Maintainability and safety indicators at design stage for mechanical products

With the industrial products competition, reliability, maintainability and safety are key characteristics for availability improvement. This is mainly true in industries like automotive, aeronautic, or NC machines tools. Therefore, it is crucial to predict these characteristics as soon as possible before the manufacturing starts. This paper presents an approach to provide indicators for maintainability and safety prediction at early stage of design. The assessment procedure uses the product CAD 3D model and an associated semantic matrix gathering information on the product components criticality and reliability. Using this information we calculate indicators for the product maintainability and safety. An academic application is developed to illustrate our approach and point out the interaction between maintainability and safety constraints to determine suitable solution. In conclusion, we state on possible extensions of this approach for evaluating other product lifecycle characteristics and we give guidelines on the implementation aspects.     

Model-driven assessment of system dependability

Designers of complex real-time systems need to address dependability requirements early on in the development process. This paper presents a model-based approach that allows developers to analyse the dependability of use cases and to discover more reliable and safe ways of designing the interactions of the system with the environment. The hardware design and the dependability of the hardware to be used also needs to be considered. We use a probabilistic extension of statecharts to formally model the interaction requirements defined in the use cases. The model is then evaluated analytically based on the success and failure probabilities of events. The analysis may lead to further refinement of the use cases by introducing detection and recovery measures to ensure dependable system interaction. A visual modelling environment for our extended statecharts formalism supporting automatic probability analysis has been implemented in AToM³, A Tool for Multi-formalism and Meta-Modelling. Our approach is illustrated with an elevator control system case study.

Proactive and visual approach for product maintainability design

Maintainability design, as a vital element in product design, is generally conducted after the physical or virtual prototype is done, and this design way always accompanies with the characters of lag, passiveness, subjectivity and lack of relevance. And even design flaws are exposed based on corresponding maintainability analysis, the improvements are hardly finished owing to various practical difficulties. This paper proposed a novel proactive maintainability design method. Based on the maintainability and functionability/structurability (F/S) factors of the product first, a “many to many” mapping relationship between maintainability and F/S spaces is established, in which quantitative assessment and visualization representation are two aspects. Separately, the quantitative assessment provides an objective and precise result of relationship confirmation, and the visualization representation brings a comprehensive and intuitive way for maintainability and F/S designers. Finally, the case study section shows the availability and effectiveness of the methodology by verification and comparison. The proposed methodology considers maintainability affection on F/S just from the start point of product design. Hence, compared with the physical prototype and virtual prototype approach, the implementation of the proposed methodology can make maintainability design and functional/structural design to be conducted almost simultaneously.     

An approach to competitive product line design using conjoint data

In today’s highly competitive environment, where market oriented firms aim to maximize profits through customer satisfaction, there is an increasing need to design a product line, rather than a single product. The main goal of designing a profit maximizing product line is to target the ‘right product’ to the ‘right customer’. Although conjoint analysis has turned out to be one of the most widely used techniques for product line design, it falls to explicitly consider retaliatory reactions from competitors. In this paper, we propose a new conjoint-based approach to competitive new product line design, employing the Nash equilibrium concept. The optimal product line design problem for each firm is formulated as a nonlinear integer programming problem. In the absence of a closed-form solution, to compute the Nash equilibrium and to determine the optimal product line, we propose a two-phase procedure: a sequential iterative procedure in the first phase, and backward induction in the second. To solve the optimization problem in each of the iterations of the sequential procedure, we used the branch-and-bound method. The proposed approach is illustrated under several scenarios of competition using previously published conjoint data.     

