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The cold sintering process (CSP) is a low-temperature consolidation method used to fabricate materials and their composites by applying transient solvents and external pressure. In this mechano-chemical process, the local dissolution, solvent evaporation, and supersaturation of the solute lead to “solution-precipitation” for consolidating various materials to nearly full densification, mimicking the natural pressure solution creep. Because of the low processing temperature (<300°C), it can bridge the temperature gap between ceramics, metals, and polymers for co-sintering composites. Therefore, CSP provides a promising strategy of interface engineering to readily integrate high-processing temperature ceramic materials (e.g., active electrode materials, ceramic solid-state electrolytes) as “grains” and low-melting-point additives (e.g., polymer binders, lithium salts, or solid-state polymer electrolytes) as “grain boundaries.” In this minireview, the mechanisms of geomimetics CSP and energy dissipations are discussed and compared to other sintering technologies. Specifically, the sintering dynamics and various sintering aids/conditions methods are reviewed to assist the low energy consumption processes. We also discuss the CSP-enabled consolidation and interface engineering for composite electrodes, composite solid-state electrolytes, and multi-component laminated structure battery devices for high-performance solid-state batteries. We then conclude the present review with a perspective on future opportunities and challenges. 

Abstract When maintenance resources in a manufacturing system are limited, a challenge arises in determining how to allocate these resources among multiple competing maintenance jobs. This work formulates an online prioritization problem to tackle this challenge using a Markov decision process (MDP) to model the system behavior and Monte Carlo tree search (MCTS) to seek optimal maintenance actions in various states of the system. Further, case-based reasoning (CBR) is adopted to retain and reuse search experience gathered from MCTS to reduce the computational effort needed over time and to improve decision-making efficiency. The proposed method results in increased system throughput when compared to existing methods of maintenance prioritization while also reducing the computation time needed to identify optimal maintenance actions as more information is gathered. This is especially beneficial in manufacturing settings where maintenance decisions must be made quickly to minimize the negative performance impact of machine downtime. 

Often in manufacturing systems, scenarios arise where the demand for maintenance exceeds the capacity of maintenance resources. This results in the problem of allocating the limited resources among machines competing for them. This maintenance scheduling problem can be formulated as a Markov decision process (MDP) with the goal of finding the optimal dynamic maintenance action given the current system state. However, as the system becomes more complex, solving an MDP suffers from the curse of dimensionality. To overcome this issue, we propose a two-stage approach that first optimizes a static condition-based maintenance (CBM) policy using a genetic algorithm (GA) and then improves the policy online via Monte Carlo tree search (MCTS). The static policy significantly reduces the state space of the online problem by allowing us to ignore machines that are not sufficiently degraded. Furthermore, we formulate MCTS to seek a maintenance schedule that maximizes the long-term production volume of the system to reconcile the conflict between maintenance and production objectives. We demonstrate that the resulting online policy is an improvement over the static CBM policy found by GA. 

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.