Search results

Three-dimensional exploded view is a schematic representation of a product anticipated for performing assembly or disassembly operations. Exploded view is found in many applications, such as product instructional materials, repair and maintenance handbooks. This paper aims to propose an efficient exploded view generation technique based on assembly coherence data and disassembly feasibility testing, and illustrate it on various configurations of assemblies.

The proposed methodology extracts the assembly contact information between the constituent parts and geometric feasibility relation matrix based on the common mating surface of part pairs in liaison and assembly collision detection. These data are further used for exploded view generation.

The proposed exploded view generation method determines the possible disassembly sequences and simplifies the procedure in determining the number of disassembly levels.

The procedure consumes more time for the products with large number of part counts having numerous non-ruled surfaces.

The proposed method is effectively used to solve assemblies, where parts are assembled through oblique orientations. The method is found successful in generating exploded view for products with large number of parts through collision-free paths.

The purpose of this paper is to develop an efficient hybrid method that can collectively address assembly sequence generation (ASG) and exploded view generation (EVG) problem effectively. ASG is an act of finding feasible collision free movement of components of a mechanical product in accordance with the assembly design. Although the execution of ASG is complex and time-consuming in calculation, it is highly essential for efficient manufacturing process. Because of numerous limitations of the ASG algorithms, a definite method is still unavailable in the computer-aided design (CAD) software, and therefore the explosion of the product is not found to be in accordance with any feasible disassembly sequence (disassembly sequence is reverse progression of assembly sequence). The existing EVG algorithms in the CAD software result in visualization of the entire constituent parts of the product over single screen without taking into consideration the feasible order of assembly operations; thus, it becomes necessary to formulate an algorithm which effectively solves ASG and EVG problem in conjugation. This requirement has also been documented as standard in the “General Information Concerning Patents: 1.84 Standards for drawings” in the United States Patent and Trademark office (2005) which states that the exploded view created for any product should show the relationship or order of assembly of various parts that are permissible.

In this paper, a unique ASG method has been proposed and is further extended for EVG. The ASG follows a deterministic approach to avoid redundant data collection and calculation. The proposed method is effectively applied on products which require such feasible paths of disassembly other than canonical directions.

The method is capable of organizing the assembly operations as linear or parallel progression of assembly such that the assembly task is completed in minimum number of stages. This result is further taken for EVG and is found to be proven effective.

Assembly sequence planning (ASP) is performed most of the times considering the geometric feasibility along canonical axes without considering parallel possibility of assembly operations. In this paper, the proposed method is robust to address this issue. Exploded view generation considering feasible ASP is also one of the novel approaches illustrated in this paper.

This paper aims to fulfil a need to identify assembly interfaces from existing products based on their Assembly Process Planning (APP). It proposes a tool to identify assembly interfaces responsible for reused components integration. It is integrated into a design for mixed model final assembly line approach by focusing on the identification of assembly interfaces as a generic tool. It aims to answer the problem of interfaces’ identification from the APP.

A tool is developed to identify assembly interfaces responsible for reused component integration. It is based on the use of a rule-based algorithm that analyses an APP and then submits the results to prohibition lists to check their relevance. The tool is then tested using a case study. Finally, the resulting list is subjected to a visual validation step to validate whether the identified interface is a real interface.

The results of this study are a tool named ICARRE which identify assembly interfaces using three steps. The tool has been validated by a case study from the helicopter industry.

As some interfaces are not contained in the same assembly operations and therefore, may not have been identified by the rule-based algorithm. More research should be done by testing and improving the algorithm with other case studies.

The paper includes implications for new product development teams to address the difficulties of integrating reused components into different products.

This paper presents a tool for identifying interfaces when sources of knowledge do not allow the use of current methods.

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.

In robot programming by demonstration (PbD) of small parts assembly tasks, the accuracy of parts poses estimated by vision-based techniques in demonstration stage is far from enough to ensure a successful execution. This paper aims to develop an inference method to improve the accuracy of poses and assembly relations between parts by integrating visual observation with computer-aided design (CAD) model.

In this paper, the authors propose a spatial information inference method called probabilistic assembly graph with optional CAD model, shorted as PAGC*, to achieve this task. Then an assembly relation extraction method from CAD model is designed, where different assembly relation descriptions in CAD model are summarized into two fundamental relations that are colinear and coplanar. The relation similarity, distance similarity and rotation similarity are adopted as the similar part matching criterions between the CAD model and the observation. The knowledge of part in CAD is used to correct that of the corresponding part in observation. The likelihood maximization estimation is used to infer the accurate poses and assembly relations based on the probabilistic assembly graph.

In the experiments, both simulated data and real-world data are applied to evaluate the performance of the PAGC* model. The experimental results show the superiority of PAGC* in accuracy compared with assembly graph (AG) and probabilistic assembly graph without CAD model (PAG).

The paper provides a new approach to get the accurate pose of each part in demonstration stage of the robot PbD system. By integrating information from visual observation with prior knowledge from CAD model, PAGC* ensures the success in execution stage of the PbD system.

The construction industry has been criticized for not keeping up with other production industries in terms of cost efficiency, innovation, and production methods. The purpose of this paper is to contribute to the knowledge about what hampers efficiency in supplying engineer‐to‐order (ETO) joinery‐products to the construction process. The objective is to identify the main contributors to inefficiency and to define areas for innovation in improving this industry.

Case studies of the supply chain of a Swedish ETO joinery‐products supplier are carried out, and observations, semi‐structured interviews, and documents from these cases are analysed from an efficiency improvement perspective.

From a lean thinking and information modelling perspective, longer‐term procurement relations and efficient communication of information are the main areas of innovation for enhancing the efficiency of supplying ETO joinery‐products. It seems to be possible to make improvements in planning and coordination, assembly information, and spatial measuring through information modelling and spatial scanning technology. This is likely to result in an increased level of prefabrication, decreased assembly time, and increased predictability of on‐site work.

The role of supplying ETO joinery‐products is a novel research area in construction. There is a need to develop each segment of the manufacturing industry supplying construction and this paper contributes to the collective knowledge in this area. The focus is on the possibilities for innovation in the ETO joinery‐products industry and on its improved integration in the construction industry value chain in general.

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.

In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.

A human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.

The task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.

This proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.

The purpose of this paper is to discriminate fake interference caused by polygonal approximation so as to achieve accurate assembly sequence planning and assembly simulation.

An approximation zone model is proposed to formulate polygonal approximation. Fake interference is discriminated from hard interference by evaluating if polygonal models intersect within corresponding approximation zones. To reduce the computation, the surface-surface, surface-end face and end face-end face intersection test methods have been developed to evaluate the intersection and obtain collision data. An updated collision detection algorithm with this method is presented, which is implemented by a system named AutoAssem.

This method has been applied to a set of products such as a valve for assembly interference matrix generation, static and dynamic collision detection. The results show that it ensures the accuracy of assembly sequence planning and assembly simulation for polygonal models.

This method facilitates assembly design in the virtual environment with polygonal models. It can also be applied to computer aided design systems to achieve quick and accurate collision detection.

Fake interference between polygonal models may result in serious errors in assembly sequence planning and assembly simulation. Assembly zone model and novel polygon intersection verification methods have been proposed to effectively tackle this problem. Compared to current methods, this method considers valid penetration direction and approximation difference, does not need to process complicated auxiliary data and can be easily integrated with current collision detection methods.

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.