product design. To solve the problem of inaccurate acquisition of inspirational creatures due to the lack of user perception preference analysis, a data-driven intelligent service model for BID considering user perception needs is proposed based on Kansei engineering. Firstly, by extracting the perceptual features of creatures from the semantic source elements of products through mapping and encodes them, we proposed a data acquisition method based on intuitionistic fuzzy sets considering different customer preference distributions, bridging the gap caused by the asymmetry between designers and users. Secondly, the functional relationship between biometric features and user-perceived attributes is identified and predicted, and a predictive model of biodata considering user preferences is obtained by multiple linear regression analysis. Finally, based on the data clustering and reorganization theory to understand the organization and dynamics of the database, the construction of a BID library was completed, and the design resources in the library were used as analyzed knowledge for designers to plan design activities. Taking the bionic design of a UAV product as an example, a prototype of a computer-aided design service system was developed based on the theory proposed in the article, and the analyzed knowledge was used to improve the efficiency and science of the design, effectively verifying the usefulness of this study for design. To a certain extent, this study addresses the problem of cognitive limitations of designers and cognitive differences between designers and users, promotes the application of bioinspiration in product design, and improves the marketability of design solutions.     

Research and development of liquid hydrogen (LH2) temperature monitoring system for marine applications

Monitoring the temperature in liquid hydrogen (LH₂) storage tanks on ships is important for the safety of maritime navigation. In addition, accurate temperature measurement is also required for commercial transactions. Temperature and pressure define the density of liquid hydrogen, which is directly linked to trading interests. In this study, we developed and tested a liquid hydrogen temperature monitoring system that uses platinum resistance sensors with a nominal electrical resistance of approximately 1000 Ω at room temperature, PT-1000, for marine applications. The temperature measurements were carried out using a newly developed temperature monitoring system under different pressure conditions. The measured values are compared with a calibrated reference PT-1000 resistance thermometer. We confirm a measurement accuracy of ±50 mK in a pressure range of 0.1 MPa–0.5 MPa.     

Intelligent Kano classification of product features based on customer reviews

Product features can be classified into different categories based on customer opinions. The rapid development of artificial intelligence and machine learning paves the way toward computational analysis of customer reviews for opinion mining. This paper presents an Intelligent Kano framework to extract, quantify, and classify different product features based on customer reviews. The framework is enabled by a novel integration of multiple artificial intelligence and machine learning techniques such as sentiment analysis and anomaly detection. A case study is conducted to validate the framework’s effectiveness. Over 12,000 customer reviews on two coffee machines are analyzed for the classification.     

Prevention of substrate and product inhibitions by using a dilution strategy during dark fermentative hydrogen production from molasses

Substrate and product inhibitions have a significant effect on dark fermentative hydrogen gas production. Particularly, rapid formation of volatile fatty acids leads to fast pH decreases shifting the metabolic pathway. Therefore, controlling volatile fatty acid accumulation has great importance in maintaining effective hydrogen production. In this context, a dilution strategy was applied to regulate volatile fatty acids levels within the desired concentration range. A three-factor Box-Behnken statistical experiment design was established to assess the effects of dilution time, dilution percentage and initial COD concentration on hydrogen formation yield and rate. Highest hydrogen yield (7.7 mL H₂/mL_reactor) and rate (21. 47 mL H₂/h or 9.38 mmol/L_reactor.h) were achieved when 85 gCOD/L containing fermentation media was diluted with a percentage of 130 of the initial working volume at the 3rd hour of the fermentation period. Moreover, this strategy enabled to start fermentation with 55 g glucose/L.     

Integrated rough VIKOR for customer-involved design concept evaluation combining with customers’ preferences and designers’ perceptions

Customer-involved design concept evaluation (DCE) allows customers to take part in evaluating the design alternatives to get more popular design concept. Traditional customer-involved DCE methods still focus on the collection of customer responses and only consider cost and benefit characteristics of design criteria in multi-criteria decision-making (MCDM) based evaluation process. Few studies have customized the decision-making algorithms specifically aimed at customers’ preferences. This paper further explores the customers’ influences in the early stages of the product design development, and proposes a new rough number based MCDM model (i.e., VIKOR) incorporating customers’ preferences for design specifications along with designers’ perceptions for the characteristics of design criteria (cost and benefit) to perform concept evaluation under subjective environment, and this proposed method is named as integrated rough VIKOR (IR-VIKOR). The objective of this study is to identify the best design concept which maximizes the satisfactions of expectations from most customers as well as conforms to the characteristics of design criteria. Firstly, Shannon entropy is used to obtain the weightings and relative importance ratings of design criteria from the customers’ preferences. Secondly, the customers’ preferences for design attribute values, the importance ratings of design criteria and the characteristics of design criteria are combined together to define the ideal solutions to calculate the rough evaluation index of each design alternative in IR-VIKOR, and finally the ranking result is provided by IR-VIKOR to determine the best design concept. A practical design example is introduced to illustrate the evaluation process of this proposed method, and the empirical comparisons are further carried out to validate its superiority for DCE. Through the sensitivity analysis experiments including i) inside IR-VIKOR, and ii) between IR-VIKOR and other classical MCDM methods, the proposed method is proved to be a reliable and feasible customer-involved DCE approach.     

