Aiming at the partial disassembly of discarded products,by considering the increase in operation time caused by disassembly interference between parts during the disassembly process,a partial disassembly line balan-cing problem with disassembly interference between components was studied,and a multi-objective mathematical model was established with the objectives of minimizing the smoothing index and energy consumption while maximi-zing disassembly profits from the perspectives of efficiency,energy consumption,and profitability.To address this problem,an Improved Moth-flame Optimization algorithom(IMFO)based on Pareto dominance theory was pro-posed.A suitable mechanism of encoding and decoding was designed according to the characteristics of the problem,which could save storage space and simplify the optimization operator.To enhance the search depth and breadth of moths in the solution space,the local search and global exploration strategies with sequentially changing neighbor-hood structures were designed.The method based on the independent contribution to hyper volume was introduced to screen the Pareto optimal solution.Finally,the effectiveness of the proposed model and algorithm was validated through various types of numerical examples and case studies.The processes of different disassembly schemes were compared,and the changes in energy consumption and profit with respect to disassembly depth were analyzed.
Aiming at the issue of low path planning efficiency for Automated Guided Vehicle (AGV) facing mixed U-shaped and dynamic obstacles, an adaptive proximal distillation evolutionary reinforcement learning algorithm guided by intrinsic reward policy (IRPA-PDERL). Initially, Random Network Distillation is introduced as intrinsic rewards in the fitness function, which is enhanced the policy of diversity of elite policy evaluation. Secondly, an adaptive intrinsic reward weight factor α is designed to balance the algorithm on the capacity to exploration and exploitation, aiding AGV in selecting the optimal strategy in highly uncertain dynamic obstacle environments. The decreased parameter sensitivity results from optimizing the covariance parameters of the mutation operator by covariance matrix adaption evolution strategy. Comparing with multiple algorithms on three different environments, the experimental results show that the improved algorithm reduces paths by 12.65
Autonomous shoveling of loaders is the key technology to realize automatic and intelligent operation,and the tracking control of the target working trajectory is one of its core parts.The actual trajectory of the bucket in the pile is related to the indicators such as the operation output,so it is of great significance to realize the effective track-ing control of the target working trajectory.The PID and other control methods without system models have prob-lems such as large overshoot amplitude and buffeting under the system constraints.Since the Model Predictive Con-trol(MPC)has the advantage of effectively dealing with system constraints to make the system operate smoothly,it was introduced into the motion control of the loader's working mechanism and a trajectory-tracking control method was proposed for the working mechanism based on the Nonlinear Model Predictive Control(NMPC).A kinematic model of the working mechanism in the drive space was established.Then,the description of the working trajectory was given.Furthermore,a trajectory-tracking controller for the working mechanism was designed based on the NMPC method.Finally,the Simulink/ADAMS co-simulation was carried out with the general PID as the compari-son group.The analysis showed that under the same system constraints,for the different target trajectories,the maximum absolute error of the bucket-tip displacement based on the designed controller didn't exceed±0.052m,which was 71％lower than the PID controller,and the maximum absolute error of the bucket angle didn't exceed±2.58°,which was 16％lower than the PID controller.Moreover,the designed controller had a smoother control effect.The designed controller had better performance than the PID controller in dealing with system constraints and smoothness.
In the short term, a fully autonomous level of machine intelligence cannot be achieved. Humans are still an important part of HCI systems, and intelligent systems should be able to “feel” and “predict” human intentions in order to achieve dynamic coordination between humans and machines. Intent recognition is very important to improve the accuracy and efficiency of the HCI system. However, it is far from enough to focus only on explicit intent. There is a lot of vague and hidden implicit intent in the process of human–computer interaction. Based on passive brain–computer interface (pBCI) technology, this paper proposes a method to integrate humans into HCI systems naturally, which is to establish an intent-based HCI model and automatically recognize the implicit intent according to human EEG signals. In view of the existing problems of few divisible patterns and low efficiency of implicit intent recognition, this paper finally proves that EEG can be used as the basis for judging human implicit intent through extracting multi-task intention, carrying out experiments, and constructing algorithmic models. The CSP + SVM algorithm model can effectively improve the EEG decoding performance of implicit intent in HCI, and the effectiveness of the CSP algorithm on intention feature extraction is further verified by combining 3D space visualization. The translation of implicit intent information is of significance for the study of intent-based HCI models, the development of HCI systems, and the improvement of human–machine collaboration efficiency.
Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.
