Early system design analysis and fault removal is an important step in the iterative design process to avoid costly repairs in the later stages of system development. System complexity is increasing with increased use of software to control the physical system. There is a dearth of techniques to evaluate inconsistencies, incompatibility, and fault proneness of the system design in an integrated manner. The early design analysis technique presented in this paper aids a designer to understand the interplay between the multifaceted components and evaluate his/her design in an integrated manner. The technique allows simultaneous propagation of different types of faults from various domains and evaluates their functional impact over a period of time. The structure of the technique is explained using domain-specific conceptual metamodels, whereas the execution is based on the event sequence diagram, which is one of the established reliability and safety analysis techniques. One of the notable features of the proposed technique is the object-oriented nature of the system design representation. The technique is demonstrated with the help of a case study, and the execution results of two scenarios are evaluated to demonstrate the analysis capability of the proposed technique.

A flow line is a conventional manufacturing system where all jobs must be processed on all machines with the same operation sequence. Line buffers allow nonpermutation flowshop scheduling and job sequences to be changed on different machines. A mixed-integer linear programming model for nonpermutation flowshop scheduling and the buffer requirement along with manufacturing implication is proposed. Ant colony optimization based heuristic is evaluated against Taillard's (1993) well-known flowshop benchmark instances, with 20 to 500 jobs to be processed on 5 to 20 machines (stages). Computation experiments show that the proposed algorithm is incumbent to the state-of-the-art ant colony optimization for flowshop with higher job to machine ratios, using the makespan as the optimization criterion.

Spatial grammars are rule-based, generative systems for the specification of formal languages. Set and shape grammar formulations of spatial grammars enable the definition of spatial design languages and the creation of alternative designs. The original formalism includes labels that provide the possibility to restrict the application of rules or to incorporate additional, nongeometric information in grammar rules. Labels have been used in various ways. This paper investigates the different uses of labels in existing spatial grammars, both paper based and computational, and introduces a new concept of three-dimensional (3-D) labels for spatial grammars. The approach consolidates the different label types in one integrated concept. The main use of 3-D labels is that they can simplify the matching of the left-hand side of rules in parametric grammars. A prototype implementation is used to illustrate the approach through a mechanical engineering example of generating robot arm concepts. This approach more readily enables the use of complex solid geometry in the definition and application of parametric rules. Thus, the flexible generation of complex, meaningful design solutions for mechanical engineering applications can be achieved using parametric spatial grammars combined with 3-D labels.

Appropriate topology and coefficients have a great impact on their models' performances. This paper proposes an enhanced group method of data handling (GMDH) algorithm using a modified Levenberg–Marquardt (LM) method. In this work, evolutionary algorithm and LM techniques are deployed simultaneously for optimal design of both connectivity configuration and associated coefficients of each candidate solution in the evolving population combination. However, use of singular value decomposition technique with LM is considered as a novel method to overcome the problem of initial guess, which is presented for the first time in this research. In order to illustrate the benefits of the proposed algorithm, it has been applied in a Malaysian manufacturing company for forecasting in inventory control. Moreover, a comparison between the basic GMDH algorithm and the enhanced GMDH algorithm is made, and the results show that the accuracy of the proposed method is considerably high with reasonable reduction in processing time. Therefore, the enhanced GMDH method can be used to replace old techniques in an inventory control system to generate a structure when it is applied to a Kanban setting in the just in time system.

Generative product design systems used in the context of mass customization are required to generate diverse solutions quickly and reliably without necessitating modification or tuning during use. When such systems are employed to allow for the mass customization of product form, they must be able to handle mass production and engineering constraints that can be time-consuming to evaluate and difficult to fulfill. These issues are related to how the constraints are handled in the generative design system. This article evaluates two promising sequential constraint-handling techniques and the often used weighted sum technique with regard to convergence time, convergence rate, and diversity of the design solutions. The application used for this purpose was a design system aimed at generating a table with an advanced form: a Voronoi diagram based structure. The design problem was constrained in terms of production as well as stability, requiring a time-consuming finite element evaluation. Regarding convergence time and rate, one of the sequential constraint-handling techniques performed significantly better than the weighted sum technique. Nevertheless, the weighted sum technique presented respectable results and therefore remains a relevant technique. Regarding diversity, none of the techniques could generate diverse solutions in a single search run. In contrast, the solutions from different searches were always diverse. Solution diversity is thus gained at the cost of more runs, but no evaluation of the diversity of the solutions is needed. This result is important, because a diversity evaluation function would otherwise have to be developed for every new type of design. Efficient handling of complex constraints is an important step toward mass customization of nontrivial product forms.

Design rationale (DR) explains why an artifact is designed the way it is. An explicit representation of DR is helpful to designers, allowing them to understand, improve, and reuse previous designs. The argumentation-based representation is the mainstream approach to DR representation. It has a semiformal graphical format to depict the structure of arguments for solving a design problem. This paper argues that because the design is not just a problem-solving process but also a cognitive activity that is continuously iterative and evolving, the conventional argumentation-based representation of DR has some inherent limitations. An improved, intent-driven representation model is proposed to capture and formalize the DR and its evolving history to support DR reuse. The model's knowledge structure, consisting of DR elements and their relationships, is detailed. A preliminary knowledge representation of the model based on Web Ontology Language is introduced. Furthermore, the context of DR is defined to document the complete DR and support effective traceability of design thinking. A graphical DR modeling system is developed, and an example is demonstrated to verify the system's application and the effectiveness of the proposed representation model. The paper provides an effective method to retain and manage a designer's implicit design knowledge, which has the potential to significantly improve the integrated management of product development knowledge.

Detailed design is often a time-consuming and experience-dependent engineering process, where various detailed design knowledge can be reused. This paper proposes a formal approach for modeling detailed design knowledge for effective reuse. An extended structure–behavior–function model is developed for representing the structural, behavioral, and functional information in various life cycle periods of a detailed design. Based on the extended structure–behavior–function model, an issue- and solution-based approach is then developed to model the detailed knowledge of a mechanical design. The proposed approach is implemented in a detailed design knowledge modeling system, with a fixture design knowledge modeling as a brief example.