Search results

As the last link of product remanufacturing, reassembly process is of great importance in increasing the utilization of remanufactured parts as well as decreasing the production cost for remanufacturing enterprises. It is a common problem that a large amount of remanufactured part/reused part which past the dimension standard have been scrapped, which have increased the production cost of remanufacturing enterprises to a large extent. With the aim to improve the utilization of remanufacturing parts with qualified quality attributes but exceed dimension, the purpose of this paper is to put forward a reassembly classification selection method based on the Markov Chain.

To begin with, a classification standard of reassembly parts is proposed. With the thinking of traditional ABC analysis, a classification management method of reassembly parts for remanufactured engine is proposed. Then, a homogeneous Markov Chain of reassembly process is built after grading the matching dimension of reassembly parts with different variety. And the reassembly parts selection model is constructed based on the Markov Chain. Besides, the reassembly classification selection model and its flow chart are proposed by combining the researches above. Finally, the assembly process of remanufactured crankshaft is adopted as a representative example for illustrating the feasibility and the effectiveness of the method proposed.

The reassembly classification selection method based on the Markov Chain is an effective method in improving the utilization of remanufacturing parts/reused parts. The average utilization of remanufactured crankcase has increased from 35.7 to 80.1 per cent and the average utilization of reused crankcase has increased from 4.2 to 14 per cent as shown in the representative example.

The reassembly classification selection method based on the Markov Chain is of great importance in enhancing the economic benefit for remanufacturing enterprises by improving the utilization of remanufactured parts/reused parts.

Nuclear waste tanks need to be cut into pieces before they can be safely disposed of, but the cutting process produces a large amount of aerosols with radiation, which is very harmful to the health of the operator. The purpose of this paper is to establish an intelligent strategy for an integrated robot designed for measurement and cutting, which can accurately identify and cut unknown nuclear waste tanks and realize autonomous precise processing.

A robot system integrating point cloud measurement and plasma cutting is designed in this paper. First, accurate calibration methods for the robot, tool and hand-eye system are established. Second, for eliminating the extremely scattered point cloud caused by metal surface refraction, an omnidirectional octree data structure with 26 vectors is proposed to extract the point cloud model more accurately. Then, a minimum bounding box is calculated for limiting the local area to be cut, the local three-dimensional shape of the nuclear tank is fitted within the bounding box, in which the cutting trajectories and normal vectors are planned accurately.

The cutting precision is verified by changing the tool into a dial indicator in the simulation and the experiment process. The octree data structure with omnidirectional pointing vectors can effectively improve the filtering accuracy of the scattered point cloud. The point cloud filter algorithm combined with the structure calibration methods for the integrated measurement and processing system can ensure the final machining accuracy of the robot.

Aiming at the problems of large measurement noise interference, complex transformations between coordinate systems and difficult accuracy guarantee, this paper proposes structure calibration, point cloud filtering and point cloud-based planning algorithm, which can greatly improve the reliability and accuracy of the system. Simulation and experiment verify the final cutting accuracy of the whole system.

For some special manipulators such as the ones work at the space station, nuclear or some other unmanned environments, the overload, collision, vibration, temperature change or release of the internal stress would affect the structural parameters. And thus the operation precision might constantly decrease in long-term use. In these unmanned environments, the unattended manipulators should calibrate itself when they execute high precision operations or proceed self-maintenances. The purpose of this paper is to propose an automatic visual assistant on-line calibration (AVOC) method based on multi-markers.

A camera fixed on the end of the manipulator is used to measure one to three identification points, which forms an unstable multi-sensor eye-in-hand system. A Gaussian motion method which combines the linear quadratic regulator control and extended Kalman filter together is proposed to make the manipulator track the planned trajectories when its inaccurate structural parameters form uncertain motion errors. And a Monte-Carlo method is proposed to form a high precision and stable signal acquisition when the visual system has measurement errors and intermittent signal feedback. An automatic sampling process is adopted to select the optimal measurement points basing on their variances.