Increased demand for knowledge, evolving consumer attitudes, and the convenient online transaction opportunities provided by Internet technology have led to the rapid rise and growth of online knowledge payment. At present, studies on the online knowledge payment industry mainly focus on exploring the influencing factors of users' willingness to pay for online knowledge payment products (OKPPs), and there are fewer studies on the evaluation and rating methods of OKPPs. To address this problem, this paper proposes an improved group decision-making method based on consensus adjustment and prospect theory to realize the evaluation of OKPPs from the perspective of consumer experience value. The method uses interval-valued intuitionistic fuzzy numbers to process evaluation information, selects leading users (LUs) as decision makers, and identifies four evaluation criteria based on consumer experience value, which are functional value, self-fulfillment value, hedonic value, and emotional value. The proposed group decision-making method in this paper takes into account the consensus problem of LUs’ opinions, proposes a consensus adjustment method, and uses prospect theory to incorporate the psychological factors of LUs into the group decision-making process. Finally, the effectiveness and advantages of the method proposed in this paper are verified using an example and a comparison with existing methods. This research will provide methodological reference for knowledge payment platforms (KPPs) to select high quality OKPPs, and will also urge knowledge producers (KPs) to create OKPPs that can bring higher experience value to knowledge consumers (KCs). At the same time, this research makes possible the co-creation of knowledge between KPs and KCs, and enriches and develops the relevant research on the online knowledge payment industry.
To satisfy the requirements of automobile sequence in different processes of automobile production line,a collaborative sequencing optimization method for multi-stage automobile production line was proposed to improve the collaborative production capacity of multi-process workshop and reduce the production cost.The requirements of different processes were analyzed for automobile production line,and the re-sequence cost of the buffers was estab-lished by considering traditional optimization objectives such as the number of model changes in welding shop,the number of color changes in painting shop,the total overload and idle time in assembly shop,the material consump-tion rate in assembly shop.On this basis,a multi-objective optimization model of collaborative sequencing for auto-mobile multi-stage production line was established.A NSGA-Ⅲ based on inbound and outbound rules was used to optimize the model.Finally,the effectiveness of the proposed model and method was verified by a case study.
Traditional sequence recurrent neural networks (SRNNs) have the defect of long time dependence in the prediction of time series, resulting in their poor generalization ability. Moreover, it is required to traverse the whole training data set to realize supervised learning by SRNNs, which increases the time complexity and leads to their low prediction accuracy and high computation cost in the residual life prediction of space rolling bearings in the ground simulated space environment. In view of this, a novel SRNN named variational eligibility trace meta-reinforcement recurrent network (VETMRRN) is proposed for achieving higher residual life prediction accuracy and lower computation cost. In the proposed VETMRRN, a new sequence recurrent network structure is constructed to increase the memory amount of historical information, thus improving the long-term memory capacity of VETMRRN. Then, a hyperparameter self-initialization meta-learning network with an oracle gate mechanism is designed to self-initialize the hyperparameters of VETMRRN for fast determination of the optimal review sequence length. Hence, VETMRRN can adapt to different input sequence lengths and avoid the defect of long time dependence of traditional SRNNs. Furthermore, a variational auto-encoding meta policy gradient learning algorithm with an eligibility trace operator is designed to improve the training speed and enhance the global optimization effect for VETMRRN parameters. Based on the above advantages of VETMRRN, a new residual life prediction method of space rolling bearings in the ground simulated space environment is proposed. Firstly, the time-frequency fusion features are extracted by Shapely-value feature fusion from the vibration acceleration data of space rolling bearing as the performance degradation features. Then, the performance degradation features are input into VETMRRN to predict the performance degradation feature trends of space rolling bearings. Finally, a Weibull-distribution reliability model is established based on the performance degradation feature trend values to predict the residual life of space rolling bearings. The effectiveness of the proposed VETMRRN-based prediction method is verified by the vibration acceleration data collected from the self-built vibration monitoring platform of space rolling bearings in the ground simulated space environment. The results indicate that compared to traditional SRNNs, deep sparse auto-encoding neural network (DSAE-NN), and multi-kernel least-square support vector machine (MK-LSSVM), the proposed method can improve the prediction accuracy and reduce the computation cost in the residual life prediction of space rolling bearings. In the future, the generalization performance of VETMRRN still needs to be further improved.
