INTEGRATED APPROACH TO GENERATION OF PRECEDENCE RELATIONS AND PRECEDENCE GRAPHS FOR ASSEMBLY SEQUENCE PLANNING

An integrated approach to generation of precedence relations and precedencegraphs for assembly sequence planning is presented, which contains more assembly flexibility. Theapproach involves two stages. Based on the assembly model, the components in the assembly can bedivided into partially constrained components and completely con-strained components in the firststage, and then geometric precedence relation for every component is generated automatically.According to the result of the first stage, the second stage determines and constructs allprecedence graphs. The algorithms of these two stages proposed are verified by two assemblyexamples.

Using genetic/simulated annealing algorithm to solve disassembly sequence planning

Disassembly sequence planning （DSP） plays a significant role in maintenance planning of the aircraft. It is used during the design stage for the analysis of maintainability of the aircraft. To solve product disassembly sequence planning problems efficiently, a product disassembly hybrid graph model, which describes the connection, non-connection and precedence relationships between the product parts, is established based on the characteristic of disassembly. Farther, the optimization model is provided to optimize disassembly sequence. And the solution methodology based on the genetic/simulated annealing algorithm with binaxy-tree algorithm is given. Finally, an example is analyzed in detail, and the result shows that the model is correct and efficient.

Disassembly Sequence Planning:A Survey

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled.They contain reusable resource that can be recycled and used to generate desired economic benefits.Therefore,performing their efficient disassembly is highly important in green manufacturing and sustainable economic development.Their typical examples are electronic appliances and electromechanical/mechanical products.This paper presents a survey on the state of the art of disassembly sequence planning.It can help new researchers or decision makers to search for the right solution for optimal disassembly planning.It reviews the disassembly theory and methods that are applied for the processing,repair,and maintenance of obsolete/discarded products.This paper discusses the recent progress of disassembly sequencing planning in four major aspects:product disassembly modeling methods,mathematical programming methods,artificial intelligence methods,and uncertainty handling.This survey should stimulate readers to be engaged in the research,development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

INTEGRATED APPROACH TO ASSEMBLY SEQUENCE PLANNING OF C0MPLEX PRODUCTS

Assembly sequence planning will be more difficult due to the increasingcomplexity of products. An integrated approach to assembly sequence planning of complex productsapplying de-composition-planning-combination strategy is presented. First, an assembly is decomposedinto a hierarchical structure using an assembly structure representation based on connectors. Then,an assembly planning system is used to generate the sequences that are locally optimal for eachleaf partition hi the structure hierarchy. By combining the local sequences systematically in abottom-up manner and choosing suitable ones from the merged sequences, the assembly sequence of eachparent structure including the whole assembly is generated. An integrated system has beencompleted. A complex product is given to illustrate the feasibility and the practicality of theapproach.

Disassembly sequence planning using component-joint graph and ant colony optimization

Disassembly sequence planning is an important step of mechanical maintenance. This article presents an integrated study about the generation and optimizing algorithm of the disassembly sequence. Mechanical products are divided into two categories of components and connectors. The article uses component-joint graph to represent assembly constraints, including the incidence constraints are represented by incidence matrix and the interference constraints are represented by interference constraints. The inspiring factor and pheromone matrix are calculated according to assembly constraints. Then the ant generates its own disassembly sequences one by one and updates the inspiring factor and pheromone matrix. After all iterations, the best disassembly sequence planning of components and connectors are given. Finally, an application instance of the disassembly sequence of the jack is presented to illustrate the validity of this method.

An Approach to Assembly Sequence Plannning Based on Hierarchical Strategy and Genetic Algorithm

Using group and subassembly cluster methods, the hierarchical structure of a product is ?generated automatically, which largely reduces the complexity of planning. Based on genetic algorithm, the optimal of assembly sequence of each structure level can be obtained by sequence-by-sequence search. As a result, a better assembly sequence of the product can be generated by combining the assembly sequences of all hierarchical structures, which provides more parallelism and flexibility for assembly operations. An industrial example is solved by this new approach.

Multi-period mine planning with multi-process routes

This paper attempts to optimize optimal capacities, block routing and mine sequencing problems in a mining system. The solution approach is based on a heuristics and the mixed integer programming (MIP). Unlike previous sequential solution approaches, the problems are herein solved at the same time. Furthermore, the proposed approach guarantees practical solutions because it considers ore material distribution within orebody. The paper has two main contributions: (a) the proposed approach generates production rates in a manner that the capacities are satisfied; (b) the proposed approach does not use pre-defined marginal cut-off grades. Thus, idle capacity problem is eliminated and different scheduling combinations are allowed. To see the performance of the approach proposed, a case study is carried out using a gold data. The schedule generated shows that the approach can determine optimal production rates, block destination and sequencing effectively.

