Disassembly is a critical step in the remanufacturing of end-of-life products. High labor costs and the limited ability of robots to perform intricate disassembly tasks have led to the increasing use of human‒robot collaboration (HRC) for disassembly. This paper addresses a challenge in HRC-based disassembly, i.e., the inherent human uncertainty during disassembly. The uncertainty is that the disassembly time for a task and the task sequence selection by a human during execution might differ from the pre-defined disassembly plan so that dynamic disassembly planning for subsequent tasks is necessary. Stackelberg equilibrium-enabled disassembly task assignment policies are designed to meet the above purpose efficiently and safely. The human leader's policy is to choose tasks that maximize the efficiency-related return value based on the robot's optimal response to the human's choice. As the follower, the robot selects tasks that maximize the safety-related return value for each human task choice. To identify the optimal values of the policies to ensure the safety and efficiency of the entire HRC-based disassembly process, an improved multi-agent proximal policy optimization (i-MAPPO) algorithm is designed. Finally, a case study for disassembling an electric vehicle battery is used to verify that the proposed approach can adapt to human uncertainty with a high success rate while ensuring that the disassembly time remains short and the human-robot distance remains within the safety threshold throughout the disassembly process.

Task-Parameterized Learning from Demonstrations (TP-LfD) is an effective approach for collaborative robot (cobot). It aims at generating a path of a cobot moving in a dynamic collaborative task (e.g., a pick-and-place task) adaptively with respect to knowledge learnt from demonstrated tasks. That is, the learnt knowledge from demonstrated tasks are considered task parameters, which are critical input for TP-LfD to generate a movement path of a cobot for a new dynamic task. To further enhance the adaptability of TP-LfD, in this paper, an improved TP-LfD ( i TP-LfD) approach over other developed TP-LfD approaches is presented. One of the major contributions in i TP-LfD is that a reinforcement learning based optimization algorithm is designed to eliminate irrelevant task parameters identified in demonstrations, which boosts the overall computational performance of cobot's path generation. In the end, case studies were used to validate and highlight the adaptability and robustness of the approach.

In the context of collaborative design, it is a challenge to effectively protect some IP(Intellectual Property) information while the model needs to be shared to another designer for co-design activities. In biology, genes determine the essential biological attributes of species and should be well kept. For collaborative design, the IP information of a CAD model is analogous to biological genes and can be therefore defined as Design Genes (DGs) to be protected during collaboration. Based on this concept, this paper presents a novel approach of DG based secure sharing of CAD models to support collaborative design. The approach consists of the following steps. Firstly, an innovative concept of DGs in a collaborative CAD model is defined used for representing protected knowledge in the model during collaboration. Secondly, a filter is introduced to identify the DGs of the model flexibly. An encryption algorithm is then described to encrypt the identified DGs of the model as encrypted DGs, while the other parts of the model are still geometrically valid and sharable to support collaborative design. Finally, case studies are used to validate the above steps. The innovation of the approach is the introduction of the innovative concept of DGs, development of related filter and encryption algorithm to ensure the security of critical information during collaborative design. The complexity of case studies can prove that the approach is applicable to realworld application scenarios.

An innovative Big Data enabled Intelligent Immune System (12S) has been developed to address manufacturing dynamics over life cycles to achieve sustainable manufacturing. The 12S is a novel inter-disciplinary integrated system of the artificial immune mechanism, the Wireless Sensor Network (WSN)-based Cyber Physical System (CPS), Artificial Neural Networks (ANNs) and sustainable manufacturing scheduling optimization algorithm. Abnormal energy consumption patterns of manufactured components from monitored Big Data are identified using ANNs. An intelligent immune mechanism is devised to adapt to the pattern/condition changes of machine tool systems and process dynamics. A rescheduling algorithm is triggered if abnormal manufacturing conditions are detected thereby achieving adaptive multiobjective optimization of energy consumption and manufacturing performance. Computer Numerical Controlled (CNC) machining processes have been used for system validation via industrial deployment into multiple machine lines in manufacturing factories. Around 30% in energy saving and over 50% in productivity improvement have been achieved by adopting 12S.