The design projects scheduling problem is hard due to multiple concurrent projects,as well as preemptions caused by random rework and urgent tasks.Although an optimal policy of this problem can be theoretically obtained by traditional stochastic dynamic programming,it is computationally intractable due to the curse of dimensionality.To construct efficient approximation methods for large-scale problem instances,the original stochastic scheduling problem was approximately decomposed into deterministic scheduling sub-problems in each state to obtain a subopti-mal policy.A mixed-integer programming model for the deterministic scheduling sub-problems was established and the solution methods based on meta-heuristics and priority rules were proposed.Computational experiments were conducted based on benchmark PSPLIB,which validated the effectiveness of the model and algorithm in different scheduling environments.Computational results showed that the meta-heuristics was improved by more than 12％under the objective of individual projects'average percent delay compared with the best priority rule.The meta-heu-ristics were practical because of their high computational efficiency.
金属增材制造技术是近年来信息技术、新材料技术与制造技术等多学科融合发展的先进制造技术之一，然而，制件质量稳定性差和工艺可重复性低等缺点限制了该技术的广泛应用。其主要原因是金属增材制造过程是一个复杂的多物理场耦合的动态过程，而熔池作为重要载体包含着丰富的过程特征信息。熔池动态特征信息与制件质量和工艺可重复性密切相关，因此监测熔池动态能够为提高制件质量和工艺可重复性提供有力的数据支撑。通过对该领域文献的调研，总结了金属增材制造过程中熔池温度和形貌动态监测技术的研究现状，分析了现有技术存在的问题，展望了未来金属增材制造过程动态监测的研究方向和技术方法。
In the cloud manufacturing model, with the cloudization of massive manufacturing resources and the explosive growth of user demands, the traditional cloud manufacturing platform has problems such as insufficient computing power, lack of real-time data and difficulty in guaranteeing data security in the service selection link. To solve the above issues, a novel cloud manufacturing service selection method based on blockchain was proposed. The method combined subjective and objective weight analysis technology to design a distributed cloud manufacturing service selection model, which realized the transfer of computing power sources and decentralization to solve the problem of insufficient computing power; a double-chain blockchain data storage model based on the proof-of-result mechanism was proposed to ensure the real-time and security of contract data and delivery data generated at different time periods in the cloud manufacturing process. The experimental results showed that the method could simultaneously improve the speed and quality of cloud manufacturing service selection and realize real-time and secure storage of cloud manufacturing data, thus significantly improving the production and manufacturing efficiency.
With the rapid increase of multi-source heterogeneous dynamic data of mechanical products, the digital twin technology is considered to be an important method to realize the deep integration of product data and intelligent manufacturing. As a digital archive of the physical entity in entire life cycle, the mechanical product digital twin model is cross-phased and multi-domain. Therefore, safe and stable cooperative modeling has become a basic technical problem that needs to be solved urgently. In this paper, we proposed a blockchain-based collaborative modeling method for the digital twin ontology model of mechanical products. First, an authorization network was constructed among stakeholders. Then modeling processes of the digital twin were mapped to ontology operations and formatted through extensible markup language. Finally, consensuses were obtained based on practical byzantine fault tolerance. And a material modification process of a helicopter damper bearing was taken as an example to verify. The proposed method enables all participants to accurately obtain the latest state of the digital twin model, and has the advantages of tamper-proof, traceability, and decentralization.
In the cloud manufacturing service scenario, service composition developers often need to develop customized service compositions based on users′ manufacturing needs. With the prevalence of privacy protection laws and regulations, it has been difficult to implement commonly used user demand mining algorithms such as LDA topic model in practice. Through the cross-study of blockchain and federated learning technologies for user demand mining, a Decentralized Federated Latent Dirichlet Allocation algorithm（DAFedLDA） was proposed. In DAFedLDA,a Multi-Channel Access Control Scheme（MCACS） and a Random-Dropout Data Monitor Scheme（RDDMS） were presented based on the peer-to-peer distributed LDA. On the basis of the ProgrammableWeb.com dataset, a series of experiments demonstrated the effectiveness of the proposed algorithm.
To solve the problem of velocity jump and input chattering in the track tracking of the wall-climbing robot，a hybrid robust control algorithm is proposed based on a back stepping kinematics controller with a neurodynamics model and a neural sliding mode dynamics controller with a combined asymptotic law. The bounded and smooth virtual posture errors are obtained by using the neurodynamics model to suppress the sharp velocity jump caused by the traditional back stepping algorithm. The gain of sliding mode controller based on a combined asymptotic law is adjusted by using the adaptive radial basis function neural network to eliminate input chattering. The simulation data and experimental results show the effectiveness of the algorithm.