Optimization of Wind Turbine Hub Assembly Sequence Based on Intelligent Algorithm

A method for hub assembly sequence planning oriented to the fixed position layout is proposed.An assembly relationship model was constructed to describe the relationship between hub assembly components and workstations,considering the layout of the hub assembly line and process constraints,including the assembly precedence matrix,workstation assembly capability table and criticality table of components.The evaluation mechanism for the assembly sequence was established.Values from the evaluation functions with engineering significance were used to select the optimal assembly sequence from the perspective of assembly time,assembly index and assembly path distance.In function,the criticality of components was introduced into the traditional assemblability index,comparing the multi-objective dragonfly algorithm(MODA)with non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)to complete the assembly sequence planning and assignment for workstations.The optimized results show that the presented method is feasible and efficient for solving the hub assembly sequence planning problem.

A multiple heterogeneous UAVs reconnaissance mission planning and re-planning algorithm

Reconnaissance mission planning of multiple unmanned aerial vehicles(UAVs)under an adversarial environment is a discrete combinatorial optimization problem which is proved to be a non-deterministic polynomial(NP)-complete problem.The purpose of this study is to research intelligent multiUAVs reconnaissance mission planning and online re-planning algorithm under various constraints in mission areas.For numerous targets scattered in the wide area,a reconnaissance mission planning and re-planning system is established,which includes five modules,including intelligence analysis,sub-mission area division,mission sequence planning,path smoothing,and online re-planning.The intelligence analysis module depicts the attribute of targets and the heterogeneous characteristic of UAVs and computes the number of sub-mission areas on consideration of voyage distance constraints.In the sub-mission area division module,an improved K-means clustering algorithm is designed to divide the reconnaissance mission area into several sub-mission areas,and each sub-mission is detected by the UAV loaded with various detective sensors.To control reconnaissance cost,the sampling and iteration algorithms are proposed in the mission sequence planning module,which are utilized to solve the optimal or approximately optimal reconnaissance sequence.In the path smoothing module,the Dubins curve is applied to smooth the flight path,which assure the availability of the planned path.Furthermore,an online re-planning algorithm is designed for the uncertain factor that the UAV is damaged.Finally,reconnaissance planning and re-planning experiment results show that the algorithm proposed in this paper are effective and the algorithms designed for sequence planning have a great advantage in solving efficiency and optimality.

Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of manufacturing.These technologies enable collecting,storing,and retrieving essential information from the manufacturing stage.Data collected at sites are shared with others where execution automatedly occurs.The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process.However,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern.The current research validates the information optimally to offer a minimum set of activities to complete the disassembly process.An optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in manufacturing.However,finding an optimal DSP is complex because of its combinatorial nature.The genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP problem.The converging nature at local optima is a limitation in the traditional GA.This study improvised the GA workability by integrating with the proposed priori crossover operator.An optimality function is defined to reduce disassembly effort by considering directional changes as parameters.The enhanced GA method is tested on a real-time product to evaluate the performance.The obtained results reveal that diversity control depends on the operators employed in the disassembly attributes.The proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the product.The effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

FAST GENERATION OF OPTIMAL ASSEMBLY PLANS

Existing approaches to automatic assembly planning often lead to combinatorial explo- sion. When the parts composing the assembly increase in number, computer-aided planning be- comes much slower than manual planning. Efforts to reduce the computing time by taking into ac- count various constraints and criteria to guide the search for the optimal plan requires too much input information, so as to offset the convenience of automatic assembly planning. In addition, as the planner becomes more complicated, such efforts often fail to reach the objective. This paper presents a new concep── unit , asserting that the intemal structure of an assembly is hierachical. Every disassembly operation only handles several units, no matter how many parts are involved. Furthermore, the scenario of disassembly is brought to light. It relates to only two key data──the liaison type and the assembly direction. The computational cast of this approach is roughly propor. tional to the number of parts. A planner, implementing these principlcs can generate the optimal as- sembly plans dramatically faster than the known approaches.

Multi-User Collaborative Assembly Planning over the Internet

Due to the increasing complexity of products and for the distributed product development, more closely collaborative work among designers is required. A collaborative assembly planning approach is proposed to support assembly planning in a networked environment. The working procedure is depicted and the key techniques including collaborative-planning-oriented assembly decomposition modeling, assembly assignment modeling, and sub-plans merging are addressed. By incorporating visual models at client side with assembly application models at server side, a web-based supporting environment for collaborative assembly planning has been developed using VRML and Java-EAI techniques. A case study is given to illustrate the feasibility and validity of the idea.

