Concurrent engineering approach in the development of composite products: A review

Concurrent Engineering (CE) is regarded as a systematic design approach which integrates concurrent design of product with the related processes which is able to accomplish product that can be produced at lower cost, shorter time and with higher quality and this achievement was termed as cost, time and quality (CTQ) improvement. Since its establishment, CE philosophy was well implemented in product development with traditional materials such as metals but up to date, the work on CE in composite product development is still limited. Hence, a review on the implementation of Concurrent Engineering (CE) approach in the development of composite products is presented in this paper which includes review of various studies of CE techniques in composite product development. In addition, the relationship between CE and Pugh total design method is discussed in the context of composite design. Moreover, publications related to materials selection, life cycle analysis and sustainability issues of composite materials are also reviewed whereby a section is devoted to highlight previous work on materials selection using Analytical Hierarchy Process method. It was observed that materials selection of composite materials is a very important activity as far as CE in composite product development. The use of various techniques and computer aided materials selection tools such as Analytical Hierarchy Process has helped designers to select the most optimum composite materials for engineering components. Furthermore, based on current trends in composites product development, the role of CE is expected to be more crucial to assist composites designers in achieving the design requirements from various stakeholders effectively and efficiently considering the expanding range of composite materials availability as well as realizing new potential for biocomposites applications through introduction of innovative alternative problem solving methods as part of the CE family.     

An aggregation technique for optimal decision-making in materials selection

Materials selection is an onerous but very important activity in the design process. An inappropriate choice of material(s) can adversely affect the productivity and profitability and hence reputation of a manufacturing organization. The complexity of materials selection makes multi-criteria analysis an invaluable tool in the engineering design process. However, the application of various multi-criteria decision making (MCDM) methods can yield different results, especially when alternatives lead to similar performance. Therefore, an aggregation technique is proposed in this paper for optimal decision-making. In this approach, ranking orders obtained by various MCDM methods are used as the input of the suggested procedure and the outputs are aggregation rankings, which help designers and engineers to reach a consensus on materials selection for a specific application. An illustrative example is given to demonstrate the application of this procedure and its effectiveness in obtaining optimal materials selection.     

A decision making methodology for material selection using an improved compromise ranking method

An ever increasing variety of materials is available today, with each having its own characteristics, applications, advantages, and limitations. In choosing the right material, there is not always a single definite attribute of selection and the designers and engineers have to take into account a large number of material selection attributes. This paper presents a logical procedure for material selection for a given engineering application. The procedure is based on an improved compromise ranking method considering the material selection attributes and their relative importance for the application considered. Two examples are included to illustrate the approach.     

Materials selection using complex proportional assessment and evaluation of mixed data methods

Material selection is a very fast growing multi-criteria decision-making (MCDM) problem involving a large number of factors influencing the selection process. Proper choice of material is a critical issue for the success and competitiveness of the manufacturing organizations in the global market. Selection of the most appropriate material for a particular engineering application is a time consuming and expensive process where several candidate materials available in the market are taken into consideration as the tentative alternatives. Although a large number of mathematical approaches is now available to evaluate, select and rank the alternative materials for a given engineering application, this paper explores the applicability and capability of two almost new MCDM methods, i.e. complex proportional assessment (COPRAS) and evaluation of mixed data (EVAMIX) methods for materials selection. These two methods are used to rank the alternative materials, for which several requirements are considered simultaneously. Two illustrative examples are cited which prove that these two MCDM methods can be effectively applied to solve the real time material selection problems. In each example, a list of all the possible choices from the best to the worst suitable materials is obtained which almost match with the rankings as derived by the past researchers.     

