Design fixation is a phenomenon with important significance to many fields of design due to the potential negative impacts it may have in design outcomes, especially during the ideation stage of the design process. The present study aims to provide a framework for understanding, or at least probing, design fixation by presenting a review of existing defixation approaches, as well as metrics that have been employed to understand and account for design fixation. This study also describes the results of two design by analogy (DbA) methods, WordTree and SCAMPER, to overcome design fixation in an experiment that involved 97 knowledge-domain experts. The study outcomes are at least twofold: a common framework of metrics and approaches to overcome design fixation in a wide range of design problems and nonintuitive results for DbA approaches in design fixation and other related creativity metrics. The application of WordTree and SCAMPER shows that both methods yield increased novelty compared to a control, where the SCAMPER results are significantly higher than WordTree. It is also found that WordTree mitigates design fixation whereas SCAMPER appears to be ineffective for this purpose but effective to generate an increased quantity of novel ideas. These results demonstrate that both DbA methods provide de-fixation capabilities and enhance designers’ creativity during idea generation.

The surface roughness of the machined parts is one of the most important factors that have considerable influence on the quality and functional properties of products. The objective of this study is development of a surface roughness prediction model for machining Inconel 718 in high-pressure jet assisted turning using the fuzzy expert system, where the fuzzy system is optimized using two bioinspired algorithms: genetic algorithm and particle swarm optimization. The effect of various influential machining parameters, such as diameter of the nozzle, pressure of the jet, cutting speed, feed rate, and distance between the impact point of the jet and cutting edge were taken into consideration in this study. The predicted surface roughness values obtained from developed fuzzy expert systems were compared with the experimental data, and the results indicate that proposed systems can be effectively used to estimate the surface roughness in high-pressure jet assisted turning.

The main function of a heavy truck is to transport goods, with ton-kilometers/year as an example of a major quantitative performance measure. Furthermore, the truck is directly operated by a driver, who has several additional functional requirements, of both ergonomic and communicative characters. Failure of these functions may be a subjective experience, differing between drivers, but the failures are still important. Today's just-in-time delivery systems rely on getting the goods on time, and this requires high availability. Availability is reduced not only by technical failures but also by subjectively experienced failures, because these also require repairs, or downtime. Product reliability is a systems property that cannot be attributed to a single component. It is in many cases related to interaction between components, or to interaction between humans and the technical system, in the case of subjectively experienced failures. Reliability assessments of systems with interactive functions require a system model that includes the interfaces between the technical system and human features that are carriers of interactive functions. This paper proposes a model of system architecture, for the purpose of reliability assessments, that integrates different and complementary representations, such as function–means diagrams and a design structure matrix. The novelty of the presented approach is that it treats and integrates the technical and the human subsystems through the human–technical system interfaces. The proposed systems reliability approach is described and verified with a component analysis case study of an extended truck cab and driver system.

Optimized lightweight manufacturing of parts is crucial for automotive and aeronautical industries in order to stay competitive and to reduce costs and fuel consumption. Hence, aluminum becomes an unquestionable material choice regarding these challenges. Nevertheless, using only virgin aluminum is not satisfactory because its extraction requires high use of energy and effort, and its manufacturing has high environmental impact. For these reasons, the use of recycled aluminum alloys is recommended considering their properties meet the expected technical and environmental added values. This requires complete reengineering of the classical life cycle of aluminum-based products and the collaboration practices in the global supply chain. The results from several interdependent disciplines all need to be taken into account for a global product/process optimization. Toward achieving this, a method for sustainability assessment integration into product life cycle management and a platform for life cycle simulation integrating environmental concerns are proposed in this paper. The platform may be used as a decision support system in the early product design phase by simulating the life cycle of a product (from material selection to production and recycling phases) and calculating its impact on the environment.

The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.

In decision-based design, the principal role of a designer is to make decisions. Decision support is crucial to augment this role. In this paper, we present an ontology that provides decision support from both the “construct” and the “information” perspectives that address the gap that existing research focus on these two perspectives separately and cannot provide effective decision support. The decision support construct in the ontology is the compromise decision support problem (cDSP) that is used to make multiobjective design decisions. The information for decision making is archived as cDSP templates and represented using frame-based ontology for facilitating reuse, consistency maintaining, and rapid execution. In order to facilitate designers’ effective reuse of the populated cDSP templates ontology instances, we identified three types of modification that can be made when design consideration evolves. In our earlier work, part of the utilization (consistency checking) of the ontology has been demonstrated through a thin-walled pressure vessel redesign example. In this paper, we comprehensively present the ontology utilization including consistency checking, trade-off analysis, and design space visualization based on the pressure vessel example.

More than 40 years after Rittel and Webber published the first articles on the theory of wicked problems this theory has been applied to a wide range of fields involved in real-world problem solving. Interest in the theory seems greater than ever. This has led to an interest in rethinking the theory. A number of authors do this by imposing interpretations on the theory that are incompatible with each other and with the statements of the theory's authors. We agree that it is time to critically reexamine the theory and rethink what implications it has for design. However, rather than imposing an incompatible interpretation, our approach is see what new conclusions can be drawn from a systematic and critical examination of what Rittel and Webber actually said. This reexamination of their specific claims and arguments is what we call untangling wicked problems. From this untangling, we derive new conclusions about how designers should tackle wicked problems and how design rationale can aid them in doing so.