A Direct Toolpath Constructive Design Method for Controllable Porous Structure Configuration with a TSP-based Sequence Planning Determination

The inherent capabilities of additive manufacturing(AM)to fabricate porous lattice structures with controllable structural and functional properties have raised interest in the design methods for the production of extremely in-tricate internal geometries.Current popular methods of porous lattice structure design still follow the traditional flow,which mainly consists of computer-aided design(CAD)model construction,STereoLithography(STL)model conversion,slicing model acquisition,and toolpath configuration,which causes a loss of accuracy and manufac-turability uncertainty in AM preparation stages.Moreover,toolpath configuration relies on a knowledge-based approach summarized by expert systems.In this process,geometrical construction information is always ignored when a CAD model is created or constructed.To fully use this geometrical information,avoid accuracy loss and ensure qualified manufacturability of porous lattice structures,this paper proposes a novel toolpath-based con-structive design method to directly generate toolpath printing file of parametric and controllable porous lattice structures to facilitate model data exchange during the AM preparation stages.To optimize the laser jumping route between lattice cells,we use a hybrid travelling salesman problem(TSP)solver to determine the laser jumping points on contour scans.Four kinds of laser jumping orders are calculated and compared to select a minimal laser jumping route for sequence planning inside lattice cells.Hence,the proposed method can achieve high-precision lattice printing and avoid computational consumption in model conversion stages from a geomet-rical view.The optical metallographic images show that the shape accuracy of lattice patterns can be guaranteed.The existence of“grain boundaries”brought about by the multi-contour scanning strategy may lead to different mechanical properties.

Ant colony optimization for assembly sequence planning based on parameters optimization

As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing costs. ASP is a typical NP-complete problem that requires effective methods to find the optimal or near-optimal assembly sequence. First, multiple assembly constraints and rules are incorporated into an assembly model. The assembly constraints and rules guarantee to obtain a reasonable assembly sequence. Second, an algorithm called SOS-ACO that combines symbiotic organisms search (SOS) and ant colony optimization (ACO) is proposed to calculate the optimal or near-optimal assembly sequence. Several of the ACO parameter values are given, and the remaining ones are adaptively optimized by SOS. Thus, the complexity of ACO parameter assignment is greatly reduced. Compared with the ACO algorithm, the hybrid SOS-ACO algorithm finds optimal or near-optimal assembly sequences in fewer iterations. SOS-ACO is also robust in identifying the best assembly sequence in nearly every experiment. Lastly, the performance of SOS-ACO when the given ACO parameters are changed is analyzed through experiments. Experimental results reveal that SOS-ACO has good adaptive capability to various values of given parameters and can achieve competitive solutions.

A Discrete Bat Algorithm for Disassembly Sequence Planning

Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.

Assembly Sequence Planning for Mechanical Products

A method for assembly sequence planning is proposed in this paper. First, two methods for assembly sequence planning are compared, which are indirect method and direct method. Then, the limits of the previous assembly planning system are pointed out. On the basis of indirect method, an improved method for assembly sequence planning is put forward. This method is composed of four parts, which are assembly modeling for products, assembly sequence representing, assembly sequence planning, and evaluation and optimization. The assembly model is established by human machine interaction, and the assembly model contains components' information and the assembly relation among the components. The assembly sequence planning is based on the breaking up of the assembly model. And/or graph is used to represent assembly sequence set. Every component which satisfies the disassembly condition is recorded as a node of an and/or graph. After the disassembly sequence and/or graph is generated, heuristic algorithm AO * algorithm is used to search the disassembly sequence and/or graph, and the optimum assembly sequence planning is realized. This method is proved to be effective in a prototype system which is a sub project of a state 863/CIMS research project of China ‘Concurrent Engineering’.

Application of Assembly Modeling and Simulation Technology to a Large Size Wind-drive Generator

Challenges still remain in carrying out assembly modeling efficiently in virtual assembly （VA） fields. One of the root causes is the apparent weakness in effective description of assembly knowledge and information. The assembly modeling, disassembly modeling, assembly interference inspection, assembly sequence planning and optimization, and assembly simulation display for key techniques is studied theoretically in this paper. An example of product assembly modeling is provided to illustrate the effectiveness of the proposed approach. On the basis of re- search, using assembly simulation techniques and multimedia techniques to finish structure design in linkage design of a large size wind-drive generator. The application of the modeling method has shortened the lead time dramatically.