Smart manufacturing (SM) processes exhibit rapidly increasing complexity, nonlinear patterns in hyperdimensional spaces, high volumes of data, transient sources of variations, reduced lifetime, ultrahigh conformance, and non-Gaussian pseudo-chaotic behaviors. Standard quality control techniques and paradigms are not up to handling all these dynamics. Therefore, quality engineers went stagnant, with little innovation to offer to the manufacturing industry. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) have been applied to solve complex engineering problems and drive innovation. This new era where computer science principles are applied to quality control is called Quality 4.0 (Q4.0). However, the Six Sigma five-step problem-solving strategy (define, measure, analyze, improve, and control) does not fit the full ML cycle. The limitations of the Six Sigma techniques and paradigms in driving manufacturing innovation are discussed. A case study where a 3D quality pattern that can be easily detected by an MLA is not detected by traditional process monitoring methods. Early results motivate the development of the new era of Q4.0 without the limitations of Six Sigma and the potential of AI.