In a Cloud-enabled collaborative environment, how to ensure that the shared sensitive information of CAD models is effectively protected is one of the key challenges. In this paper, a parametric encryption method for CAD models in a Cloud manufacturing environment is presented. The method contains a Self-adaptive Encryption Algorithm and an Entire Encryption Algorithm. Based on the Self-adaptive Encryption Algorithm, the sensitive part of a CAD model is encrypted parametrically and adaptively, guaranteeing the security and validity of accessing the different parts of the CAD model when it being shared by its co-designer. Based on the Entire Encryption Algorithm, the whole CAD model is encrypted entirely in a large scale to prevent potential information leakage to unauthorized users when the model is uploaded to the Cloud.

As the popularization of 3D print technology, in the future cloud manufacturing environment, it makes the customization of personalized product with low cost to be possible. The design in the customization of personalized product is particularly important. And the key issues are the expression and understanding of design intentions from designers with different backgrounds in collaborative CAD which is main design tool for industrial products. An operation-effects merging for collaborative design method of personalized product is presented, which treats the operation effects reflecting the design intention as the research object, and merges the operation effects based on the geometric semantics to support the real-time collaborative product design freely. The advantage of the proposed method is that, the design intentions are expressed without any delay in the design which can improve the design efficiency and product quality.

Waste Electrical and Electronic Equipment (WEEE) are one of the fastest growing waste streams in the EU. It is expected to grow to more than 12 million tons by 2020. Moreover, WEEE is a complex mixture of materials and components which include hazardous and high-value materials [1]. If it is not properly managed WEEE can cause major environmental problems and huge waste of material resources. In this paper, a systematic end-of-life (EoL) management approach has been developed to handle WEEE with the environment and economic considerations. The developed approach is capable to find out the optimal selective disassembly sequence and the best EoL Type options for each component with a maximum profit and meet the requirements of the WEEE and Restriction of Hazardous Substances (RoHS) Directives. It also can help designers make optimal decisions while designing a product considering EoL recycling rate and profit. An industrial case study on Liquid Crystal Display Televisions (LCD-TVs) of the type LC24F4 is performed to validate the developed approach.

In recent years, with significantly increasing pressures from both energy price and the scarcity of energy resources have dramatically raised sustainability awareness in the industrial sector where the effective energy efficient process planning and scheduling are urgently demanded. To response this trend, development of a low cost, high accuracy, great flexibility and distributed real time energy monitoring platform is imperative. This paper presents the design, implementation, and testing of a remote energy monitoring system to support energy efficient sustainable manufacturing in an industrial workshop based on a hierarchical network architecture by integrating WSNs with Internet communication into a knowledge and information services platform. In order to verify the feasibility and effectiveness of the proposed system, the system has been implemented in a real shop floor to evaluate with various production processes. The assessing results showed that the proposed system has significance in practice of discovering energy relationships between various manufacturing processes which can be used to support for machining scheme selection, energy saving discovery and energy quota allocation in a shop floor.

Waste Electrical and Electronic Equipment (WEEE) are one of the most significant waste products in modern societies, and disassembly is a critical step to reduce WEEE. In the past two decades, despite there are many research articles published for disassembly research of WEEE, those researches mainly focus on searching optimised disassembly sequences according to different considerations of stakeholders, while another important problem, i.e., the generation of solution space has received little attentions. However, before applying disassembly planning and optimisation techniques in real industrial cases such as Liquid Crystal Display (LCD) televisions, it cannot evade the issue of the solution space generation. In this paper, an effective approach was developed to generate the solution space for LCD televisions. Firstly, six space interference matrices are generated based on a CAD model, which can be used to represent the space relationship of each components in six directions in a Cartesian coordinate system. A matrix analysis algorithm is then developed to obtain all feasible disassembly sequences by analysing the obtained six space interference matrices in a 3D environment. The result can be used as a solution space to support a disassembly planning method to achieve better economic value and environmental protection requirements within an acceptable runtime. Finally, an industrial case on Changhong LCD television of the type LC24F4 is used to verify and demonstrate the performance of the developed research.

Model security for collaborative product design in a networked environment (or called networked manufacture, grid manufacture, and cloud manufacture) is an important and also challenging research issue. In order to support collaborative product design in a secure and flexible means, a multi-granularity partial encryption method has been proposed in this paper. Base on the above method, parts of a Computer Aided Design (CAD) model can be selected flexibly by users for encrypting with multi-granularity, according to different users' requirements. The secret keys for the different parts of the CAD model can be customized to meet the requirements of users. Case studies have been developed to demonstrate the effectiveness of the proposed method.

