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Artificial Intelligence (AI) has played an important role for data-driven decision making in complex engineering problems. However, there has been a huge waste of efforts to configure AI methods (e.g., to select preprocessing and modeling methods, etc.), catering to different contexts (e.g., data analytics objectives, data distributions, etc.). In current practice, data scientists need to manually configure the AI methods in trial-and-errors according to a specific context, including determining the different options of the pipeline components and evaluating the advantages and limitations of an AI method. In this paper, we propose a Local Low-rank Response Imputation (Lori) method, which will automatically configure AI methods to specific contexts by completing a sparse context pipeline response matrix. Different from the traditional recommendation systems, Lori performs multivariate partition of the entire context-pipeline response matrix based on the principal Hessian directions of the low-rank imputed response matrix. Thus, the partitioned local low-rank response matrices can be closely modeled to automatically match the AI methods with the data sets. A small-scale and a large-scale case studies in three manufacturing processes demonstrated the merits of the proposed Lori method. 

Systems with both quantitative and qualitative responses are widely encountered in many applications. Design of experiment methods are needed when experiments are conducted to study such systems. Classic experimental design methods are unsuitable here because they often focus on one type of response. In this paper, we develop a Bayesian D-optimal design method for experiments with one continuous and one binary response. Both noninformative and conjugate informative prior distributions on the unknown parameters are considered. The proposed design criterion has meaningful interpretations regarding the D-optimality for the models for both types of responses. An efficient point-exchange search algorithm is developed to construct the local D-optimal designs for given parameter values. Global D-optimal designs are obtained by accumulating the frequencies of the design points in local D-optimal designs, where the parameters are sampled from the prior distributions. The performances of the proposed methods are evaluated through two examples. 

Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.