Engineering design changes constantly occur in complex engineering design processes. Designers need appropriate measures to handle the numerous design changes in order to realize consistent and completely validated product models so that successful product development is assured. In this paper, a time-based mathematic model is presented to characterize the sequential change propagation process, and then the shortest path algorithm is given to find the most timesaving routes for changes to propagate to other dependent design tasks. An analysis method is introduced to compute the sensitivities of change impacts on the affected design tasks, which indicates that the more time consumed by a change to take its effect, the more sensitive the change impacts on those downstream dependent tasks. A case study of change propagations in motorcycle engine design process was presented to demonstrate the proposed method.

Many real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives (energy efficiency, robustness, and makespan) and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exists a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.

Design rationale (DR) is an important category within design knowledge, and effective reuse of it depends on its successful retrieval. In this paper, an ontology-based DR retrieval approach is presented, which allows users to search by entering normal queries such as questions in natural language. First, an ontology-based semantic model of DR is developed based on the extended issue-based information system-based DR representation in order to effectively utilize the semantics embedded in DR, and a database of ontology-based DR is constructed, which supports SPARQL queries. Second, two SPARQL query generation methods are proposed. The first method generates initial SPARQL queries from natural language queries automatically using template matching, and the other generates initial SPARQL queries automatically from DR record-based queries. In addition, keyword extension and optimization is conducted to enhance the SPARQL-based retrieval. Third, a design rationale retrieval prototype system is implemented. The experimental results show the advantages of the proposed approach.

Product configuration is when an artifact from a product family is assembled from a set of predefined components that can only be combined in certain ways. These ways are defined by configuration rules. The product developers inspect the configuration rules when they develop new configuration rules or modify the configuration rules set. The inspection of configuration rules is thereby an important activity to avoid errors in the configuration rules set. Several formulations of constraint satisfaction problems (CSPs) are proposed that facilitate the inspection of configuration rules in propositional logic (IF-THEN, AND, NOT, OR, etc.). Many of the configuration rules are so called production rules; that is, a configuration rule is an IF-THEN expression that fires when the IF condition is met. Several configuration rules build chains that fire during the product configuration. It is therefore important not only to inspect single configuration rules but also to analyze the effect of multiple configuration rules. Formulating the tasks as variations of the CSP can support the inspection activity. More specifically, we address the reformulation of configuration rules, testing of feature variant combinations, and counting of item quantities from an item set. The suggested CSPs are tested on industrial vehicle configuration rules for computational performance. The results show that the time for achieving results from the solving of the CSP is within seconds. Our future work will be to implement the various CSPs into a demonstrator that could be tested by product developers.

Dealing with component interactions and dependencies remains a core and fundamental aspect of engineering, where conflicts and constraints are solved on an almost daily basis. Failure to consider these interactions and dependencies can lead to costly overruns, failure to meet requirements, and lengthy redesigns. Thus, the management and monitoring of these dependencies remains a crucial activity in engineering projects and is becoming ever more challenging with the increase in the number of components, component interactions, and component dependencies, in both a structural and a functional sense. For these reasons, tools and methods to support the identification and monitoring of component interactions and dependencies continues to be an active area of research. In particular, design structure matrices (DSMs) have been extensively applied to identify and visualize product and organizational architectures across a number of engineering disciplines. However, the process of generating these DSMs has primarily used surveys, structured interviews, and/or meetings with engineers. As a consequence, there is a high cost associated with engineers' time alongside the requirement to continually update the DSM structure as a product develops. It follows that the proposition of this paper is to investigate whether an automated and continuously evolving DSM can be generated by monitoring the changes in the digital models that represent the product. This includes models that are generated from computer-aided design, finite element analysis, and computational fluid dynamics systems. The paper shows that a DSM generated from the changes in the product models corroborates with the product architecture as defined by the engineers and results from previous DSM studies. In addition, further levels of product architecture dependency were also identified. A particular affordance of automatically generating DSMs is the ability to continually generate DSMs throughout the project. This paper demonstrates the opportunity for project managers to monitor emerging product dependencies alongside changes in modes of working between the engineers. The application of this technique could be used to support existing product life cycle change management solutions, cross-company product development, and small to medium enterprises who do not have a product life cycle management solution.

Documents are a useful source of expert knowledge in organizations and can be used to foresee, in an earlier stage of a product's life cycle, potential issues and solutions that might occur in later stages of its life cycle. In this research, these stages are, respectively, design and assembly. Even if these documents are available online, it is rather difficult for users to access the knowledge contained in these documents. It is therefore desirable to automatically extract the knowledge contained in these documents and store them in a computer accessible or manipulable form. This paper describes an approach for the first step in this acquisition process: automatically identifying segments of documents that are relevant to aircraft assembly, so that they can be further processed for acquiring expert knowledge. Such identification of relevant segments is necessary for avoiding processing of unrelated information that is costly and possibly distracting for domain relevance. The approach to extracting relevant segments has two steps. The first step is the identification of sentences that form a coherent segment of text, within which the topic does not shift. The second step is to classify segments that are within the topics of interest for knowledge acquisition, that is, aircraft assembly in this instance. These steps filter out segments that are unrelated, and therefore need not be processed for subsequent knowledge acquisition. The steps are implemented by understanding the contents of documents. Using methods of discourse analysis, in particular, discourse representation theory, a list of discourse entities is obtained. The difference in discourse entities between sentences is used to distinguish between segments. The list of discourse entities in a segment is compared against a domain ontology for classification. The implementation and results of validation on sample texts for these steps are described.