Product mix determination and capacity allocation for heterogeneous products in thin film transistor liquid crystal display manufacturing

In the thin-film transistor liquid crystal display (TFT-LCD) manufacturing, the development of models to determine product mix and capacity allocation for a multi-stage, multi-site, and multi-generation company is very important. As rapid advancement of the electronic-paper display (EPD) technology, it is a trend of the coexistence of heterogeneous products involving LCD and EPD products. In the case we investigated, the TFT-LCD company merged an EPD company and incorporated their electro-phoretic technologies into their company. Notably, the manufacturing processes of the EPD products do not incorporate the process steps of liquid crystal injection and attachment of color filters. Consequently, to minimize the total cost, it is essential to decide the appropriate product mix and capacity allocation with considerations of the resource consumption of LCD and EPD products simultaneously. In this paper, we present mathematical models to determine product mix and capacity allocation, which involve three subsystems for TFT-LCD process with consideration of net demand, inventory level, yield rates, cost, margin, outsourcing allocation, cycle time, and panel conversion rate. To demonstrate the applicability of the proposed models, we present a real-world case taken from a TFT-LCD company located in the Science-Based Industrial Park at Hsinchu, Taiwan and perform sensitivity analysis to investigate the effect on the optimal solution.     

A systematic methodology for optimal mixture design in an integrated biorefinery

Biomass is a sustainable source of energy which can be utilised to produce value-added products such as biochemical products and biomaterials. In order to produce a sustainable supply of such value-added products, an integrated biorefinery is required. An integrated biorefinery is a processing facility that integrates multiple biomass conversion pathways to produce value-added products. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the large number of available pathways, various systematic screening tools have been developed to address the process design aspect of an integrated biorefinery. Process design however, is often inter-linked with product design as it is important to identify the optimal molecule (based on desired product properties) prior to designing its optimal production routes. In cases where the desired product properties cannot be met by a single component chemical product, a mixture of chemicals would be required. In this respect, product and process design decisions would be a challenging task for an integrated biorefinery. In this work, a novel two-stage optimisation approach is developed to identify the optimal conversion pathways in an integrated biorefinery to convert biomass into the optimal mixtures in terms of target product properties. In the first stage, the optimal mixture is designed via computer-aided molecular design (CAMD) technique. CAMD technique is a reverse engineering approach which predicts the molecules with optimal properties using property prediction models. Different classes of property models such as group contribution (GC) models and quantitative structure property relationship (QSPR) are adapted in this work. The main component of the mixture is first determined from the target product properties. This is followed by the identifying of additive components to form an optimal mixture with the main component based on the desired product properties. Once the optimal mixture is determined, the second stage identifies the optimal conversion pathways via superstructural mathematical optimisation approach. With such approach, the optimal conversion pathways can be determined based on different optimisation objectives (e.g. highest product yield, lowest environmental impact etc.). To illustrate the proposed methodology, a case study on the design of fuel additives as a mixture of different molecules from palm-based biomass is presented. With the developed methodology, optimal fuel additives are designed based on optimal target properties. Once the optimal fuel additives are designed, the optimal conversion pathways in terms of highest product yield and economic performance that convert biomass into the optimal fuel additives are identified.     

Nail clipper ergonomic evaluation and redesign for the elderly

This study designs and evaluates a new nail clipper for the elderly using ergonomic methods. The nail clipper usage problems and requirements were collected first. After requirement analysis we applied ergonomic simulation in redesigning the nail clipper and developing a pedal plate. A usability test was conducted to evaluate both typical and newly developed nail clippers using 20 elder participants. The dependent measures were the total number of nails clipped, completion time and discomfort and satisfaction subjective ratings. The experimental results indicated that the newly developed nail clipper was superior to the typical one in subjective ratings for discomfort and satisfaction. The performance of the newly-developed nail clipper is similar to that of the typical clipper. Some important nail clipper design implications for the ageing population are discussed.Relevance to industryWith the increase in the elderly population, problems caused by aging have gradually emerged. Aging may cause the elderly inconvenience in using many household objects that are not specifically designed for the elderly. Therefore, industrial designers should try to redesign household objects to enhance living quality for the elderly. This study used nail clipper design as an example to show how to apply ergonomic methods in product design.     

Experimental study of the pyrolysis of waste bitumen for oil production

This work focuses on bitumen slow pyrolysis. Mass and energy yields of oil, solid and gas were obtained from pyrolysis experiments using a semi-batch reactor in a nitrogen atmosphere, under three non-isothermal conditions (maximum temperature: 450 °C, 500 °C and 550 °C). The effect of temperature on the product yields was discussed. The gas compositions were analysed using gas chromatography (GC) and the heating value of oil and solid residue was also measured. Using a thermo-gravimetric analyser, kinetic parameters were evaluated through Ozawa-Flynn-Wall (OFW) method. Results showed that oil yield is maximum at 500 °C (50%). Moreover, gas yield increased with increasing pyrolysis temperature from 18% to 36%. On the other hand, solid yield showed an opposite trend: it decreased from 39% to 32%. As regard energy yields, they showed a similar trend with the mass ones. H_2, CH₄, C₂H₄, C₂H₆ and C₃H₈ are the main components of the produced gas phase. It has been noticed that the recovery of bitumen to liquid oil through pyrolysis process had a great potential since the oil produced had high calorific value comparable with commercial fuels.     

Models for circular data from time series spectra

Circular data are those for which the natural support is the unit circle and its toroidal extensions. Numerous constructions have been proposed which can be used to generate models for such data. We propose a new, very general, one based on the normalization of the spectra of complex-valued stationary processes. As illustrations of the new construction's application, we study models for univariate circular data obtained from the spectra of autoregressive moving average models and relate them to existing models in the literature. We also propose and investigate multivariate circular models obtained from the high-order spectra of stationary stochastic processes generated using linear filtering with an autoregressive moving average response function. A new family of distributions for a Markov process on the circle is also introduced. Results for asymptotically optimal inference for dependent observations on the circle are presented which provide a new paradigm for inference with circular models. The application of one of the new families of spectra-generated models is illustrated in an analysis of wind direction data.