Data-driven engineering design: A systematic review using scientometric approach

In the last two decades, data regarding engineering design and product development has increased rapidly. Big data exploration and mining offer numerous opportunities for engineering design; however, owing to the multitude of data sources and formats coupled with the high complexity of the design process, these techniques are yet to be utilised to the best of their full potential. In this study, a comprehensive assessment of the state-of-the-art data-driven engineering design (DDED) in the last 20 years was conducted. A scientometric approach was employed wherein first, a systematic article acquisition procedure was performed, where a dataset of 3339 articles related to engineering design and big data analytics applications were extracted from Web of Science (WoS) and Scopus. Thereafter, this dataset was reduced to a dataset of 366 articles based on concise data screening. The resulting articles were used to analyse the dynamics of research in DDED throughout the last 20 years and to detect the primary research topics related to DDED, the most influential authors, and the papers with the highest impact in the DDED domain. Furthermore, the co-occurrence network of keywords/keyphrases and co-authorship networks were constructed and analysed to reveal the interconnection of the research topics and the collaboration between the most prolific authors. Finally, an insight how big data analytics is being applied through product development activities to support decision-making in engineering design was presented.     

Research on quality performance conceptual design based on SPEA2+

In order to solve the multi-objective performance optimal problems, SPEA2+ is used to realize the performance design of injection molding machine. The optimization objectives are constructed to maximize mould control power, maximize injection quantity and minimize injection power. The mathematical model is found to optimize the problem. A solution is extracted to eliminate the imprecise nature of preference through the Pareto optimal set based on fuzzy set theory. Compared with NSGA-II and SPEA2, SPEA2+ could acquire the Pareto front with better distribution and smaller distance with the optimum solutions. Finally, the case illustration of HTG1000X3Y injection molding machine is taken as an example to demonstrate that such method is effective and practical. Effective references could be provided to decision makers for objectives tradeoff at the performance conceptual design stage of injection molding machine.     

Sequential optimization and reliability assessment for multidisciplinary systems design

With higher reliability and safety requirements, reliability-based design has been increasingly applied in multidisciplinary design optimization (MDO). A direct integration of reliability-based design and MDO may present tremendous implementation and numerical difficulties. In this work, a methodology of sequential optimization and reliability assessment for MDO is proposed to improve the efficiency of reliability-based MDO. The central idea is to decouple the reliability analysis from MDO with sequential cycles of reliability analysis and deterministic MDO. The reliability analysis is based on the first-order reliability method (FORM). In the proposed method, the reliability analysis and the deterministic MDO use two MDO strategies, the multidisciplinary feasible approach and the individual disciplinary feasible approach. The effectiveness of the proposed method is illustrated with two example problems.     

Anticipating the use of future things: Towards a framework for prospective use analysis in innovation design projects

Anticipation of future product use is a persistent issue in User-Centered Design. In this paper, we argue that one obstacle to early integration of use analysis in innovation design is overreliance on retrospective use analysis, i.e. that which is based on clear references to existing products or activities. In contrast, innovation design projects are full of uncertainty, leading to a need for prospective analysis. After having described some limitations of prospective use analysis, we contend that creativity tools may be used to assist the anticipation of future product use, by allowing designers to approach the variability of situations of future use in a structured manner rather than by “muddling through”. We illustrate the expected benefits of this approach with two case studies, and describe some prospects for future research and practice in ergonomics.     

A new digital human environment and assessment of vehicle interior design

Vehicle interior design directly relates to driver performance measures such as comfort, efficiency, risk of injury, and vehicle safety. A digital human is a convenient tool for satisfying the need to reduce the design cycle in order to save time and money. This paper presents a digital human environment, Santos™, developed at The University of Iowa, and its assessment as applied to the interior design of a Caterpillar vehicle. The digital human environment involves male models and accommodates a large percentage of the operator population (from the 5th percentile to the 95th percentile). It has a user-friendly interface and includes various tools such as posture prediction, reachability check, zone differentiation, and biomechanics assessment for the upper body and hand. The key difference from a traditional digital human environment is that Santos’s environment is optimization-based. This can answer design questions regarding whether the operator can reach relevant controls, what the comfort level is if one can reach the control, and what strength is required of the operator to pull a shift, etc. The illustrative example of a Caterpillar cab is demonstrated using this digital human environment.     

