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.

Downtime is arguably the single most significant contributor to system inefficiency in a multistage manufacturing system. Achieving near-zero downtime has been the ultimate goal of production operation management at the plant floor. Accurate estimation of the impact of each downtime incident is of great importance for deciding where to allocate limited resources among various manufacturing stages. In this paper, we focus on quantitative analysis of the impact of each individual downtime event in terms of permanent production loss and financial cost. We start from the transient analysis of a single downtime event and later extend to more generic scenarios where downtime incidents occur concurrently at different stages. Apart from the analytical study, a practical computation procedure using real-time production records can be readily derived and implemented at the plant floor. Case studies are conducted to demonstrate its potential in facilitating the decision making at plant floor on project identification, prioritization, and budget allocation in a multistage manufacturing system.

A long-standing goal of the Human-Robot Collaboration (HRC) in manufacturing systems is to increase the collaborative working efficiency. In line with the trend of Industry 4.0 to build up the smart manufacturing system, the collaborative robot in the HRC system deserves better designing to be more self-organized and to find the superhuman proficiency by self-learning. Inspired by the impressive machine learning algorithms developed by Google Deep Mind like Alphago Zero, in this paper, the human-robot collaborative assembly working process is formatted into a chessboard and the selection of moves in the chessboard is used to analogize the decision-making by both human and robot in the HRC assembly working process. To obtain the optimal policy of the working sequence to maximize the working efficiency, agents in the system are trained with a self-play algorithm based on reinforcement learning, without guidance or domain knowledge beyond game rules. A convolution neural network (CNN) is also trained to predict the distribution of the priority of move selections and whether a working sequence is the one resulting in the maximum of the HRC efficiency. A height-adjustable standing desk assembly is used to demonstrate the proposed HRC assembly algorithm and its efficiency in real-time task planning.