Additive manufacturing (AM) can build up complex parts in a layer-by-layer manner, which is a kind of novel and flexible production technology. The special manufacturing capability of AM shows great application potential in various fields. However, an open-loop control method cannot guarantee the reliability and repeatability of an AM process. Defects often occur to deteriorate product quality and lead to material and time waste, which hinders the development of AM industry. In this regard, a lot of efforts have been made to make an AM process more controllable. This work proposes an AM control framework that divides the related studies into three feedback loops, including the in-situ monitoring of process defects, fault diagnosis of 3-D printers, and closed-loop control of an AM process. These three loops constitute the inspection and control of AM from the machine level to product level. Specifically, the measurement requirements for monitoring techniques, defect detection, fault diagnosis, and closed-loop control are summarized. The challenges and future trends in realizing a more reliable and repeatable AM process are discussed. Note to Practitioners —This survey is motivated by urgent need to solve product quality problems in additive manufacturing (AM) caused by open-loop control. Three feedback loops can be established to solve them. The first one is defect detection that inspects part quality during fabrication. The second one is the fault diagnosis of a 3-D printer that monitors the health and operation conditions of its actuators. The last one is closed-loop control that improves AM process reliability and repeatability by regulating process variables in real time. These three loops are all based on the feedback signals of in-situ monitoring systems. This paper reviews the related studies and provides guidance for establishing the monitoring systems, performing defect detection and fault diagnosis, and designing closed-loop control systems, which helps realize more reliable and repeatable AM.

This article studies how to solve the energy management (EM) problem while balancing economy and security. Firstly, a defense scheme is developed to relieve the negative effects of denial-of-service (DoS) attacks. Secondly, a secure predefined-time distributed EM algorithm via the time-base generator (TBG) is devised which can be suitable for directed graphs, and sensitive information is decomposed into two dynamic variables for key information privacy preservation. Thirdly, a dynamic event-triggered (DET) mechanism is proposed to economize communication resources. By constructing the Lyapunov energy function, it is proven that the convergence time of the devised distributed algorithm can be predefined by users independent of initial conditions. Zeno behavior is proven to be avoided, which exhibits the effectiveness of the DET approach. At last, the simulations are given to show the validity and advancement of the designed secure distributed algorithm.

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer's destination and allocated parking space. In addition, the location privacy of the customers' destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme.

The penetration of plug-in electric vehicles (PEVs) has greatly increased over the past few years. By using vehicle-to-grid (V2G) technology, PEVs can be used as "mobile batteries" in a microgrid. Here, we aim to coordinate the V2G dispatch with traditional energy management in a corporate energy system (CES). To do so, a two-stage robust optimization (RO) model is built with respect to uncertainties in the CES, e.g., photovoltaic (PV) power. Particularly, relationships between the working time schedule and PEVs are investigated and analyzed for the first time, and a novel PEV aggregator model, i.e., shift-working V2G, is presented. The shift-working V2G model provides beneficial characteristics, like weakened randomness and stable storage capacity. A quantitative method to evaluate the V2G capacity is then presented. An analytical solution methodology is also proposed, which can equivalently convert the robust "min-max-min" model to a single-level mixed-integer linear programming (MILP) model. Case studies are conducted for an iron and steel company in Shanghai, China, with almost 40,000 PEVs. The results show that V2G integration can significantly improve the load-tracking ability of CES and help reduce the energy cost, although the V2G cost is considered. The computational efficiency is also improved compared with the existing methods.

This article focuses on the problem of deadlock for sequential automated manufacturing systems (AMSs) that allow for the general resource allocation and flexible routings. A class of Petri nets, systems of sequential systems with shared resources (S ⁴ PR), are used to model these considered AMSs. Our previous work has showed that deadlocks in S ⁴ PR are characterized by saturated perfect activity-circuit (PA-circuit). In this article, we divide all saturable PA-circuits into two categories: 1) dependent and 2) independent. An algorithm is proposed to compute all independent saturable PA-circuits. We prove that by adding a monitor for each independent PA-circuit to ensure that it is not saturated, all dependent PA-circuits cannot be saturated either and deadlocks in S ⁴ PR are successfully prevented. The presented method simplifies the structure of the deadlock controller without imposing tight constraints on the system. Finally, the proposed controller is illustrated by some examples.

Reducing energy consumption in the energy-intensive production systems is of great significance and attracts more and more attention. Although a lot of efforts have been devoted to formulate and solve the energy consumption optimisation problem in the Bernoulli serial lines, existing methods for solving the problem in long Bernoulli lines either cannot obtain the optimal solution or are time-consuming. For this purpose, this paper divides the problem in the long line into a set of problems in two-machine lines and proposes a computationally efficient and globally convergent algorithm to solve it. Extensive numerical experiments show that the proposed algorithm not only can optimally (in the numerical way) solve the energy consumption optimisation problem in long Bernoulli serial lines, but also is orders of magnitude more efficient than existing algorithms.