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1. MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing

   https://doi.org/10.3390/s21010243  

   Wang, Lening; Du, Pang; Jin, Ran (January 2021, Sensors)

   null (Ed.)

   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. 

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2. What do we hear from a drum? A data-consistent approach to quantifying irreducible uncertainty on model inputs by extracting information from correlated model output data

   https://doi.org/https://doi.org/10.1016/j.cma.2020.113228  

   Butler, T.; Hakula, H. (October 2020, Computer methods in applied mechanics and engineering)

   null (Ed.)

   In manufacturing processes, controlling system responses with uncertain system inputs, e.g., due to variations in material parameters of critical system sub-components, is a crucial task for performing reliable quality control and verification & validation (V&V) of system design. As a model for a manufacturing process, we consider the production of drums, that is, thin elastic membranes, whose properties are modeled via Dirichlet Laplacian eigenproblems with uncertain diffusion coefficients. Both a quality control and V&V problem are formulated within a data-consistent framework utilizing push-forward and pullback measures. In both problems, the uncertain diffusion coefficients are parameterized for every instance and the corresponding eigen-information defines correlated data streams. Subsequently, the quantities of interest required in the solution to the data-consistent inverse problems are determined by an a posteriori analysis of these data streams using feature extraction techniques. While the methodology proposed here is quite general, the specific efficacy of the proposed methodology is comprehensively explored in the numerical results for both the quality control and V&V problems associated with the manufacturing of drums. 

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3. Distributed cryptosystem for service-oriented smart manufacturing

   https://doi.org/10.1080/24725854.2023.2291728  

   Krall, Alexander; Finke, Daniel; Yang, Hui (December 2024, IISE Transactions)

   Advanced sensing and cloud systems propel the rapid advancements of service-oriented smart manufacturing. As a result, there is widespread generation and proliferation of data in the interest of manufacturing analytics. The sheer amount and velocity of data have also attracted a myriad of malicious parties, unfortunately resulting in an elevated prevalence of cyber-attacks whose impacts are only gaining in severity. Therefore, this article presents a new distributed cryptosystem for analytical computing on encrypted data in the manufacturing environment, with a case study on manufacturing resource planning. This framework harmonizes Paillier cryptography with the Alternating Direction Method of Multipliers (ADMM) for decentralized computation on encrypted data. Security analysis shows that the proposed Paillier-ADMM system is resistant to attacks from external threats, as well as privacy breaches from trusted-but-curious third parties. Experimental results show that smart allocation is more cost-effective than the benchmarked deterministic and stochastic policies. The proposed distributed cryptosystem shows strong potential to leverage the distributed data for manufacturing intelligence, while reducing the risk of data breaches. 

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4. Characterizations and Optimization for Resilient Manufacturing Systems With Considerations of Process Uncertainties

   https://doi.org/10.1115/1.4055425  

   Ma, Qiyang; Che, Yiming; Cheng, Changqing; Wang, Zimo (February 2023, Journal of Computing and Information Science in Engineering)

   Abstract The recent COVID-19 pandemic reveals the vulnerability of global supply chains: the unforeseen supply crunches and unpredictable variability in customer demands lead to catastrophic disruption to production planning and management, causing wild swings in productivity for most manufacturing systems. Therefore, a smart and resilient manufacturing system (S&RMS) is promised to withstand such unexpected perturbations and adjust promptly to mitigate their impacts on the system’s stability. However, modeling the system’s resilience to the impacts of disruptive events has not been fully addressed. We investigate a generalized polynomial chaos (gPC) expansion-based discrete-event dynamic system (DEDS) model to capture uncertainties and irregularly disruptive events for manufacturing systems. The analytic approach allows a real-time optimization for production planning to mitigate the impacts of intermittent disruptive events (e.g., supply shortages) and enhance the system’s resilience. The case study on a hybrid bearing manufacturing workshop suggests that the proposed approach allows a timely intervention in production planning to significantly reduce the downtime (around one-fifth of the downtime compared to the one without controls) while guaranteeing maximum productivity under the system perturbations and uncertainties. 

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5. Conceptual Architecture of Digital Twin With Human-in-the-Loop-Based Smart Manufacturing

   https://doi.org/10.1115/IMECE2023-112791  

   Kim, Duck Bong; Bajestani, Mahdi Sadeqi; Shao, Guodong; Jones, Albert; Noh, Sang Do (October 2023, American Society of Mechanical Engineers)

   This paper proposes a conceptual architecture of digital twin with human-in-the-loop-based smart manufacturing (DH-SM). Our proposed architecture integrates cyber-physical systems with human spaces, where artificial intelligence and human cognition are employed jointly to make informed decisions. This will enable real-time, collaborative decision-making between humans, software, and machines. For example, when evaluating a new product design, information about the product’s physical features, manufacturing requirements, and customer demands must be processed concurrently. Moreover, the DH-SM architecture enables the creation of an immersive environment that allows customers to be effectively involved in the manufacturing process. The DH-SM architecture is well fitted to those relatively new manufacturing processes, such as metal additive manufacturing, since they can benefit from using digital twins, data analytics, and artificial intelligence for monitoring and controlling those processes to support non-contact manufacturing. The proposed DH-SM will enable manufacturers to leverage the existing cyber-physical system and extended reality technologies to generate immersive experiences for end users, operators, managers, and stakeholders. A use case of wire + arc additive manufacturing is discussed to demonstrate the applicability of the proposed architecture. Relevant development and implementation challenges are also discussed. 

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