Data analysis and experiment results prove the efficiency and feasibility of the method proposed in this paper. With this method, the positioning accuracy is largely promoted from about 2 mm to 0.04–0.05 mm.

Experiments were carried out successfully on a manipulator in a life sciences glove box that will work at the Chinese space station. It is a low cost and efficient manipulator calibration method. The whole autonomic calibration process takes less than 10 min and requires no human intervention. In addition, this method not only can be used in the calibration of other unmanned articulated manipulator that works in deep ocean, nuclear industry or space but also be useful for the maintenance work in modern factories owing a lot of industrial robots.

Movement of people from one retail trade area to another in search of better options and deals has been studied across the world owing to its large impact on trade flow. Studies have been done in various rural and urban settings. However, almost all except one fails to provide a comprehensive model of outshopping which has its own limitations with regard to its applicability’s across cultures and in various settings. Nonetheless findings from the literature provides necessary inputs to start studies in various other cultures and settings. Results are presented in form of various definitions, various types, methodologies used, factors identified (individual characteristics, market characteristics, product related variables and accessibility factors) and patterns across continents. Attempts have also been made to explain their applicability to Indian conditions along with various limitations and gaps.

The chapter discusses online branding in the business-to-business domain and the benefits it offers to B2B actors. Online branding is a tool to interact and communicate with existing and potential customers. The authors also present content marketing as a marketing effort available to B2B marketers, as well as the nature of digital relationships in social media. The reader learns the importance of branding the company through digital channels and those benefits that can be reached through such actions. The digital tools presented in the chapter relate to social media, for example, Twitter, Facebook, LinkedIn, YouTube, blogs, search engine optimization, and paid advertising. These tools are discussed in detail, both related to their benefits and pitfalls.

The purpose of this paper is to propose a new managerial method to integrate the environmental dimension in the domino effects (DEs) analysis.

The proposed method is a three-step approach: identification of primary hazards in the form of potential events causing the DE, a mixed quantification (deterministic and probabilistic) of the risk of the DEs with a view to its control and capitalizing the results from the BLEVESOFT software as well as those relating to the probability of occurrence of the DEs in the form of a prioritized action plan dedicated to surrounding environments (proximity territory).

The primary hazards are technologically manageable at the studied system but are unpredictable if triggered at the environmental subsystem because they are difficult to be managed and often cause panic, which is a form of a very catastrophic DE.

The research could affect members of the engineering and construction industry, and can be applied in several domains since it studies the DE phenomenon. which is a common problem especially in industrials plants.

The proposal method is applied in an industrial terminal in Algeria.

This paper presents an exploratory study of using a new managerial method that aims to combine the potentialities of geomatic sciences that allow the spatial representation of nearby territories to assess the severity of DEs through a deterministic approach, and the modeling of DEs as well as their analysis by a probabilistic approach.

The most costly part in an open-pit mine is the transportation of material out of the mine. The efficiency of the truck-and-shovel fleet plays a major role in cost control. The paper aims to discuss these issues.

The truck dispatching simulation model with consideration of age-based maintenance is proposed.

This paper underlines an impact of truck dispatching decisions and reveals remarkable differences in the total production under different approaches of operational availability. Additionally, the simulated results introduce an effective scheduled maintenance for different truck age levels.

The approach is based on a case study taking into account the stochastic equipment behavior and environment in a real open-pit mine. This approach can be used more generally in situations in which truck fleets are used to transport material.

The purpose of the paper is to define the best deployment alternatives for a public protection and disaster relief (PPDR) mobile network service – the implementation alternatives being either a dedicated network, a commercial network or a hybrid of the two network types. The selection criteria are based on the social benefits that the PPDR mobile service is expected to bring to society. The critical parameters are population density and service availability, which both directly relate to the socioeconomic benefits achieved by providing broadband (BB) mobile services in various demographic areas.

A causal loop model has been developed to define the socioeconomic benefits of the PPDR network, the parameters being population density, service availability, socioeconomic value of the service and the costs of the network. The network solution alternatives are studied using the Finnish PPDR network as a reference – analysing various areas of the country with differing population densities from remote, rural and more densely populated suburban and urban areas.

