Robotized unplugging of a cylindrical peg press-fitted into a cylindrical hole.

It is well accepted that remanufacturing, the returning of a product that has reached the end of its service life to its original condition, is economically and environmentally beneficial. Robotizing disassembly can make remanufacturing even more cost-effective by removing a substantial proportion of the labour costs associated with dismantling end-of-life products for subsequent processing. As unplugging of press-fitted components is a common operation in disassembly, it is appropriate to investigate how it can be robotized. This paper discusses an unplugging technique, twist-and-pull or twisting-pulling, to reduce the axial frictional resistance during the unplugging process and enable a robot to perform it easily. Through theoretical modelling, simulations, and experimental analysis, the paper explores the interaction between twisting, pulling and axial friction reduction during unplugging. Analysis of the experimental, simulation and theoretical results has confirmed that for a small radial interference, twist-and-pull reduces the axial friction and the maximum required unplugging force.

Application and planning of an energy-oriented stochastic disassembly line balancing problem.

End-of-life (EOL) products are getting more and more attention as a result of the rapid decline in environmental resources and the dramatic rise in population at the moment. Disassembly is a crucial step in the reuse of EOL products. However, the disassembly process for EOL products is highly uncertain, and the disassembly planning method may not produce the anticipated outcomes in actual implementation. Based on the physical nature of the product disassembly process with multiple uncertain variables, certainty disassembly cannot adequately characterize the uncertain variables effectively. Uncertainty disassembly takes into account the changes in parts caused by product use, such as wear and corrosion, which can better coordinate the arrangement of disassembly tasks and better match the actual remanufacturing process. After analysis, it was found that most of studies on uncertain disassembly focus on the economic efficiency perspective and lack of energy consumption considerations. For the gaps in the current study, this paper proposes a stochastic energy consumption disassembly line balance problem (SEDLBP) and constructs a mathematical model of SEDLBP based on the disassembly of spatial interference matrix, In this model, the energy consumption generated by the disassembly operation and workstation standby is not a constant value but is generated stochastically in a uniformly distributed interval. In addition, an improved social engineering optimization algorithm that incorporates stochastic simulation (SSEO) is proposed in this paper to effectively address the issue. The incorporation of swap operators and swap sequences in SSEO makes it possible to solve discrete optimization problems efficiently. A comparison of a case study with some well-tested intelligent algorithms demonstrates the efficacy of the solutions produced by the proposed SSEO.

A Two-Stage Screw Detection Framework for Automatic Disassembly Using a Reflection Feature Regression Model.

For remanufacturing to be more economically attractive, there is a need to develop automatic disassembly and automated visual detection methods. Screw removal is a common step in end-of-life product disassembly for remanufacturing. This paper presents a two-stage detection framework for structurally damaged screws and a linear regression model of reflection features that allows the detection framework to be conducted under uneven illumination conditions. The first stage employs reflection features to extract screws together with the reflection feature regression model. The second stage uses texture features to filter out false areas that have reflection features similar to those of screws. A self-optimisation strategy and weighted fusion are employed to connect the two stages. The detection framework was implemented on a robotic platform designed for disassembling electric vehicle batteries. This method allows screw removal to be conducted automatically in complex disassembly tasks, and the utilisation of the reflection feature and data learning provides new ideas for further research.

Hybrid evolutionary algorithm for stochastic multiobjective disassembly line balancing problem in remanufacturing.

With the development of the industrial economy and the accelerated renewal of products, many end-of-life products (EOL) have been generated to pollute our environment. This fact highlights the importance of recycling and remanufacturing EOL products as an active research topic. An efficient disassembly line is one solution for improving the remanufacturing and recycling processes of EOL products while reducing the environmental pollution. Although many optimization models and intelligent algorithms were developed to address the disassembly line balancing problem (DLBP), uncertainty was ignored by them. To alleviate the drawbacks of uncertainty for the disassembly operation, this study proposes a stochastic multi-objective optimization model for the DLBP minimizing the disassembly idle rate, smoothness, and energy consumption generated during the operation under uncertain operation time. Another novelty of this paper is to present an improved version of the northern goshawk optimization algorithm using a stochastic simulation method to solve the proposed model. The feasibility of the proposed model and the applicability of the developed algorithm are shown by two extensive examples. Finally, the performance of the proposed algorithm is revealed by a comparison with recent and state-of-the-art algorithms from the literature.

Environment-oriented disassembly planning for end-of-life vehicle batteries based on an improved northern goshawk optimisation algorithm.

Due to environmental pollution and resource shortages, the electric vehicle industry has been developing swiftly, and the market demand for batteries, as an essential part of electric vehicles, has also surged. Proper disassembly of end-of-life vehicle batteries (ELV batteries) is necessary to achieve the integrity and closure of their life cycle, promote the development of green remanufacturing, effectively reduce the pollution of the environment caused by metal ion leakage, and reduce people's dependence on natural resources to a certain extent. To schedule the disassembly operations of ELV batteries more rationally and further promote their disassembly quality and efficiency, this paper proposes a dual-objective disassembly sequence planning (DSP) optimisation model, which aims to minimise the hazard index and energy cost during ELV battery disassembly operations. Since the proposed model is a complex NP-hard optimisation problem, this study develops an efficient metaheuristic algorithm for solving this model based on the northern goshawk optimisation algorithm. The main algorithm adds two types of discrete recombination operators and a local search operator. At the same time, the predatory behaviour of the goshawk is optimised by combining the characteristics of the disassembly sequence planning problem to improve its performance. Finally, the disassembly of the battery of a Tesla Model 1 is used as a case study to demonstrate the effectiveness and feasibility of the proposed method.

