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Digital low drop-out regulator (D-LDO) with fast settling time and superior transient response is gaining increasing attention to make up for the deficiency of analog LDO. However, as the traditional digital LDOs regulate the output voltage code at a rate of 1 bit per clock cycle, the transient response speed is limited. In this paper, a multi-bit conversion technique is proposed to improve the transient response speed. The multi-bit conversion technique is achieved by an error detector with adaptive regulation of proportion and integration parameters in the digital controller before pass devices. Besides, a voltage sensor and a time-to-digital converter are employed to convert the output voltage to digital codes. Implemented in a 180-nm CMOS process, the proposed D-LDO features under 36/33 mV of undershoot/overshoot at VOUT = 0.95 V as the load current steps up with 40 mA/1 ns on a 0.5 nF load capacitor. The simulated response time is 0.18-ns, the figure-of-merit of speed FOM1 is 0.65 ps and FOM2 achieves 0.068 pF.

The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process, and scheduling could exert significant effects on the energy performance of manufacturing systems. However, only a few studies have specifically addressed energy-efficient scheduling for remanufacturing. Considering the uncertain processing time and routes and the operation characteristics of remanufacturing, we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing. An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines, batch machines, and uncertain processing routes and time. The algorithm demonstrated superior performance in terms of optimal value, run time, and convergent generation in comparison with other algorithms. Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW · h of energy saving for the investigated problem size. In addition, the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%. This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.

The soaring global additive manufacturing (AM) market implies considerable potentials of energy and material savings. However, very few researches have addressed the energy and material efficiency issue in AM process through processing parameters optimization. In this study, we developed a predictive model of specific energy consumption (SEC) and metallic powder usage rate in laser cladding process. Three approaches were adopted to perform the modeling, namely, basic gene expression programming (GEP), response surface methodology (RSM), and integrated Tabu search and GEP (TS-GEP). Comparison amongst these methods revealed that TS-GEP demonstrated the highest fitting performance in terms of the root mean square deviation (RMSD) and coefficient of determination (R2). The experimental validation showed that TS-GEP enabled high robustness and precision of the modeling even though the accuracy of prediction was slightly lower than that of RSM in some cases. Analysis of variance was conducted to examine the contribution of the processing parameters. Results presented that the dominating factor was powder feed rate followed by laser power, Z-increment, and scanning speed irrespective of the interactive effects. With the predictive models, the Pareto front was determined by non-dominated sorting genetic algorithm II (NSGA-II) to provide the optimal set of processing parameters for the maximization of energy and metallic powder efficiency. This study would facilitate appropriate parameter selection of laser cladding process and assist the sustainable manufacturing in AM domain.

Scheduling has been extensively applied to remanufacturing for the organization of production activities, and it would directly influence the overall performance of the remanufacturing system. Since the conjunction of Petri net (PN) and artificial intelligence (AI) searching technique was demonstrated to be a promising approach to solve the scheduling problems in manufacturing systems, this study built a transition timed PN combined with heuristic A* algorithm to deal with the scheduling in remanufacturing. The PN was applied to the formulation of remanufacturing process, while the A* algorithm generated and searched for an optimal or near optimal feasible schedule through the reachability graph (RG). We took the high value-added cylinder block of engine as a research object to minimize the makespan of reprocessing a batch used components. This scheduling problem involved in batch and parallel processing machines, and the uncertain processing time and routes will complicate the scheduling problem. Three heuristics were designed to guide the search process through the RG in PN. To avoid state space explosion and select promising nodes, a new rule-based dynamic window was developed to improve the efficiency of the algorithm, and this rule was examined to outperform the conventional one. Under the determined scheduling strategy, the dynamic behavior of energy consumption rate during the processing time was simulated using PN tool, which would assist remanufacturers to develop potential strategies for energy efficiency improvement. Considering the uncertainty of processing time, the Monte Carlo simulation method was adopted to statistically analyze the distributions of makespan and total energy consumption, which would contribute to the comprehensive production scheduling and energy profile assessment for sustainable remanufacturing.