With the enhancement of people's environmental awareness, low-carbon and energy-efficiency in manufacturing industry has been drawing much attention due to the huge consumption of raw materials and energy during machining processes. And it is a complex problem to evaluate part machining processes from the view point of carbon emission because there are many factors involved in it, such as machine tools, raw materials, energy, workshop, and so on. For solving this problem, a web-based architecture is established which mainly contains six parts, i.e., process system, process flow, data acquisition network, data transmission network, calculation of carbon emission and optimization of machine energy consumption. By introducing network technology into low-carbon manufacturing, the architecture forms a closed-loop low-carbon manufacturing system which can not only calculate the carbon emission of machining processes, but also reduce the carbon emission of a workshop with time by means of optimizing the machine energy consumption. What's more, three key enabling technologies are described in detail to make sure the evaluation architecture runs smoothly, that is, calculation of the carbon footprint, low-carbon data processing and prediction of part energy consumption.

The environmental impact of manufacturing processes is closely associated with energy consumption. Decreasing energy consumption of manufacturing processes can significantly improve the environmental performance of manufacturing to obtain sustainability. However, in most manufacturing process planning and scheduling methods, little attention has been paid to the energy consumption. In this paper, an energy consumption model and a honey-bee mating optimization (HBMO) approach have been developed to take the energy consumption into account as an objective to facilitate sustainable manufacturing process planning and scheduling. In the approach, a honey-bee mating process is simulated to achieve the optimization process where makespan and energy consumption are used as the performance criteria. A case study of process planning and scheduling is given. The experimental results are promising and compare well with the final results of the genetic algorithm based optimization approach.

This paper presents a Pulse Width Modulation (PWM) based method for Active Power (AP) and Reactive Power (RP) measurements in electric sinusoidal and Non-sinusoidal power systems. The method takes advantage of the fact that frequencies present in a power line are of a specific fundamental frequency range (a range centred around the 50Hz or 60Hz) and that in case of the presence of harmonics the frequencies of those dominating in the power line spectrum can be specified on the basis of the fundamental. In order to estimate the power components in a generalised power line, in the first stage the elimination of any harmonics present and the identification of the power line fundamental frequency are cascaded by the application, in the next step, of a PWM stage at which a Pulse Wave is generated based on the frequency of the fundamental. In the final stage of the method the power components are calculated. In the major advantages of the proposed method are included: a) in contrast to a number of existing methods a time delay or shifting of the input signal is not required, b) the method does not involve a time delay by ?/2 of the Current signal with respect to the Voltage signal, c) the fundamental frequency of the power line is considered as an unknown parameter, simultaneously estimated with the signal's spectrum using the Fast Fourier Technique (FFT) and so the method is insensitive to fundamental power line frequency changes, and d) complex computations that include multiplication of the Voltage and Current sample values by trigonometric functions are avoided.

Sustainability has become a critical driving force shaping the future of Waste Electrical and Electronic Equipment (WEEE) management. In this research, lifecycle information and flow management has been investigated to enable transition from the current “management authority-centric reporting model for WEEE” to a new “globally sustainable management model for WEEE”. In order to achieve the target, case studies on LCD TV WEEE have been conducted to understand supply chain information flows and recovery & remanufacturing processes. Based on that, information/flow framework design for WEEE management has been explored.

Designing an effective and efficient intelligent interactive system for energy conservation in domestic environment requires considerable deliberations on tradeoffs of meeting the criteria derived from human-machine interaction and reasoning mechanism, so the system can provide the users with right information based on right decisions at right time. In this paper, we report a mixed initiative intelligent system that employs multi-modal sensor system, context awareness model, semantics, and service-oriented architecture to provide real-time energy consumption information and recommendations for positive behaviour change on households' energy consumption. The system adopts concept of On-Line Analytical Process (OLAP) to summarise large volume of data generated from energy consumption monitoring sensors into a set of meaningful information for the intelligent system to reason. Service-Oriented Architecture (SOA) is used to model hardware and software components, so they can be composed flexibly to meet the user and system requests. The system is currently deployed in around 250 households across Europe for evaluation. Initial households' feedbacks are encouraging.

A slicing algorithm has been developed for directly slicing a CAD (Computer Aided Design) model as RP layers, and based on them closed NURBS (Non-Uniform Rational B-Spline) mathematical curves are introduced to represent the contour of each sliced layer. A tool-path algorithm has been then developed to generate mixed tool-paths with contour offset tool-paths for the boundary of a sliced layer, and zigzag tool-paths for the internal area of the layer. In addition, optimization strategies has been developed to identify the best build degree of zigzag tool-path with the minimum build time and to generate adaptive speeds for contour tool-paths for further reducing the build time. In the end, a human ear model has been used to demonstrate the performance of this research in terms of processing efficiency and surface accuracy.

