Application of EbereDimMT001 with Fuzzy Logic in Product Quality Technology Maturity Assessment of Metal Additive Manufacturing Process

Additive manufacturing technology has for a long time been referred to as a new technology in all publications to date. A technology of over 20years of application since 1996, still being regarded and qualified as emerging technology to date. It was based on the fact and curiosity that the new generic Semi-Direct Technology Maturity Assessment Model (SDTMAM), EbereDimMT001 was designed. Hence, there is a need to assess to determine the level of technology advancement in the additive manufacturing technology (AMT). The research therefore aimed at adapting and implementing an already developed and designed model on a metal additive manufacturing technology (MAMP), first along the process capability variable of product quality (PQ). The manufactured product achievable characteristics such as (i) dimensional accuracy (ii) surface roughness (iii) precision or repeatability and (iv) tolerance, were considered for the technology capability parameters, where 18-number of metal additive manufacturing parameters were adopted to cover to achieve wide satisfactory technology applications, operational conditions and attributes. The digital technology and artificial intelligence driven model, EbereDimMT001 was applied with fuzzy logic and Set theory-based 26-number experts’ survey questionnaires model was also adopted to source and collect the MAMP research data for maturity assessment. The 26-number experts’ survey questionnaires are the research statements or proposition generated and coined from the 28 number of established metal additive manufacturing process capability performance indices, which are the subsets of the operational MAMP parameters A capability maturity model integration (CMMI) maturity profile of the Software Engineering Institute (SEI) of the Carnegia Mellon University, USA was adopted for maturity profiling of the scientific technology maturity assessment of metal additive manufacturing process. The models were coupled in series and was implemented progressively on a metal additive manufacturing process, and the product quality (PQ) technology maturity level was found at the quantitatively managed maturity level of 4 of 5 after research and results simulation. Thus, the product quality technology maturity level (ML) of a MAMP is at 3.18ML of the 5MLs, which means 63.5% maturity, therefore represents the product quality (PQ) technology maturity level of a metal additive manufacturing process. Meanwhile, this research is a sole effort and at student reach. Especially the questionnaire administration and sourcing of the research data. Hence, it is observed that the questionnaire did not get to the most desired experts’ respondents at the upper echelon of the major or top-class metal additive manufacturing industries, research institutions, dealers and product users, for research data and probable difference or an improved result. So, there should be no limitation in the research respondents. data access and sourcing plan.