Random frequency diverse array (RFDA) based directional modulation (DM) was proposed as a promising technology in secure communications to achieve a precise transmission of confidential messages, and artificial noise (AN) was considered as an important helper in RFDA-DM. Compared with previous works that only focus on the spot of the desired receiver, in this work, we investigate a secrecy region around the desired receiver, that is, a specific range and angle resolution around the desired receiver. Firstly, the minimum number of antennas and the bandwidth needed to achieve a secrecy region are derived. Moreover, based on the lower bound of the secrecy capacity in RFDA-DM-AN scheme, we investigate the performance impact of AN on the secrecy capacity. From this work, we conclude that: 1) AN is not always beneficial to the secure transmission. Specifically, when the number of antennas is sufficiently large and the transmit power is smaller than a specified value, AN will reduce secrecy capacity due to the consumption of limited transmit power. 2) Increasing bandwidth will enlarge the set for randomly allocating frequencies and thus lead to a higher secrecy capacity. 3) The minimum number of antennas increases as the predefined secrecy transmission rate increases.

Cloud manufacturing (CMfg) has received increasingly attention from both academia and industry. Cloud service composition is a critical technique in CMfg that connects different available manufacturing cloud services (MCSs) to generate a composite manufacturing cloud service (CMCS) to satisfy users’ requirements. Many available MCSs with the same or similar functionality but different QoS attributes are deployed in the CMfg platform. So it is challenging to obtain an optimal CMCS to satisfy the users’ complex requirements. Considerable numbers of approaches have been proposed to solve this problem. However, most of them often fall in a local optimum instead of the global one. In this paper, a novel eagle strategy using uniform Mutation and modified Whale Optimization Algorithm (MWOA) is proposed to maintain a balance between the global and local search abilities. In this approach, the uniform mutation is applied to perform the global search to preserve the diversification of the population, and a modified whale optimization algorithm is designed to perform the local search. The performance of the new approach is verified on various benchmark functions and different scales of QoS-aware cloud service composition problems. The experimental results demonstrate that the proposed MWOA has superior performance over the other methods.

Improving the accuracy of material feeding for printed circuit board (PCB) template orders can reduce the overall cost for factories. In this paper, a data mining approach based on multivariate boxplot, multiple structural change model (MSCM), neighborhood component feature selection (NCFS), and artificial neural networks (ANN) was developed for the prediction of scrap rate and material feeding optimization. Scrap rate related variables were specified and 30,117 samples of the orders were exported from a PCB template production company. Multivariate boxplot was developed for outlier detection. MSCM was employed to explore the structural change of the samples that were finally partitioned into six groups. NCFS and ANN were utilized to select scrap rate related features and construct prediction models for each group of the samples, respectively. Performances of the proposed model were compared to manual feeding, ANN, and the results indicate that the approach exhibits obvious superiority to the other two methods by reducing surplus rate and supplemental feeding rate simultaneously and thereby reduces the comprehensive cost of raw material, production, logistics, inventory, disposal, and delivery tardiness compensation.

Accurate prediction of material feeding before production for a printed circuit board (PCB) template can reduce the comprehensive cost caused by surplus and supplemental feeding. In this study, a novel hybrid approach combining fuzzy c-means (FCM), feature selection algorithm, and genetic algorithm (GA) with back-propagation networks (BPN) was developed for the prediction of material feeding of a PCB template. In the proposed FCM&ndash, GABPN, input templates were firstly clustered by FCM, and seven feature selection mechanisms were utilized to select critical attributes related to scrap rate for each category of templates before they are fed into the GABPN. Then, templates belonging to different categories were trained with different GABPNs, in which the separately selected attributes were taken as their inputs and the initial parameter for BPNs were optimized by GA. After training, an ensemble predictor formed with all GABPNs can be taken to predict the scrap rate. Finally, another BPN was adopted to conduct nonlinear aggregation of the outputs from the component BPNs and determine the predicted feeding panel of the PCB template with a transformation. To validate the effectiveness and superiority of the proposed approach, the experiment and comparison with other approaches were conducted based on the actual records collected from a PCB template production company. The results indicated that the prediction accuracy of the proposed approach was better than those of the other methods. Besides, the proposed FCM&ndash, GABPN exhibited superiority to reduce the surplus and/or supplemental feeding in most of the case in simulation, as compared to other methods. Both contributed to the superiority of the proposed approach.