The role of impressions on users’ tactile interaction with product materials: An analysis of associative concept networks

Depth impressions are an inner associative layer of humans’ expressed impressions. To analyze tactile interaction, it is essential to examine what users feel and imagine and how they create depth impressions by touching and looking at different product materials. On the basis of tactile interactions, this study aims to capture and analyze users’ depth impressions of materials. This research also proposes an ‘impressionably’ new tactile material for design from the viewpoint of depth impressions. To capture depth impressions, we investigated users’ tactile interactions in an experiment. The experiment used samples of six common natural and artificial materials, along with the proposed new micro-print-based material. A concept network-based method was employed in two stages to analyze the experimentally obtained verbalized protocols and to identify any depth impressions. This method allowed us to capture and analyze the depth impressions behind the surface impressions. This research found that the feel of materials’ tactile naturalness and users’ habituation to the tested samples are related to their depth impressions and the complexity of their concept networks. The depth impressions and concept network of the proposed micro-print material are distinct and beyond those for existing natural or artificial materials. These findings will provide the basis for employing new analysis tools and facilitate the development of impressionably better tactile materials for design.     

A novel hybrid multiple criteria decision making model for material selection with target-based criteria

In engineering design, the decision to select an optimal material for a particular product is a problem requiring multi-criteria decision analysis that involves both qualitative and quantitative factors. The evaluation of alternative materials may be based on imprecise information or uncertain data. Furthermore, there can be significant dependence and feedbacks between the different criteria for material selection. However, most existing decision approaches cannot capture these complex interrelationships. In response, this paper proposes a general framework for evaluating and selecting the best material for a given application. A novel hybrid multiple criteria decision making (MCDM) model combining DEMATEL-based ANP (DANP) and modified VIKOR is used to solve the material selection problems of multiple dimensions and criteria that are interdependent. Moreover, target-based criteria as well as cost and benefit criteria can be addressed simultaneously in the proposed model. Finally, an empirical case concerning the bush material selection for a split journal bearing is presented to illustrate the potential of the new model. The results show that the proposed method for material selection is effective and provides meaningful implications for designers and engineers to refer.     

A quality function deployment-based model for materials selection

Materials play a key role during the entire product design and manufacturing phase as a wrongly selected material may often lead to premature product failure causing loss of revenue and repute of the concerned manufacturing organization. While selecting the most suitable material for a specific application, the designers often need a sound and systematic methodology to deal with this problem having multiple candidate alternative choices and conflicting objectives. Most of the previously applied methodologies for material selection have either adopted criteria weights estimated using subjective judgments of the designers or failed to give due emphasis on the voice of the customers to meet their requirements. In this paper, a maiden venture is taken to solve the material selection problems using a quality function deployment (QFD)-based approach that can integrate the voice of the customers for a product with its technical requirements. The applicability and solution accuracy of this QFD-based material selection model is demonstrated with the help of four illustrative examples. To ease out the materials selection decision-making process, a user-friendly software prototype in Visual BASIC 6.0 is also developed.     

Selection of materials using compromise ranking and outranking methods

Selection of proper materials for different components is one of the most challenging tasks in the design and development of products for diverse engineering applications. Materials play a crucial and important role during the entire design and manufacturing process. Wrong selection of material often leads to huge cost involvement and ultimately drives towards premature component or product failure. So the designers need to identify and select proper materials with specific functionalities in order to obtain the desired output with minimum cost involvement and specific applicability. This paper attempts to solve the materials selection problem using two most potential multi-criteria decision-making (MCDM) approaches and compares their relative performance for a given material selection application. The first MCDM approach is ‘Vlse Kriterijumska Optimizacija Kompromisno Resenje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. These two methods are used to rank the alternative materials, for which several requirements are considered simultaneously. Two examples are cited in order to demonstrate and validate the effectiveness and flexibility of these two MCDM approaches. In each example, a list of all the possible choices from the best to the worst suitable materials is obtained taking into account different material selection criteria. The rankings of the selected materials almost corroborate with those as obtained by the past researchers.     

Deep-Learning-Enabled Fast Optical Identification and Characterization of 2D Materials

Advanced microscopy and/or spectroscopy tools play indispensable roles in nanoscience and nanotechnology research, as they provide rich information about material processes and properties. However, the interpretation of imaging data heavily relies on the “intuition” of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, the optical characterization of 2D materials is used as a case study, and a neural-network-based algorithm is demonstrated for the material and thickness identification of 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, flake sizes, and their distributions, based on which an ensemble approach is developed to predict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other optical identification applications. This artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials, and potentially accelerate new material discoveries.     

