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Abstract Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control. 

A facile and novel processable method to synthesize the Ni nanoparticles (Ni NPs) by tailoring their size in the matrix of the silicon oxycarbide (SiOC) ceramic system is reported. This method is based on polymer‐derived ceramics (PDCs), instead of the conventional powder route. The specific structural characteristics and magnetic properties of the various Ni NPs/SiOC composites as a function of carbon content are systematically investigated. The magnetic properties are experimentally investigated as a function of NP size and measurement temperature. It is demonstrated that the change in the size of Ni NPs (average from ≈4 to ≈ 19 nm) determines the magnetic nature of superparamagnetism. Zero‐field‐cooled (ZFC) and field‐cooled (FC) magnetization studies under magnetic fields of 100 Oe are performed. The saturatedMversusH(M–H) loops (saturation magnetization) increase and the coercivity decreases with the size reduction of Ni NPs. It is an indicator of the presence of superparamagnetic behavior and single‐domain NP for ceramic materials.