Advanced weapon systems and space applications require precision parts with better surface finishes. As machine tool automation increases, machinist's skills decline with disuse and exacerbates quality problems. Yet the machinist's intuitive interaction with the machining process is currently an indispensable skill component. Such machinist interaction could, in the future, be replaced with strategic deployment of an integrated Multi-Spectral Sensor (LMSS) package to monitor machine performance, condition signals and fuse data from multiple sources with an Autonomous Intelligent System Module (AISM). Urea's Phase I objective is to establish feasibility of developing a generic open-architecture IMSS/AISM to monitor metal cutting, including identification of: time varying machining problems (tool wear); architecture for integrating an expert system and machine learning platforms to deal with dynamic, unpredictable changes; architecture requirements to interface with existing computer controllers; design specifications for IMSS/AISM (hardware & software); experimental protocol and computer simulation strategy to be carried out in Phase II. The approach is predicated on a synergistic amalgam of critical technology areas, including machine intelligence/learning, sensor data fusion, adaptive control, group technology & graphics-data base design. The generic, flexible, open-architecture IMSS/AISM design can be adapted for use in many machining operations without being limited to specific machine/cutting tools. Anticipated benefits/potential commercial applications - important benefits from IMSS/AISM will be consistent high quality machined parts, more efficient process plans, much less dependency on the unpredictable human variables, and greater control over machine shop costs. The commercial potential or the technology is significant, e.g., New machine tools and retrofit.
