To specify realistic tolerances and ensure acceptable product yields, a product development team must take into account the current process capability of the factory. Using historical process capability data in product development is frequently discussed in academic and industry literature. However, while many organizations have databases, the use of historical data in design is limited. This paper addresses the technical challenges of implementing a producibility analysis tool. First, a description of how data is currently collected and recorded is given. Second, the infeasibility of a “just-press-a-button” system for checking producibility is described. Finally, a new paradigm for data access is proposed, implemented in software, and tested on data used by industry.

This paper presents a methodology for assessment of diagnosability of mechanical and hydraulic systems. The method is developed on the basis of relationships between system performance parameters and physical objects, that is, components of the system. These relationships are identified by system functional domain and are modeled in terms of a bipartite graph, called Diagnosability Bipartite Graph (DBG). A matrix called Diagnosability Matrix (DM) represents the DBG. Various diagnosability parameters of the system are derived from the DBG and the DM and these are useful in evaluation and comparison of design variants of the system. These parameters: are maximum number of set conflicts (MNS), maximum number of components in a set conflict (MNCS), diagnosability effort and cost (DEC), and average merit of diagnosability (AMD). The design having the lowest value of MNCS, AMD, and DEC; and highest value of MNS has the highest diagnosability. On the basis of these, a best design alternative is selected from diagnosability point of view. Moreover, components, which have poor diagnosability, are also identified. Maximum number of set conflicts (MNS) also guides in system fault diagnosis. The proposed procedure aids in the design and development of maintainable systems from diagnosability consideration. The method can also be used for evaluating and comparing the diagnosability of the systems. This method is illustrated with the help of two examples.

This paper describes a new method for generating the turning-gait of a six-legged robot using a combined genetic algorithm (GA)-Fuzzy approach. The main drawback of the traditional methods of gait generation is their high computational load. Thus, there is still a need for the development of a computationally tractable algorithm that can be implemented online to generate stable gait of a multilegged robot. In the proposed genetic-fuzzy system, the fuzzy logic controllers (FLCs) are used to generate the stable gait of a hexapod and a GA is used to improve the performance of the FLCs. The effectiveness of the proposed algorithm is tested on a number of turning-gait generation problems of a hexapod that involve translation as well as rotation of the vehicle. The hexapod will have to take a sharp circular turn (either clockwise or counter-clockwise) with minimum number of ground legs having the maximum average kinematic margin. Moreover, the stability margin should lie within a certain range to ensure static stability of the vehicle. Each leg of a six-legged robot is controlled by a separate FLC and the performance of the controllers is improved by using a GA. It is to be noted that the actual optimization is done off-line and the hexapod can use these optimized FLCs to navigate in real-world scenarios. As an FLC is computationally less expensive, the proposed algorithm will be faster compared with the traditional methods of gait-generation, which include both graphical as well as analytical methods. The GA-tuned FLCs are found to perform better than the author-defined FLCs.

The external formation of integrated circuits based on lithographic processes is not the only possible method for manufacturing electron devices, either integrated circuits or photovoltaic cells. Planar technology, based on external formation, requires a certain sequence of interactions between the structured medium and the object being formed, in which the configuration of the region to be formed and its position are defined by the structure of the media and their alignment with the object. By contrast, in self-formation the interaction between an object to be formed and its chaotic medium is controlled by the object structure itself, and it is this interaction that changes the object's structure, leading to its increasing complexity. Three kinds of self-formation can be used in manufacturing: self-formation, based on a certain sequential interaction between an object and chaotic media; development, which requires only a single chaotic medium; and reproduction, which involves a developing object generating primary objects. All these cases represent self-formation of artificial systems. Simpler patterns of self-formation have already led to various patents and technologies, while more complex ones may be applied in the future. The following article attempts to define the necessary and sufficient conditions for self-formation.

The evolution of computer aided design (CAD) systems and related technologies has promoted the development of software for the automatic configuration of mechanical systems. This occurred with the introduction of knowledge aided engineering (KAE) systems that enable computers to support the designer during the decision-making process. This paper presents a knowledge-based application that allows the designer to automatically compute and evaluate mass properties of racing cars. The system is constituted by two main components: the computing core, which determines the car model, and the graphic user interface, because of which the system may be used also by nonprogrammers. The computing core creates the model of the car based on a tree structure, which contains all car subsystems (e.g., suspension and chassis). Different part–subpart relationships define the tree model and link an object (e.g., suspension) to its components (e.g., wishbones and wheel). The definition of independent parameters (including design variables) and relationships definition allows the model to configure itself by evaluating all properties related to dimension, position, mass, etc. The graphic user interface allows the end user to interact with the car model by editing independent design parameters. It visualizes the main outputs of the model, which consist in numeric data (mass, center of mass of both the car and its subsystems) and graphic elements (car and subsystems 3D representation).