Fuzzy decision support for tools selection in the core front end activities of new product development

The innovation process may be divided into three main parts: the front end (FE), the new product development (NPD) process, and the commercialization. Every NPD process has a FE in which products and projects are defined. However, companies tend to begin the stages of FE without a clear definition or analysis of the process to go from Opportunity Identification to Concept Generation; as a result, the FE process is often aborted or forced to be restarted. Koen’s Model for the FE is composed of five phases. In each of the phases, several tools can be used by designers/managers in order to improve, structure, and organize their work. However, these tools tend to be selected and used in a heuristic manner. Additionally, some tools are more effective during certain phases of the FE than others. Using tools in the FE has a cost to the company, in terms of time, space needed, people involved, etc. Hence, an economic evaluation of the cost of tool usage is critical, and there is furthermore a need to characterize them in terms of their influence on the FE. This paper focuses on decision support for managers/designers in their process of assessing the cost of choosing/using tools in the core front end (CFE) activities identified by Koen, namely Opportunity Identification and Opportunity Analysis. This is achieved by first analyzing the influencing factors (firm context, industry context, macro-environment) along with data collection from managers followed by the automatic construction of fuzzy decision support models (FDSM) of the discovered relationships. The decision support focuses upon the estimated investment needed for the use of tools during the CFE. The generation of FDSMs is carried out automatically using a specialized genetic algorithm, applied to learning data obtained from five experienced managers, working for five different companies. The automatically constructed FDSMs accurately reproduced the managers’ estimations using the learning data sets and were very robust when validated with hidden data sets. The developed models can be easily used for quick financial assessments of tools by the person responsible for the early stage of product development within a design team. The type of assessment proposed in this paper would better suit product development teams in companies that are cost-focused and where the trade-offs between what (material), who (staff), and how long (time) to involve in CFE activities can vary a lot and hence largely influence their financial performances later on in the NPD process.     

Incorporation of high recyclability material selection in computer aided design

Manufacturing are now under increasing pressure of tighten environmental legislation to preserve natural resources. Product design, as a part of manufacturing activities, has to be more concern to deliver product specification with reduced environmental impact. Design for Recycling (DFR) is one of the promising approaches in prolonging material utilization of a product in the early stage of design. DFR focus on harmonizing product design with the recycling practices in order to minimize the loss of valuable materials and preventing unnecessary waste streams at the end of product’s life. In turn, it limits the usage of natural resources.To implement DFR, designers are faced with the challenge of compromising different design objectives such as cost, functional or technical requirements and product’s recyclability performance. This paper attempts to propose an intelligent approach that could facilitate designers to make an easy and quick recyclability assessment as well as selecting recyclable materials integrated with computer aided design. In this paper, the use of fuzzy inference system and genetic algorithm is proposed to optimize the multi-objective problem in the selection of recyclable materials. Case study on the actual conceptual design using computer aided design environment is demonstrated and showing that the proposed method successfully can be applied concurrently during product design. Comparison of proposed method with Sustainability Express Solid Work is also presented. The proposed method can assist product designers to design a high recyclability product without ignoring technical perspectives.     

论产品艺术没计创新的选材之道

分析了产品设计中材料选择的重要性和复杂性,并进一步探讨了产品设计的选材理论依据,在此基础上,研究在新材料和新工艺层出不穷的情况下,以系统的理论为指导,利用几个优秀产品设计的例子说明产品设计选材应当遵循的基本原则,归纳总结出产品艺术设计创新的"精、美、巧、奇、宜"选材之道。     

