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The purpose of this paper is to present an automated method for complicated truss structure subassembly identification.

A community-detecting algorithm is introduced and adapted to reach the target. The ratio between oriented bounding boxes of parts is used as the weight to reflect the compact degree of assembly relationships. The authors also propose a method to merge nodes together at cut-vertex in model, by which the solving process could be accelerated.

This method could identify the subassemblies of complex truss structures according to the specific requirements.

This research area is limited to truss structures. This research offers a new method in assembly sequences planning area. It could identify subassemblies in complex truss structures, with which the existing method is not adequate to deal.

This method could facilitate the complex truss structures assembly planning, lower the human errors and reduce the planning time.

The method could inspire general assembly analysis planning.

All authors of this paper confirm that this manuscript is original and has not been submitted or published elsewhere.

The purpose of this paper is to perform a comparative assessment on working of the existed subassembly identification (SI) methods, which are widely practiced during the product development stage and to propose an improved method for solving the SI problem in assembly sequence planning (ASP).

The cut-set method is found as a suitable method among various knowledge-based methods such as the theory of loops, theory of connectors and theory of clusters for the workability enhancement to meet the current requirements. Necessary product information is represented in the matrix format by replacing the traditional AND/OR graphs and the advanced predicates are included in the evaluation criteria.

The prominent methods in SI are followed a few of the predicates to avoid complexity in solution generation. The predicate consideration is found as the most influencing factor in eliminating the infeasible part combinations at SI. However, the quality of identified subassemblies without advanced predicates is not influencing the solution generation phase but practical applicability is affecting adversely.

The capability of performing SI by the cut-set method is improved to deal with the complex assembly configurations. The improved method is tested by applying on different assembly configurations and the effectiveness is compared with other existent methods of ASP along with the conventional method.

The purpose of this paper is to propose a framework to identify all the feasible disassembly sequences for a multi‐component product and to find an optimal disassembly sequence, according to specific criteria such as cost, duration, profit, etc.

Taking into account topological and geometrical constraints of a product structure, an AND/OR disassembly graph is built. Each graph node represents a feasible subassembly. Two nodes i and j are connected by an arc (i, j), called a transition, if the subassembly j can be obtained from the subassembly i by removing one or several connectors. Constraint programming approach is used to generate the feasible subassemblies and related transitions.

If a cost z_ij is incurred to perform a transition (i, j), an optimal disassembly sequence can be generated for a given subassembly, using the shortest path algorithm or a linear programming model.

The proposed approach performs very well compared to other approaches published in the literature, even when applied to products requiring parallel disassembly and including a large number of parts.

This approach has been successfully applied to assess the wheelchair maintainability at the design stage and will be implemented in CAD systems. One other application, regarding the disassembly process and total revenue maximization for product recycling, is now under consideration.

Applying constraint programming to efficiently generate the set of the feasible subassemblies constitutes the main contribution in this paper. This process is the hardest step in the disassembly sequencing problem.

Parallel assembly sequence planning (PASP) reduces the overall assembly effort and time at the product development stage. Methodological difficulties at framework development and computational issues at their implementation made the PASP complex to achieve. This paper aims to propose a novel stability concept for subassembly detection to minimize the complexities in PASP.

In this research, a heuristic method is developed to identify, represent and implement the stability predicate to perform subassembly detection and assembly sequence planning (ASP) at the further stages. Stability is organized into static, dynamic, enriched and no stability between the mating assembly parts. The combination of parts that possesses higher fitness is promoted to formulate the final solution about PASP.

The results obtained by applying the proposed concept on complex configurations revealed that stability predicate plays a dominant role in valid subassembly detection and final sequence generation further.

The value of the presented study lies in the three types of stability conditions and effective integration to existed ASP method. Unlike the existed heuristics in subassembly detection, the proposed concept identifies the parallel subassemblies during ASP.

This paper aims to develop methods for generating disassembly tasks for selective disassembly. The disassembly task contains the disassembly information, namely, disassembly direction, disassembly tool and selective disassembly sequence.

Ontology is adopted to represent the product, and ontology rules are used to represent the disassembly knowledge. A product ontology model (POM) is introduced on the basis of material, connection matrix and interference matrix. Two types of disassembly knowledge are taken into account, one is the disassembly knowledge of disassembly tool selection and the other is the disassembly knowledge of special connections. Based on the POM and the disassembly knowledge, decision support methods are designed to generate disassembly tasks.

A centrifugal pump is used to demonstrate the proposed methods, and the result shows that the methods work well.

The methods developed in this study are fundamental approaches. The ontology and the ontology rules can be extended with more disassembly knowledge.

The main contribution of this research is the development of methods for representing disassembly knowledge based on ontology rules and the decision support methods for generating disassembly tasks.

