The synthesis of four-bar mechanisms is a well-understood, classical design problem. The original systematic work in this field began in the late 1800s and continues to be an active area of research. Limitations to the classical theory of four-bar synthesis potentially limit its application to certain real-world problems by virtue of the small number of precision points and unspecified order. This paper presents a numerical technique for four-bar mechanism synthesis based on genetic algorithms that removes this limitation by relaxing the accuracy of the precision points.

This paper proposes and evaluates an artificial neural network based method of modeling the dynamic behavior of spatially distributed continuous engineering processes. The technique is applicable to situations where the differential equations governing the behavior of a system are nonlinear and poorly understood, such as is the case for frost-heave and thaw-settlement processes in soils. A description is first provided of a means of modeling the unknown component of governing differential equations. A range of levels of understanding of these equations is considered. A method of discretizing the resultant neural models of these equations is then illustrated, and the way in which these can be used to simulate the behavior of a process is described. The performance of the proposed neural network approach is then assessed in a series of experiments simulating the nonlinear thermal behavior of translucent solid materials. The system is proven capable of providing highly accurate simulations of system behavior sustained over many thousands of simulation time steps. The paper concludes with an identification of several ongoing areas of further development and application of the proposed tool.

This paper presents an approach for the case adaptation, especially case repairing, in a case-based process planning system: PROCASE, for machining of rotational parts. In PROCASE, a new process plan is generated by adapting an existing similar process plan from its case library. Case adaptation is a crucial issue in achieving an automated case-based process planning system. This is because, usually, an existing process plan cannot necessarily produce an exact identical part as of the desired part. Adaptation is essential to tailor this existing plan to generate a new process plan for the new part. The case adaptation in this paper comprises case modification, case simulation, and case repairing. The modifier uses the knowledge extracted from case library to edit the retrieved similar plan. The simulator plays an important role in verifying the adapted plan as well as in directing the plan repairing. The repairing rules are indexed by the error messages obtained from the simulation. With the proposed case adaptation, the system will have the capability to repair the erroneous plans to achieve an automated and intelligent process planning system. This paper will first briefly introduce the case representation and case retrieval in PROCASE. Then the rest of the paper is dedicated to the case adaptation.

This paper gives a theoretical foundation and implementation details of data and control structures in a diagnostic expert system with adaptive uncertainty management. The considered problem covers a class of expert systems and the application domain in which the operating personnel actively monitor and control the outcome of an industrial manufacturing process. Based on subjective ranking of the latent fault in the final output product, the operator queries the expert system for a recommended corrective action. Subsequent successful or unsuccessful corrective actions consistently modify confidence measures of all corrective recommendations from the same fault set, according to the embedded analytical model. The described approach is based on the relational data representation and procedural control structures, as opposed to a more traditional declarative, production-based system.