In industrial applications, the quantity of error recovery codes far exceeds that required for supervisory control. Declarative programming techniques can be integrated with the traditional procedural control to reduce the efforts in developing error recovery codes and to provide flexibility for future modifications as new error types are encountered. A flexible control system that employs reasoning for both control and error recovery should distribute its functions among hierarchical levels. A symbolic level for task level control and error recovery reasoning is composed of a system model, an assembly plan and a reasoning system. A device level includes all the devices and modular control programs. A robotic assembly of a gear plate is presented as an example application of the intelligent error recovery system. The results from two implemented versions of the example application show that incremental revisions to such a system can be made easily and cost effectively, but the system requires a relatively large initial declarative programming investment.>

The diagnostic step of the error recovery process in manufacturing systems is discussed, and an algorithm for constructing and pruning a failure propagation tree is formalized. The methodology of building of failure propagation tree is proposed. This methodology is based on a hierarchical process plan, a precondition-postcondition model of manufacturing operations, and a classification of the failure reasons. The hierarchical operation model embodies the process plan, and the functional and structural classifications for each failure reason of every operation in the model. Based on the hierarchical operation model, the final failure propagation tree is generated or expanded by linking the operations in the model. The expansion process is accomplished by iteratively applying the expansion rule and priority rule to the operations in the hierarchical operation model.>

The authors focus on the diagnostic step of the error recovery process in manufacturing systems and develop theories for evaluating the diagnosability of failure knowledge in manufacturing systems. The theories are based on the action-error-feature data set, described previously by the authors (1989). The definition of one-error diagnosability and the necessary and sufficient conditions for multiple-error diagnosability are given. A simple matrix operation, called covering analysis, for testing the conditions is also presented. The methodology provides a qualitative measure for the embedded diagnostic knowledge in terms of the ability to diagnose the number of multiple simultaneous errors in the system. This provides the diagnosability of the model and the feasibility of the computation, because the covering analysis is simple and at most two analyses are required to determine multiple-fault diagnosability. A discussion of the results obtained and simple examples to illustrate those results are presented.>

This paper details the functions of a generic manufacturing workstation controller in the context of a computer integrated manufacturing (CIM) system. For each of these functions the required software and hardware performance characteristics are assessed. A brief overview of CIM is presented followed by a discussion of manufacturing workstations. The functions presented and described are task decomposition, inventory management, monitoring and control, reporting and communications. The performance attributes are presented using a qualitative scale. In software they include several characteristics of the operating system, application development potential, and communications support. The hardware attributes are discussed in terms of modularity of architecture, computing speed, memory requirements, I/O, interrupt structure, network support, remote unit support and environmental features. The results are concisely presented on two qualitative assessment matrices which match functions with performance attributes. Details supporting these assessments are presented in the text.

This paper reports on the experiences at Union University in using a mini-computer based time-sharing facility versus a loosely-coupled micro-computer based facility. These two facilities are the only computer resources used by two disjoint academic populations. Comparisons are drawn on acquisition costs, staffing, and operating costs. The micro-computer system is an inexpensive, viable alternative.

An efficient algorithm called PRIM is proposed for transposing an arbitraxy R ×C matrix which is too large to be stored in its entirety in working memory and which instead is stored by rows on disk. PRIM facilitates the execution of numerical matrix algorithms which operate both by rows and by columns.