针对传统机器人抓取算法成本高、位姿估计准确率低、在极端场景中鲁棒性差等问题,提出一种基于单视图关键点投票的机器人抓取方法.该方法基于单个RGB图像,采用投票推理2D关键点的方式,再结合3D关键点的位置,利用多组点对映射关系(E-Perspective-n-Point,EPnP),算法计算物体的6D位姿,并将其转换为最优的机器人抓取姿势,实现机器人抓取.实验表明,即使在遮挡、截断、杂乱场景中,也能体现较好的估计结果.所提方法抓取成功率达到了 94％,能引导机器人实现准确抓取.
废旧汽车(ELV)回收再制造作为提升汽车产业可持续性的有效实践,由于回收质量、再制造成本的不确定导致决策者的风险不确定性,以废旧汽车回收再制造环节为对象,考虑废旧汽车回收质量的不确定及决策者的风险规避特性,运用Stackelberg博弈模型分析了再制造商与零售商的风险规避度对废旧汽车回收再制造供应链定价策略、参与者利润、ELV回收质量、以及供应链整体利润的影响.研究表明:随着零售商风险规避度的增大,再制造商的销售价格增加,而零售商的零售价格持续减低;随着再制造商风险规避度的增加,再制造商销售价格与零售商零售价格保持下降趋势;ELV回收质量与再制造商与零售商的风险规避度呈负相关的关系,且ELV回收质量越低,废旧汽车回收再制造供应链的期望效用越小.最后,建议废旧汽车回收时的定价决策要综合考虑再制造商与零售商的风险规避度以及废旧汽车回收质量.
共享电单车具有的速度快、舒适度高等特点使其拥有大量用户,运营商以过度投放策略来抢占市场先机进而引发了电单车故障率高、停放难且不规范等现实问题,增大了回收管理的难度.为此,研究了一种共享电单车绿色回收供应商选择方法,为回收管理提供决策支撑.首先,分析了共享电单车的回收需求特征,构建供应商评价指标体系,并引入区间直觉模糊集提出混合评价信息统一量化方法;其次,为集结群体评价信息,提出内嵌双参数的连续区间直觉模糊有序加权平均算子,并以群体一致性为目标构建了参数求解模型;然后,以所提算子为基础,提出用于解决回收供应商选择问题的群体决策方法;最后,通过共享电单车回收案例分析验证了所提方法的适用性与有效性.
数据监测与控制是铝电解过程提高生产质量的重要手段,针对铝电解过程的数据监测算法缺乏多样性、实时性和稳定性等问题,研究了工艺过程数据实时聚类方法,建立了一种基于自适应生长层次神经气(GHNG)的生产奇异性监测模型.该模型包括自适应学习、节点生成与删除、拓扑结构展示等机制,为提高模型稳定性和分析数据多样性的能力,综合利用节点累积误差自适应调节获胜节点及邻域节点权值;依据在线数据演化趋势动态删除、增加神经节点并更新聚类中心位置,实现实时展现数据实例动态聚类结果,进一步提高聚类算法的时效性,同时对在线监测模型和算法进行了性能测试.最后,通过铝电解过程数据监测实例验证了该模型和算法的奇异性监测能力更强,能对铝电解工艺过程进行准确、有效的监测和控制,为生产/管理者提供决策支持.
在具有产能约束的产品族网络市场中,研究一个具有双目标的领导者(自身利润和总消费者剩余最大化),以及多个以各自利润最大化为目标的跟随者,形成的复杂博弈问题.基于双层规划思想、带精英策略的快速非支配排序遗传算法和古诺博弈理论,设计一种含博弈的双层规划算法,采用精英策略提高算法的寻优能力和收敛速度,并运用数值算例分析算法的性能.基于该算法求解产品族网络市场竞争问题,得到网络均衡解集.研究发现:①领导者参与的产品变体细分市场的需求斜率越小(大)对关注自身利润(总消费者剩余)目标的领导者越有利;②网络中存在涟漪效应,产品变体产能约束能够抑制这种涟漪效应,但是企业总产能约束反而会加大涟漪效应;③在网络市场拓展中,领导者比跟随者更具有市场侵略性;④领导者更倾向于选择"远交近攻"的联盟方式,但跟随者之间更倾向于选择"近交远攻"的联盟方式.