儿童户外设施产品中材料非物质性的设计表达

目的 通过分析儿童户外设施产品材料的类别、使用者和使用条件,归纳产品中材料非物质性在产品设计中的表达维度,从非物质的角度提供新的产品选材角度。方法 运用文献研究法梳理儿童产品中材料的研究现状,基于调研得到的实际产品案例,统计其中产品材料的应用情况,结合图表分析和案例分析的方式,分别从儿童的生理、心理和认知等维度论述材料非物质性在产品形象层面的表达,从产品的使用场所、设计主题和体验维度论述材料非物质性在产品意义层面的表达,从材料“型性”与“构性”的维度论述材料非物质性在产品型构层面的表达,构建材料与产品间的非物质关联性。结论 材料的非物质性可以成为儿童户外设施产品新的选材因素。它在形象表达上围绕使用者自身,体现的是产品“物用”层面的表达;在意义表达上与产品主题、意境、体验等多种因素相关,体现的是“物境”层面的表达;在型构表达上是材料物质性的扩展与延伸,体现的是“物构”层面的表达。     

A subjective and objective integrated multiple attribute decision making method for material selection

The selection of an optimal material for an engineering design from among two or more alternative materials on the basis of two or more attributes is a multiple attribute decision making (MADM) problem. The selection decisions are complex, as material selection is more challenging today. There is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations and in making right decisions. This paper proposes a novel MADM method for material selection for a considered design problem. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Three examples are presented to illustrate the potential of the proposed method.     

Study of the compatibility between criteria in a set of materials requirements: Application to a machine tool frame

This contribution to materials selection theory aims at developing methodological methods and tools to analyse a complex set of material requirements with the objective of forecast whether there are materials that can fulfil it, or alternatively, if multi-materials selection is more likely to provide a solution. This “pre-analysis” of requirements examines the two main reasons which may prevent a single material solution: nonuniformity of materials space filling, or intrinsic contradiction between properties.A variety of statistical tools is used, based on Multivariate Analysis Methods as Principal Component Analysis (PCA) and the estimation of density distribution in the materials space. These tools allow to evaluate the “statistical compatibility” between the requirements and the available materials, and provides an estimate of the likelihood to find a single material solution or not. The methodology is applied to the research of a material for a machine tool frame.     

Incorporating production concerns in conceptual product design

This paper presents a set of analytical tools that can be used to alter a product's design, manufacturing processes and assembly techniques to increase production rate. The analytical tools obtain these improvements by simultaneously considering each part's geometric attributes and complexity, vendor selection, material and process selection, and capacity planning at the conceptual stage of the product realization process. The method detects and then avoids heavily used resources by indicating which combination of one or more of its components' geometric attributes, manufacturing processes, material and assembly methods should be altered. The method is illustrated with the analysis of an overhead projector. It is shown that production rate can be doubled by either making small changes to a component's geometric attributes or by selecting different manufacturing processes. Neither of these changes affects the functionality of the product.     

Proposal for a multi-material design procedure

This paper describes a proposal for a multi-material design procedure. First, the context of the study and the requirements of the multi-material must be clearly defined in order to specify the parameters that the designer must select or optimise in order to produce the design: the components and their volume fraction, the architecture and morphology at different scales, etc. The general design procedure proposed here starts with the reasons why the designer has turned to multi-materials, from which a multi-material concept with fixed parameters can be defined. In this first stage the design problem can be made less complex by reducing the number of unknown parameters and guiding the designer towards the appropriate selection or optimisation tools: (i) subdivision of requirements, guided by applying statistical analysis tools to the materials database to search for appropriate multi-material components, (ii) tools to filter the materials database and search for multi-material components and their volume fraction, (iii) optimisation tools to search for the appropriate architecture when components are known or to search for architecture and components simultaneously. The paper demonstrates how these tools can be applied to different design concepts.     

Expert System for Product Manufacturability and Cost Evaluation

Product design is an important stage in the product development cycle. A large proportion of a product's cost is governed by product design. Every effort should be made to ensure a product is “rightly designed for manufacture” at the design stage in order to avoid unnecessary expenditure of time and money in production resulting from a need for redesign. An evaluation tool in the form of an expert system has been developed for product designers to assess manufacturability of product designs. This paper describes the mode of operation in the development of the expert system from which manufacturing information such as selection of processes and materials, and estimation of cost can be obtained. The system enables product designers to evaluate product design alternatives by comparing the analyzed results to come up with the optimum design that best satisfies the requirements. The developed system runs on the PC platform.