Load haul dumper (LHD) is one of the main ore transporting machineries used in underground mining industry. Reliability of LHD is very significant to achieve the expected targets of production. The performance of the equipment should be maintained at its highest level to fulfill the targets. This can be accomplished only by reducing the sudden breakdowns of component/subsystems in a complex system. The identification of defective component/subsystems can be possible by performing the downtime analysis. Hence, it is very important to develop the proper maintenance strategies for replacement or repair actions of the defective ones. Suitable maintenance management actions improve the performance of the equipment. This paper aims to discuss this issue.

Reliability analysis (renewal approach) has been used to analyze the performance of LHD machine. Allocations of best-fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K–S) test. Parametric estimation of theoretical probability distributions was made by utilizing the maximum likelihood estimate (MLE) method.

Independent and identical distribution (IID) assumption of data sets was validated through trend and serial correlation tests. On the basis of test results, the data sets are in accordance with IID assumption. Therefore, renewal process approach has been utilized for further investigation. Allocations of best-fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K–S) test. Parametric estimation of theoretical probability distributions was made by utilizing the MLE method. Reliability of each individual subsystem has been computed according to the best-fit distribution. In respect of obtained reliability results, the reliability-based preventive maintenance (PM) time schedules were calculated for the expected 90 percent reliability level.

As the reliability analysis is one of the complex techniques, it requires strategic decision making knowledge for the selection of methodology to be used. As the present case study was from a public sector company, operating under financial constraints the conclusions/findings may not be universally applicable.

The present study throws light on this equipment that need a tailored maintenance schedule, partly due to the peculiar mining conditions, under which they operate. This study mainly focuses on estimating the performance of four numbers of well-mechanized LHD systems with reliability, availability and maintainability (RAM) modeling. Based on the drawn results, reasons for performance drop of each machine were identified. Suitable recommendations were suggested for the enhancement of performance of capital intensive production equipment. As the maintenance management is only the means for performance improvement of the machinery, PM time intervals were estimated with respect to the expected rate of reliability level.

Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms.

A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated.

The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited.

The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications.

The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

Flexible automated assembly is an emerging need in several industries. The purpose of this paper is to address the introduction of an innovative concept in flexible assembly: the fully flexible assembly system (F‐FAS).

After an analysis of the state of the art, the authors describe the proposed F‐FAS, from a layout, constitutional elements, functioning principles and working cycle point of view. Second, the authors compare the traditional FAS and the manual assembly system versus the proposed F‐FAS according to their throughput and unit production costs, deriving a convenience map as a function of the number of components used in assembly and of the efficiency of the F‐FAS. Finally, using a prototype work cell developed at the Robotics Laboratory of University of Padua, the authors validate the F‐FAS concept.

Results of the research indicate that the concept of full‐flexibility can be exploited to bring automation to a domain where traditional FAS are not competitive versus manual assembly. In fact, the F‐FAS outperforms both traditional FAS and manual assembly, in terms of unit direct production costs, when the size of the batch is small, the number of components used in assembly is large and the efficiency of the F‐FAS is reasonably high. The F‐FAS prototype demonstrated the possibility of working, for certain conditions (models/components/production mix), in the F‐FAS convenience area, highlighting the achievable cost reduction versus traditional assembly systems.

The novelty of the study lies in the F‐FAS concept, its performances in terms of flexibility, compactness, throughput and unit direct production costs. A prototype work cell validated the concept and demonstrated its viability versus traditional assembly systems, thanks to convenience analysis.

This study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.

This study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.

By conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.

This paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

Autonomous robots must be able to understand long-term manipulation tasks described by humans and perform task analysis and planning based on the current environment in a variety of scenes, such as daily manipulation and industrial assembly. However, both classical task and motion planning algorithms and single data-driven learning planning methods have limitations in practicability, generalization and interpretability. The purpose of this work is to overcome the limitations of the above methods and achieve generalized and explicable long-term robot manipulation task planning.

The authors propose a planning method for long-term manipulation tasks that combines the advantages of existing methods and the prior cognition brought by the knowledge graph. This method integrates visual semantic understanding based on scene graph generation, regression planning based on deep learning and multi-level representation and updating based on a knowledge base.

The authors evaluated the capability of this method in a kitchen cooking task and tabletop arrangement task in simulation and real-world environments. Experimental results show that the proposed method has a significantly improved success rate compared with the baselines and has excellent generalization performance for new tasks.

The authors demonstrate that their method is scalable to long-term manipulation tasks with varying complexity and visibility. This advantage allows their method to perform better in new manipulation tasks. The planning method proposed in this work is meaningful for the present robot manipulation task and can be intuitive for similar high-level robot planning.