Publications

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Generative process planning describes methods process engineers use to modify manufacturing/process plans after designs are complete. A completed design may be the result from the introduction of a new product based on an old design, an assembly upgrade, or modified product designs used for a family of similar products. An engineer designs an assembly and then creates plans capturing manufacturing processes, including assembly sequences, component joining methods, part costs, labor costs, etc. When new products originate as a result of an upgrade, component geometry may change, and/or additional components and subassemblies may be added to or are omitted from the original design. As a result process engineers are forced to create new plans. This is further complicated by the fact that the process engineer is forced to manually generate these plans for each product upgrade. To generate new assembly plans for product upgrades, engineers must manually re-specify the manufacturing plan selection criteria and re-run the planners. To remedy this problem, special-purpose assembly planning algorithms have been developed to automatically recognize design modifications and automatically apply previously defined manufacturing plan selection criteria and constraints.

Sandia's Archimedes 3.0{copyright} Automated Assembly Analysis System has been applied successfully to several large industrial and weapon assemblies. These have included Sandia assemblies such as portions of the B61 bomb, and assemblies from external customers such as Cummins Engine Inc., Raytheon (formerly Hughes) Missile Systems and Sikorsky Aircraft. While Archimedes 3.0{copyright} represents the state-of-the-art in automated assembly planning software, applications of the software made prior to the technological advancements presented here showed several limitations of the system, and identified the need for extensive modifications to support practical analysis of assemblies with several hundred to a few thousand parts. It was believed that there was substantial potential for enhancing Archimedes 3.0{copyright} to routinely handle much larger models and/or to handle more modestly sized assemblies more efficiently. Such a mature assembly analysis capability was needed to support routine application to industrial assemblies that overstressed the system, such as full nuclear weapon assemblies or full-scale aerospace or military vehicles.

Designing products for easy assembly and disassembly during their entire life cycles for purposes including product assembly, product upgrade, product servicing and repair, and product disposal is a process that involves many disciplines. In addition, finding the best solution often involves considering the design as a whole and by considering its intended life cycle. Different goals and manufacturing plan selection criteria, as compared to initial assembly, require re-visiting significant fundamental assumptions and methods that underlie current assembly planning techniques. Previous work in this area has been limited to either academic studies of issues in assembly planning or to applied studies of life cycle assembly processes that give no attention to automatic planning. It is believed that merging these two areas will result in a much greater ability to design for, optimize, and analyze the cycle assembly processes. The study of assembly planning is at the very heart of manufacturing research facilities and academic engineering institutions; and, in recent years a number of significant advances in the field of assembly planning have been made. These advances have ranged from the development of automated assembly planning systems, such as Sandia's Automated Assembly Analysis System Archimedes 3.0{copyright}, to the startling revolution in microprocessors and computer-controlled production tools such as computer-aided design (CAD), computer-aided manufacturing (CAM), flexible manufacturing systems (EMS), and computer-integrated manufacturing (CIM). These results have kindled considerable interest in the study of algorithms for life cycle related assembly processes and have blossomed into a field of intense interest. The intent of this manuscript is to bring together the fundamental results in this area, so that the unifying principles and underlying concepts of algorithm design may more easily be implemented in practice.

Automatic assembly sequencing and visualization tools are valuable in determining the best assembly sequences, but without Human Factors and Figure Models (HFFMs) it is difficult to evaluate or visualize human interaction. In industry, accelerating technological advances and shorter market windows have forced companies to turn to an agile manufacturing paradigm. This trend has promoted computerized automation of product design and manufacturing processes, such as automated assembly planning. However, all automated assembly planning software tools assume that the individual components fly into their assembled configuration and generate what appear to be a perfectly valid operations, but in reality the operations cannot physically be carried out by a human. Similarly, human figure modeling algorithms may indicate that assembly operations are not feasible and consequently force design modifications; however, if they had the capability to quickly generate alternative assembly sequences, they might have identified a feasible solution. To solve this problem HFFMs must be integrated with automated assembly planning to allow engineers to verify that assembly operations are possible and to see ways to make the designs even better. Factories will very likely put humans and robots together in cooperative environments to meet the demands for customized products, for purposes including robotic and automated assembly. For robots to work harmoniously within an integrated environment with humans the robots must have cooperative operational skills. For example, in a human only environment, humans may tolerate collisions with one another if they did not cause much pain. This level of tolerance may or may not apply to robot-human environments. Humans expect that robots will be able to operate and navigate in their environments without collisions or interference. The ability to accomplish this is linked to the sensing capabilities available. Current work in the field of cooperative automation has shown the effectiveness of humans and machines directly interacting to perform tasks. To continue to advance this area of robotics, effective means need to be developed to allow natural ways for people to communicate and cooperate with robots just as they do with one another.