Socioeconomic value is a common measure for assessing the value of governmental investments; population density has a strong impact on the optimum deployment alternatives as the socioeconomic value is directly proportional to this variable. The flat nationwide fee of the mobile users means that the users are subsidised in sparsely populated areas – and overcharged in densely populated areas. This is the main reason why the commercial network seems to be most feasible in rural areas, whereas the dedicated network works best in urban areas. Based on the case study, the commercial network is most preferable up to the point when the population density reaches 50-125 persons/km². After that point, the dedicated network becomes more appropriate. Proposals are being made to improve the availability of the commercial networks enabling them to serve as a PPDR network: ensuring priority functionality and a protected power supply; allowing PPDR subscribers the exclusive use of one of the 700 MHz spectrum bands in restricted, critical areas; and extending use of the existing narrowband PPDR network in areas where communication availability is crucial.

On the one hand, the financing of BB PPDR mobile networks is an unresolved issue in many countries. On the other hand, the ability of commercial BB networks to provide better quality of service is improving, making viable the alternative to subscribe for radio service from a commercial operator. Therefore, the feasibility study on how to provide an optimum mobile BB service for PPDR organisations is of real value at this time.

Remote handling (RH) manipulators have been widely studied for maintenance tasks in fusion reactors. Those tasks always require heavy load, high accuracy and large work space for manipulators. Traditionally, the maintenance of fusion devices always depends on manual RH. With the development of calculating ability, the intelligent automatic maintenance makes it possible for a fusion device instead of the previous manual operation. As the flexibility of arm and the deformation of manipulator will cause problems, which are mainly inaccuracy and lower efficiency. This paper aims to study an effective way to promote the arm behavior to solve these problems.

By making use of the experimental advanced superconducting tokamak articulated maintenance arm as a platform, a series of experiments is designed to measure errors of kinematics and to collect the database of the flexible arm. Through studying the data and the arm structure, recurrent neural network (RNN) method was adopted to estimate the deformation of flexible arm and eventually compensate deformation in robot control to achieve higher accuracy.

By means of delicate RNN modeling, errors of kinematics have been reduced to a smaller order than the RH mode. This intelligent maintenance method will also reduce complexity of operations in maintenance.

This paper presents the use of an artificial intelligent algorithm to solve a nonlinear deformation problem of the flexible arm. The results demonstrate that it is efficient in dealing with this problem in fusion application. The RNN’s successful application has also shown that intelligent algorithms can be widely applied in fusion maintenance.

For the expected increase in the capacity of existing transportation systems and efficient energy utilisation, smart maintenance solutions that are supported by online and integrated condition monitoring systems are required. Industrial internet is one of the smart maintenance solutions which enables real-time acquisition and analysis of asset condition by linking intelligent devices with different stakeholders’ applications and databases. The purpose of this paper is to present some aspects of industrial internet application as required for integrating weather information and floating road condition data from vehicle mounted sensors to enhance effective and efficient winter maintenance.

The concept of real-time road condition assessment using in-vehicle sensors is demonstrated in a case study of a 3.5 km road section located in Northern Sweden. The main floating data sources were acceleration and position sensors from a smartphone positioned on the dash board of a truck. Features extracted from the acceleration signal were two road roughness estimations. To extract targeted information and knowledge, the floating data were further processed to produce time series data of the road condition using Kalman filtering. The time series data were thereafter combined with weather data to assess the condition of the road.

In the case study, examples of visualisation and analytics to support winter maintenance planning, execution and resource allocation were presented. Reasonable correlation was shown between estimated road roughness and annual road survey data to validate and prove the presented results wider applicability.

The paper describes a concept of floating data for an industrial internet application for efficient road maintenance. The resulting improvement in winter maintenance will promote dependable, safe and sustainable transportation of goods and people, especially in Northern Nordic region with harsh and sometimes unpredictable weather conditions.