Peg-hole disassembly using active compliance.

When considered in two-dimensional space, a cylindrical peg being withdrawn from a clearance-fit hole can exhibit one of four contact states: no contact, one-point contact, two-point contact and line contact. Jamming and wedging can occur during the two-point contact. Effective control of the two-point contact region can significantly reduce resistance in peg-hole disassembly. In this paper, we explore generic peg-hole disassembly processes with compliance and identify the effects of key parameters including the degree of compliance, the location of the compliance centre and initial position errors. A quasi-static analysis of peg-hole disassembly has been performed to obtain the boundary conditions of the two-point contact region. The effects of key variables on the two-point contact region have been simulated. Finally, peg-hole disassemblies with different locations of compliance centre achieved using active compliance have been experimentally investigated. The proposed theoretical model can be implemented to predict the range and position of the two-point contact region from the perspective of peg-hole disassembly.

Sustainable self-healing at ultra-low temperatures in structural composites incorporating hollow vessels and heating elements.

Self-healing composites are able to restore their properties automatically. Impressive healing efficiencies can be achieved when conditions are favourable. On the other hand, healing might not be possible under adverse circumstances such as very low ambient temperature. Here, we report a structural composite able to maintain its temperature to provide a sustainable self-healing capability-similar to that in the natural world where some animals keep a constant body temperature to allow enzymes to stay active. The composite embeds three-dimensional hollow vessels with the purpose of delivering and releasing healing agents, and a porous conductive element to provide heat internally to defrost and promote healing reactions. A healing efficiency over 100% at around -60°C was obtained. The effects of the sheets on the interlaminar and tensile properties have been investigated experimentally. The proposed technique can be implemented in a majority of extrinsic self-healing composites to enable automatic recovery at ultra-low temperatures.

Strain Modal Analysis of Small and Light Pipes Using Distributed Fibre Bragg Grating Sensors.

Vibration fatigue failure is a critical problem of hydraulic pipes under severe working conditions. Strain modal testing of small and light pipes is a good option for dynamic characteristic evaluation, structural health monitoring and damage identification. Unique features such as small size, light weight, and high multiplexing capability enable Fibre Bragg Grating (FBG) sensors to measure structural dynamic responses where sensor size and placement are critical. In this paper, experimental strain modal analysis of pipes using distributed FBG sensors ispresented. Strain modal analysis and parameter identification methods are introduced. Experimental strain modal testing and finite element analysis for a cantilever pipe have been carried out. The analysis results indicate that the natural frequencies and strain mode shapes of the tested pipe acquired by FBG sensors are in good agreement with the results obtained by a reference accelerometer and simulation outputs. The strain modal parameters of a hydraulic pipe were obtained by the proposed strain modal testing method. FBG sensors have been shown to be useful in the experimental strain modal analysis of small and light pipes in mechanical, aeronautic and aerospace applications.

A Multiuser Manufacturing Resource Service Composition Method Based on the Bees Algorithm.

In order to realize an optimal resource service allocation in current open and service-oriented manufacturing model, multiuser resource service composition (RSC) is modeled as a combinational and constrained multiobjective problem. The model takes into account both subjective and objective quality of service (QoS) properties as representatives to evaluate a solution. The QoS properties aggregation and evaluation techniques are based on existing researches. The basic Bees Algorithm is tailored for finding a near optimal solution to the model, since the basic version is only proposed to find a desired solution in continuous domain and thus not suitable for solving the problem modeled in our study. Particular rules are designed for handling the constraints and finding Pareto optimality. In addition, the established model introduces a trusted service set to each user so that the algorithm could start by searching in the neighbor of more reliable service chains (known as seeds) than those randomly generated. The advantages of these techniques are validated by experiments in terms of success rate, searching speed, ability of avoiding ingenuity, and so forth. The results demonstrate the effectiveness of the proposed method in handling multiuser RSC problems.

Honey Bees Inspired Optimization Method: The Bees Algorithm.

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

Influence of thermal treatment on nanostructured gold model catalysts.

We fabricated films of Au onto single crystal alumina (Al(2)O(3)(0001)) and nanostructured the surface using a high resolution focused ion beam (FIB) to remove specific regions of the film. The nanostructures consist of lines and orthogonal lines cut into the film, resulting in one- and two-dimensional islands of gold. When these films are heated above 300 °C, small nanoparticles of gold form due to the dewetting of the Au film from the alumina surface. The dimensions of these islands are dictated by the nature of the nanopatterning. The isolated islands generally have the smallest nanoparticles after heating, while the unpatterned film has much larger particles. Sintering is reduced within the nanostructured metal domains due to isolation of Au islands from each other. The evaporation rate is higher within these islands, due to the smaller size of nanoparticles and hence the higher effective vapor pressure over the surface (the Kelvin effect).

Effects of the nanostructuring of gold films upon their thermal stability.

We report results relating to the thermal stability of nanoparticles and show a remarkable effect of nanostructuring of the metal. Au films are nanostructured by focused ion beam sputtering (FIB) to produce isolated areas of metal, which are imaged by atomic force microscopy (AFM). Images of the surface show that, if the islands are made small enough, the metal in the islands is lost by evaporation, whereas the nonfabricated areas outside are relatively stable and the nanoparticles remain present there.