The process plan selection and job shop scheduling are carried out separately and sequentially in many factories and the scheduling is always conducted after the process plan of each job has been determined. In fact, the activities for the determination of process plan and the scheduling plan are coupled with each other and actually complementary. Implementation of the two activities with an appropriate collaborative approach is essential to achieve greater performance and higher productivity for the manufacturing system. In this article, a novel cross-entropy-based approach for the joint process plan selection and scheduling optimization that can assist process planning and scheduling system to achieve optimal scheduling plan and determine the operations, machine for each operation and operation sequence for each job collaboratively was proposed. In order to facilitate the manipulation and improve the optimized performance of the approach, an efficient representation scheme and a generation method for samples were developed. Meanwhile, the updating mechanism for new introduced probability distribution parameters according to which the cross-entropy procedure generates samples was established. To verify the adaptability and performance of the proposed approach, experimental studies were conducted and comparisons were made between this approach and some previous methods. The experimental results indicate that the proposed approach is an alternative and acceptable method to solve the joint process plan selection and scheduling optimization problem.

Data mining (DM) with Big Data has been widely used in the lifecycle of electronic products that range from the design and production stages to the service stage. A comprehensive analysis of DM with Big Data and a review of its application in the stages of its lifecycle will not only benefit researchers to develop strong research themes and identify gaps in the field but also help practitioners for DM application system development. In this paper, a brief clarification of DM-related topics is presented first. A flowchart of DM and the main content of the flowchart steps are given in which commonly used data preparation and preprocessing approaches, DM functions and techniques, and performances indicators are summarized. Then, a comprehensive review covering 105 articles from 2007 to 2017 on DM or Big Data applications in the electronics industry is provided according to the flowchart from various points of view such as data handling, applications of DM, or Big Data at different lifecycle stages, and the software used in the applications. On this basis, a diagram of data content for different knowledge areas and a framework for DM and Big Data applications in the electronics industry are established. Finally, conclusions and future research directions are given.

It is of more practical significance to carry out research on the integrated process planning and scheduling/rescheduling (IPPSR) to achieve a global improvement for the performance of a manufacturing system with sustained pursuit and various uncertainties. In this paper, a framework and a unified dynamic rescheduling model for IPPSR with three typical types of situations normally encountered in a production system, i.e. arrival of new jobs, machine breakdown and order cancellation have been constructed. Meanwhile, an improved evolutionary algorithm (IEA) for the integrated process planning and scheduling (IPPS) to generate an optimal initial scheduling plan has been developed. In order to improve the performance of the algorithm for IPPS and facilitate the extension of it to rescheduling situation, new genetic representation for the scheduling plan combined with process plans and corresponding genetic operators are developed. Based on the dynamic rescheduling model and initial optimised result, the integ...

Process planning is a function that establishes the technological requirements necessary to convert a part from its raw material to the finished form. Generally, the result of process planning is delivered to the workshop to guide the manufacturing process in the form of process plan. However, a part always has multi alternative process plans for the processing means and techniques are not unique, therefore, optimization and selection of process plans is an important task of flexible process planning. In this paper, the flexibility of process planning and the AND/OR network adopted to represent the flexibility of process plans were described, and a mathematical model for the optimization of flexible process planning based on the AND/OR network was established. On this basis, a new heuristic method, called cross-entropy (CE) approach, was proposed to optimize flexible process planning. In order to facilitate the implementation of the CE-based approach, the new sample representation and probability distribution parameter were introduced; meanwhile, the new sample generation mechanism was presented and the updating expression of probability distribution parameter was deduced. Case studies, used for comparing this approach with genetic algorithm (GA) and genetic programming (GP)-based approach, were discussed to indicate the performance and adaptability of the proposed CE-based approach in terms of the solution quality and computational efficiency of the algorithm. The results show that the CE-based approach is effective for the optimization research of flexible process planning.