An Optimization Method for the Remanufacturing Dynamic Facility Layout Problem with Uncertainties

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. Facility layout design, as the cornerstone of effective facility planning, is concerned about resource localization for a well-coordinated workflow that leads to lower material handling costs and reduced lead times. However, due to stochastic returns of used products/components and their uncontrollable quality conditions, the remanufacturing process exhibits a high level of uncertainty challenging the facility layout design for remanufacturing. This paper undertakes this problem and presents an optimization method for remanufacturing dynamic facility layout with variable process capacities, unequal processing cells, and intercell material handling. A dynamic multirow layout model is presented for layout optimization and a modified simulated annealing heuristic is proposed toward the determination of optimal layout schemes. The approach is demonstrated through a machine tool remanufacturing system

Energy efficient cutting parameter optimization

Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented

A framework for energy monitoring of machining workshops based on IoT

Machining workshop is a widely distributed manufacturing system that consumes massive energy in low efficiency. Due to the complicated and dynamic energy flow of the machining workshop, machinery manufacturers still lack an effective method to monitor and manage the energy efficiency. Hence, this paper proposes an energy efficiency monitoring system for machining workshop with the support of the newly emerging Internet of Things (IoT) technology. With the application of the proposed system, potential opportunities for energy efficiency improvement can be identified. Machinery manufacturers can easily reduce energy consumption and energy cost by managing the machining process

Multi-objective Tool Sequence Optimization in 2.5D Pocket CNC Milling for Minimizing Energy Consumption and Machining Cost

Tool sequence selection is an important task for 2.5D pocket milling and has a significant influence on both the energy consumption and machining cost of the final product. In this paper, the influence of tool sequence on energy consumption is firstly analyzed. Then a multi-objective tool sequence optimization model is proposed with the objective of minimizing energy consumption and machining cost and solved by the graph algorithm. Finally, a case study is carried out to validate the proposed model and search for the trade-off solutions between energy consumption and machining cost

A big data based cost prediction method for remanufacturing end-of-life products

Remanufacturing is considered as an important industrial process to restore the performance and function of End-of-Life (EOL) products to a like-new state. In order to help enterprises effectively and precisely predict the cost of remanufacturing processes, a remanufacturing cost prediction model based on big data is developed. In this paper, a cost analysis framework is established by applying big data technologies to interpret the obtained data, identify the intricate relationship of obtained sensor data and its corresponding remanufacturing processes and associated costs. Then big data mining and particle swarm optimization Back Propagation (BP) neural network algorithm are utilized to implement the cost prediction. The application of presented model is verified by a case study, and the results demonstrates that the developed model can predict the cost of the remanufacturing accurately allowing early decision making for remanufacturability of the EOL products

An Optimization Method for the Remanufacturing Dynamic Facility Layout Problem with Uncertainties

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. Facility layout design, as the cornerstone of effective facility planning, is concerned about resource localization for a well-coordinated workflow that leads to lower material handling costs and reduced lead times. However, due to stochastic returns of used products/components and their uncontrollable quality conditions, the remanufacturing process exhibits a high level of uncertainty challenging the facility layout design for remanufacturing. This paper undertakes this problem and presents an optimization method for remanufacturing dynamic facility layout with variable process capacities, unequal processing cells, and intercell material handling. A dynamic multirow layout model is presented for layout optimization and a modified simulated annealing heuristic is proposed toward the determination of optimal layout schemes. The approach is demonstrated through a machine tool remanufacturing system

Networked and Distributed Control Method with Optimal Power Dispatch for Islanded Microgrids

In this paper, a two-layer network and distributed control method is proposed, where there is a top-layer communication network over a bottom-layer microgrid. The communication network consists of two subgraphs, in which the first is composed of all agents, while the second is only composed of controllable agents. The distributed control laws derived from the first subgraph guarantee the supply-demand balance, while further control laws from the second subgraph reassign the outputs of controllable distributed generators, which ensure active and reactive power are dispatched optimally. However, for reducing the number of edges in the second subgraph, generally a simpler graph instead of a fully connected graph is adopted. In this case, a near-optimal dispatch of active and reactive power can be obtained gradually, only if controllable agents on the second subgraph calculate set points iteratively according to our proposition. Finally, the method is evaluated over seven cases via simulation. The results show that the system performs as desired, even if environmental conditions and load demand fluctuate significantly. In summary, the method can rapidly respond to fluctuations resulting in optimal power sharing

Finite element analysis of rapid canine retraction through reducing resistance and distraction

Objective: The aims of this study were to compare different surgical approaches to rapid canine retraction by designing and selecting the most effective method of reducing resistance by a three-dimensional finite element analysis. Material and Methods: Three-dimensional finite element models of different approaches to rapid canine retraction by reducing resistance and distraction were established, including maxillary teeth, periodontal ligament, and alveolar. The models were designed to dissect the periodontal ligament, root, and alveolar separately. A 1.5 N force vector was loaded bilaterally to the center of the crown between first molar and canine, to retract the canine distally. The value of total deformation was used to assess the initial displacement of the canine and molar at the beginning of force loading. Stress intensity and force distribution were analyzed and evaluated by Ansys 13.0 through comparison of equivalent (von Mises) stress and maximum shear stress. Results: The maximum value of total deformation with the three kinds of models occurred in the distal part of the canine crown and gradually reduced from the crown to the apex of the canine; compared with the canines in model 3 and model 1, the canine in model 2 had the maximum value of displacement, up to 1.9812 mm. The lowest equivalent (von Mises) stress and the lowest maximum shear stress were concentrated mainly on the distal side of the canine root in model 2. The distribution of equivalent (von Mises) stress and maximum shear stress on the PDL of the canine in the three models was highly concentrated on the distal edge of the canine cervix. . Conclusions: Removal of the bone in the pathway of canine retraction results in low stress intensity for canine movement. Periodontal distraction aided by surgical undermining of the interseptal bone would reduce resistance and effectively accelerate the speed of canine retraction