Integrating engineering design and analysis using a multi-representation approach

With the present gap between CAD and CAE, designers are often hindere in their efforts to explore design alternatives and ensure product robustness. This paper describes the multi-representation architecture (MRA)—a design-analysis integration strategy that views CAD-CAE integration as an information-intensive mapping between design models and analysis models. The MRA divides this mapping into subproblems using four information representations: solution method models (SMMs), analysis building blocks (ABBs), product models (PMs), and product model-based analysis models (PBAMs). A key distinction is the explicit representation of design-analysis associativity as PM-ABB idealization linkages that are contained in PBAMs.The MRA achieves flexibility by supporting different solution tools and design tools, and by accommodating analysis models of diverse discipline, complexity and solution method. Object and constraint graph techniques provide modularity and rich semantics.Priority has been given to the class of problems termedroutine analysis—the regular use of established analysis models in product design. Representative solder joint fatigue case studies demonstrate that the MRA enables highly automated routine analysis for mixed formula-based and finite element-based models. Accordingly, one can employ the MRA and associated methodology to create specialized CAE tools that utilize both design information and general purpose solution tools.Nomenclature MRA multi-representation architecture - SMM solution method model - ABB analysis building block - PM product model - PBAM product model-based analysis model - ABB-SMM transformation - idealization relation between design and analysis attributes - PM-ABB associativity linkage indicating usage of one or more _i eislab. eislab. gatech. edu.     

Data mining in design of products and production systems

Data mining is acquiring its own identity by refining concepts from other disciplines, developing generic algorithms, and entering new application areas. Engineering design and manufacturing have been affected by the data mining pursuit. This paper outlines areas of product and manufacturing system design that are particularly suitable for data-mining applications. One of the emerging areas is innovation. The key challenges of data mining in the domains discussed in the paper are outlined.     

Selecting representative affective dimensions using Procrustes analysis: an application to mobile phone design

Collecting affective responses (ARs) from consumers is crucial to designers aspiring to produce an appealing product. Adjectives are frequently used by researchers as an affective means by which consumers can describe their subjective feelings regarding a specific product design. This study proposes a Kansei engineering (KE) approach for selecting representative affective dimensions using factor analysis (FA) and Procrustes analysis (PA). A semantic differential (SD) experiment is used to examine consumers' ARs toward a set of representative product samples. FA is employed to extract the underlying latent factors using an initial set of affective dimensions. A backward elimination process based on PA is used to determine the relative significance of adjectives in each step according to the calculated residual sum of squared differences (RSSDs) to finally obtain the ranking of the initial set of adjectives. Additionally, the results of the proposed approach are compared to the method that combines FA and two-stage cluster analysis (CA). A case study of mobile phone design is provided to demonstrate the analysis results.     

Incorporating customer personalization preferences in open product architecture design

Open product architecture is a key enabler for product personalization, as it allows the integration of personalized modules in a product architecture to satisfy individual customer needs and preference. A critical challenge for integrating personalized modules into a product architecture is determining the optimal assembly architecture when considering market expectations and manufacturing constraints. In this paper, an optimization method is proposed for determining the personalized product design architecture that incorporates individual customer preferences. First, a decision hierarchy is presented to describe the integrated design decisions of the product architecture, including product variety determination, module variant selection, and personalized module configuration. Next, a profit model is formulated as an overall performance metric that incorporates customer preferences and manufacturing cost. The systematic patterns and randomness of diverse customer preferences are modeled by combining conjoint analysis and market segmentation with a multivariate normal mixture model. Individual customer product utilities in the target market and their product purchase intent probability are estimated through Monte-Carlo simulation, which is incorporated into the profit calculation. Manufacturing limitations on processes and materials are included as they influence manufacturer’s planning on candidate module variants and production strategies of personalized modules. These models are used to determine a product family architecture that maximizes profit by optimally determining its offering of product variants, module combinations, and personalized module configuration through a genetic algorithm. The proposed method is demonstrated by a personalized bicycle architecture design example.     

