Design of a Generic Model for Technology Maturity Assessment of Advanced Manufacturing Processes in Fuzzy Logic System

The need to ascertain the maturity of advanced manufacturing additive manufacturing technology necessitated the research into the planning, development and design of a novel generic semi-direct technology maturity assessment methodology (SDTMAM) model for technology maturity assessment of manufacturing technology.In doing so, the scope of research was decided within the independent advanced subtractive and additive manufacturing technologies.Study was based on a variable, manufacturing technologies or processes capability area of product quality, with subcomponents systematically and progressively assembled for a test-run.The initial 5-number subsystem manufacturing process product quality parameters were stipulated, and subsequently expanded to 18-number subunits too in numbers covering wider product quality technology maturity assessment parametric variables for both the subtractive and additive manufacturing technologies maturity project.28-number product quality manufacturing technology capability performance indices were generated, each coined to establish in all a 26-number of live research propositions and research statements used in the development of experts' survey questionnaire.An artificial intelligence (AI) fuzzy logic-based questionnaire model was designed, selectively for administration and research data collation.Capability maturity model integration (CMMI) was adopted for the product quality technology reference maturity profile and result data profiling.Then, the statistical processing and representation of data, the yield results and discussion, then presentation of result and analysis on implementation.However, the novel generic SDTMAM, EbereDimMT001 model was strictly followed and used independently already to determine the product quality technology maturity of advanced metal additive and metal subtractive manufacturing processes.Although with impressive and consistent results, yet there is a need for optimization of the model and results on further research.