Automatic proposal of assembly work plans with a controlled natural language

Abstract

Manufacturing companies are progressively applying digital manufacturing tools to respond to increased product complexity in shortened product lifecycles. The application results in a comprehensive documentation of the product emergence process. Furthermore at Daimler, a controlled natural language has recently been established, which enables automated analysis of natural language work task description texts. This work proposes a methodology, which enhances planning efficiency by automatically presenting a set of potentially suitable work plans for novel products. The presented work plans are reused from past planning activities. Assessment of work plan suitability is based on a statistical analysis that employs Methods-Time Measurement (MTM) data as well as work task descriptions in a controlled natural language (cnl). The proposed methodology is compared to a previously presented approach, in which text mining is used instead of a controlled natural language. The test comprises 104 work tasks of a Daimler assembly line. While result quality is only slightly improved for the cnl based approach, mapping results from product clusters to assembly sequences are simplified and analysis effort can be reduced if a cnl is already established. Future investigations should focus on investigations of applicability to different production and assembly domains.