Product platform design through sensitivity analysis and cluster analysis

Scale-based product platform design consists of platform configuration to decide which variables are shared among which product variants, and selection of the optimal values for platform (shared) and non-platform variables for all product variants. The configuration step plays a vital role in determining two important aspects of a product family: efficiency (cost savings due to commonality) and effectiveness (capability to satisfy performance requirements). Many existing product platform design methods ignore it, assuming a given platform configuration. Most approaches, whether or not they consider the configuration step, are single-platform methods, in which design variables are either shared across all product variants or not shared at all. In multiple-platform design, design variables may be shared among variants in any possible combination of subsets, offering opportunities for superior overall design but presenting a more difficult computational problem. In this work, sensitivity analysis and cluster analysis are used to improve both efficiency and effectiveness of a scale-based product family through multiple-platform product family design. Sensitivity analysis is performed on each design variable to help select candidate platform design variables and to provide guidance for cluster analysis. Cluster analysis, using performance loss due to commonization as the clustering criterion, is employed to determine platform configuration. An illustrative example is used to demonstrate the merits of the proposed method, and the results are compared with existing results from the literature.     

Performing ergonomics analyses through virtual interactive design: Validity and reliability assessment

Virtual interactive design methodology was initially proposed as an integrated environment for digital human model–based ergonomics analysis of human–machine interactions, which uses a motion capture system and virtual environment to realize task simulation. The validity and reliability of this analysis methodology has not been systematically studied. In this article, potential errors are first investigated based on the technology used in the system structure and work process; then, three experimental integration levels are proposed to use different resources for creating manikin postures. Validity and reliability to use this integrated environment for static ergonomics analysis are then assessed by comparing the ergonomics analysis outputs achieved based on the three integration levels. The results indicate that the reliability of this methodology is good; however, the validity of the integrated system is affected by the limitation of the virtual environment used in the testing. © 2011 Wiley Periodicals, Inc.     

A product configuration analysis method for emotional design using a personal construct theory

Identifying emotion-related product attributes (perceived by consumers) is no easy task in the realm of emotional design. Conventionally, this process relies heavily on the researchers who conduct the Kansei experiments selecting product attributes such as color, form, and texture for Kansei studies. However, in so doing, other product attributes that also play a vital role in product-emotion associations might be neglected by the researchers. More importantly, the identification of product attributes should be based on consumer's point of view (and feelings). Accordingly, a personal construct theory based product configuration analysis method is proposed in this work. The method develops the customer's mind map for each Kansei tag in order to capture replications of candidate products. A means-value chain is used to generate targets which are later compared with candidate products by consumers. The comparison results could suggest product attributes that are relevant to the desired Kansei. The proposed approach is presented and illustrated using a case study of Graffiti designs on notebooks. Results obtained are discussed. It appears that the proposed method is promising in identifying product attributes with desired Kansei impacts.     

Developing a design support system for the exterior form of running shoes using partial least squares and neural networks

The function of sport shoes is to improve sport performance and reduce sports-related injuries. They are commercial products developed by combining sports technology and marketing activities. Numerous studies on research and development, material application, production process improvements, and human physiological measurements for the sport shoe market exist. However, few studies have conducted an in-depth investigation on the design of forms and external appearance for sports shoes.     

The Revised Strain Index: an improved upper extremity exposure assessment model

Abstract

The Revised Strain Index (RSI) is a distal upper extremity (DUE) physical exposure assessment model based on: intensity of exertion, frequency of exertion, duration per exertion, hand/wrist posture and duration of task per day. The RSI improves upon the 1995 Strain Index (SI) by using continuous rather than categorical multipliers, and replacing duty cycle with duration per exertion. In a simulation of 13,944 tasks, the RSI and 1995 SI showed good agreement in risk predictions for 1995 SI scores ≤3 (safe) and >13.5 (hazardous). For tasks with 1995 SI scores of >3 and ≤13.5, the two models showed marked disagreement, with the RSI providing much greater discriminations between ‘safe’ and ‘hazardous’ tasks for various combinations of force, repetition and duty cycle. We believe the RSI is a substantially improved model that will be useful for DUE task analysis, intervention and design.

Practitioner Summary: RSI is a substantial improvement over the 1995 SI. It should be a valuable tool for designing and analysing tasks to determine risk of musculoskeletal injuries. RSI is applicable to a wide variety of tasks including very low force and very high repetition tasks such as keyboard use.