This paper describes methods for implementation of non-contact eddy current array (ET-Array) sensing for industrial processes. In-process (in-situ) sensing for additive manufacturing (AM) and continuous manufacturing processes (e.g., for multilayered electrode sheets) must keep up with production rates while enabling process qualification. This paper presents novel implementations of unique ET-Arrays (called MWM-Arrays) that have been continually upgraded since foundational research at MIT. This has included “air calibration”, model-based multivariate inverse methods (MIMs), intelligent filtering, fully parallel electronics, small sensing element size (down to 0.75 mm), and support for established artificial intelligence/machine learning (AI/ML) capabilities. Processes such as laser powder bed fusion (LPBF), directed energy deposition (DED), and additive friction stir deposition (AFSD) require more observability than is currently available with conventional (e.g., optical) methods to overcome specification hurdles associated with critical component qualification (for processes, machines, and parts). Recent results are presented for LPBF in-situ sensing along with a description of methods and advantages of novel ET-Arrays and data analytics enhancements. A key goal for LPBF in-situ sensing is to provide an alternative to computed tomography (CT) for qualification. Achieving this requires reliable detection of defects approximately 0.25 mm or larger. Moreover, as AM parts increase in size and complexity, CT may no longer meet qualification requirements. Layer-by-layer in-situ ET-Array sensing has the potential to be a viable alternative to CT for component qualification. Although the majority of recent work has focused on AM, this technology also offers value for legacy and advanced processes that lack observability for defects, geometry/dimensions, and material properties.