Flexible job shop scheduling problem (FJSP) is a very important problem in the modern manufacturing system. It is an extension of the classical job shop scheduling problem. It allows an operation to be processed by any machine from a given set. It is also a NP-hard problem. This paper proposes a multi-swarm collaborative evolutionary algorithm (MSCEA) to solve FJSP. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of FJSP.

This study develops a flexible remanufacturing design for green luggage chain to consider reuse and redesign by an adaptive inventory model. This research provides several novel design policies for their remanufacturing operations to increase chain response flexibility. This paper also presents a new methodology for improving the efficiency and generality of genetic algorithms in green design applications. The methodology provides the novel functions of adaptive parameter adjustment during each evolution generation of GAs. The contributions of this algorithm are essentially from the three features: (1) the crossover rate optimization; (2) the mutation rate optimization; (3) the new green design policies' optimization. The research was successfully applied for green luggage chains to produce optimal alternative solutions for chain designers.

This paper addresses a need to increase competitiveness in design chains. Designers are focusing their efforts in the reduction of a number of factors: product development time, engineering change costs, and pilot run time. The approach in this study provides flexibility of New Product Design for Small Series Production. The work focuses on the use postponement design to enable quick response and cost effectiveness. The field studied is luggage design. An evaluation procedure is proposed which will adjust component specifications to increase their degree of availability and substitution. Practical simulations are applied in a luggage design center. The contribution of this work is to shorten the lead-time of design operations and to thereby reduce costs and increase competitiveness.

Because of the complementarity of process planning and scheduling and the multi objectives requirement from the real-world production, the research of this paper focuses on the multi-objective collaborative process planning and scheduling problem. In this paper, a game theory based genetic algorithm has been proposed to solve this problem. The game theory is used to deal with the multi objectives. And a genetic algorithm is used to identify optimal solutions efficiently from the vast search space. The experimental results show that the developed approach has achieved satisfactory improvement.

Traditionally, process planning and scheduling were performed sequentially, where scheduling was done after process plans had been generated. Considering the fact that the two functions are usually complementary, it is necessary to integrate them more tightly so that the performance of a manufacturing system can be improved greatly. In this paper, a Multi-agent-based approach has been developed to facilitate the integration of the two functions. In the approach, the two functions are carried out simultaneously, and an optimization agent based on an evolutionary algorithm is used to manage the interactions and communications between agents to enable proper decisions to be made. To verify the feasibility and performance of the proposed approach, an experimental study has been conducted and comparisons have been made between this approach and some previous works. The experimental results show the proposed approach has achieved significant improvement.

One of the significant trends in manufacturing planning is to make computer automated process planning and scheduling to work more cooperatively. To build up cooperative process planning and scheduling (CPPS), in this research, three game theory- based strategies, i.e., Pareto strategy, Nash strategy and Stackelberg strategy, have been introduced to analyze the cooperation of the two functions in a systematic way. Modern heuristic algorithms, including particle swarm optimization (PSO), simulated annealing (SA) and genetic algorithm (GA), have been developed and applied to the CPPS problem to identify targeted solutions efficiently from the vast search space caused by the complexity of the problem. Meanwhile, adaptive strategies have been developed to accommodate dynamic changes in job shops.

To support a collaborative environment and strengthen information management for the entire product lifecycles management (PLM) of products, there are increasing demands on developing new-generation product representations. The representations need to be of software platform independence, rapid sharing of information between geographically distributed applications and users, support of the entire product life cycle, and protection of commercial security of products. Conventional product representations, e.g., geometric or feature-based computer-aided design (CAD) models, are unable to meet such requirements. The extensible markup language (XML) offers several advantages, such as platform/application-independency, position-independency and machine-interpretability, so that it has the great potential to be incorporated with conventional product representations to form new-generation exchanging formats. This paper presents a survey of the XML-based product representations to support PLM.

Recently, research and development is being actively conducted to develop light-weight representation schemes for product models and visualization-based systems to support collaborative product development. In this paper, several mainstream light-weight representation schemes and visualization-based systems are surveyed. Meanwhile, technologies to integrate visualization-based systems with other functional systems to establish collaborative product development environments are discussed