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Design is a time-consuming process for mechanical production. Some design structures frequently occur in different products and can be shared by multiple assembly models. Thus, identifying these structures and adding them to a design knowledge library significantly speed up the design process. Most studies addressing this issue have traditionally focused on part models and have not extended to assembly models. This paper aims to find a method for common design structure discovery in assembly models.

Computer-aided design models have a great deal of valuable information defined by different designers in the design stages, especially the assembly models, which are actually carriers of information from multiple sources. In this paper, an approach for discovering a common design structure in assembly models is proposed by comparing information from multiple sources. Assembly models are first represented as attribute connection graphs (ACGs), in which we mainly consider topological information and various attributes of parts and connections. Then, we apply a K-means clustering method based on a similarity analysis of different attributes to classify the parts and connections and transform ACGs of assemblies into type code graphs (TCGs). After this, a discovery algorithm that improves upon fast frequent subgraph mining is used to identify common design structures in assemblies.

A new method was developed for discovering common design structures in assembly models, considering the similarity of information from multiple sources and allowing some differences in the details to keep both commonalities and individualities of common design structures.

Experiments show that the proposed method is efficient and can produce a reasonable result.

This discovery method helps designers find common design structures from different assembly models and shorten the design cycle. It is an effective approach to build a knowledge library for product design that can shorten the design cycle.

The purpose of this paper is to present methods for assessing and mapping the complexity of products and their assembly. In cases of complexity of assembly it is important to consider and model at the product design stages when only data about individual parts/products and their assembly attributes are known. Assessing the complexity of assembly systems, based on the attributes of their components, is an essential step towards designing them for the least complexity.

This paper presents a mapping method between the complexity of products and their variants and complexity of the system needed to assemble them. A method has also been developed to assess and compare the complexity of assembly systems based on the characteristics of their physical components for comparison and re‐design to reduce complexity.

The complexity dependency matrix estimates the average assembly equipment complexity for a certain product based on the interactions between parts handling, insertion and assembly attributes and assembly system functions. An automobile engine piston, domestic appliance drive, car fan motor and a three‐pin electric power plug products were used to demonstrate the application of the developed methodology.

The developed methods can be used by products and assembly systems designers to identify and alleviate major sources of complexity.

The purpose of this paper is to find a method for key assembly structure identification in complex mechanical assembly. Three-dimensional model reuse plays an increasingly important role in complex product design and innovative design. Assembly model has become important resource of models reuse in enterprises, which contains certain function assembly structures. These assembly structures implicating plenty of design intent and design experience knowledge can be used to support function-structure design, modular design reuse and semantics analysis for complex product.

A method for identifying key assembly structures in assembly model is presented from the viewpoint of assembly topology and multi-source attributes. First, assembly model is represented based on complex network. Then, a two-level evaluation model is put forward to evaluate importance of parts assembled, and the key function parts in assembly can be obtained. After that, on the basis of the function parts, a heuristic algorithm upon breadth first searching is given to identify key assembly structures.

The method could be used to evaluate key function parts and identify key assembly structures in complex mechanical assembly according to the specific circumstances.

The method can not only help designers find the key assembly structure in complex mechanical assembly model, facilitate the function-structure designing and semantics analyzing, and thereby improve the efficiency of product knowledge reuse, but also assist in analyzing influence scope of key function part changing and optimization of the assembly process for complex mechanical assembly.

The paper is the first to propose a method for key assembly structure identification in complex mechanical assembly, where the key function parts can be evaluated through a two-level evaluation model, and the key assembly structures are identified automatically based on complex network.

The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.

A spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.

The proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.

The assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.

The spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

For more than ten years, the value of additive manufacturing (AM) for after-sales service logistics has been propagated. Today, however, only few applications are observed in practice. The purpose of this paper is to discuss possible reasons for this discrepancy and to develop a method to simplify the identification of economically valuable and technologically feasible business cases.

The approach is based on the analytic hierarchy process and relies on spare part information, that is easily retrievable from the company databases. This has two advantages: first, the approach can be customized toward specific company characteristics, and second, a very large number of spare parts may be assessed simultaneously. A field study is discussed in order to demonstrate and validate the approach in practice. Furthermore, sensitivity analyses are performed to evaluate the robustness of the method.

Results provide evidence that the method allows a valid prioritization of a large spare part assortment. Also, sensitivity analyses clarify the robustness of the approach and illustrate the flexibility of applying the method in practice. More than 1,000 positive business cases of AM for after-sales service logistics have been identified based on the method.

The developed method enables companies to rank spare parts according to their potential value when produced with AM. As a result, companies can evaluate the most promising spare parts first. This increases the effectiveness and efficiency of identifying business cases and thus may support the adoption of AM in after-sales service supply chains.

Over the past years, collaborative robots have been introduced as a new generation of industrial robotics working alongside humans to share the workload. These robots have the potential to enable human–robot collaboration (HRC) for flexible automation. However, the deployment of these robots in industrial environments, particularly in assembly, still comprises several challenges, of which one is skills-based tasks distribution between humans and robots. With ever-decreasing product life cycles and high-mix low volume production, the skills-based task distribution is to become a frequent activity. This paper aims to present a methodology for tasks distribution between human and robot in assembly work by complexity-based tasks classification.

The assessment method of assembly tasks is based on the physical features of the components and associated task description. The attributes that can influence assembly complexity for automation are presented. Physical experimentation with a collaborative robot and work with several industrial cases helped to formulate the presented method.

The method will differentiate the tasks with higher complexity of handling, mounting, human safety and part feeding from low-complexity tasks, thereby simplifying collaborative automation in HRC scenario. Such structured method for tasks distribution in HRC can significantly reduce deployment and changeover times.

Assembly attributes affecting HRC automation are identified. The methodology is presented for evaluating tasks for assigning to the robot and creating a work–load balance forming a human–robot work team. Finally, an assessment tool for simplified industrial deployment.

The kitting feeding policy creates kits with the parts of each product to assemble. Each kit contains elements with heterogeneous physical properties imposing heterogeneous logistic facilities and management solutions for storage and handling. The purpose of this paper is to present and apply a two-step procedure to design the part warehouse layout and to assign locations in case of kitting with high-variety part attributes. The proposed procedure aims at reducing the kitting travelled distance, shortening the picker paths, best positioning the components in the warehouse to enhance the possibility of creating kits through a single corridor access. The saturation of the warehouse and the minimization of the required storage space are also considered.

Starting from part categorization, the proposed two-step procedure, of general applicability, designs the component warehouse, sizing the corridors (Step 1) before clustering the kits in terms of part commonality and best-assigning clusters to corridors (Step 2) with the goal of reducing the travelled distance and saturating the available storage space.

A comparison model considers the traditional versus the proposed warehouse layout highlighting the potential saving in the picker travelled distance. A case study taken from the harvesting machine agricultural sector exemplifies the applicability and the practical implications of this research.

Elements of originality are the warehouse design strategy and the assignment model for parts based on their physical attributes and their occurrence in the assembly kits. Finally, the case study taken from industry, with a high number of components and part categories, adds value to the research making the proposed procedure able to address large-scale industrial problems.

This paper aims to investigate user preferences for the information systems in order to achieve user satisfaction by using conjoint analysis.

Conjoint analysis is applied for the measurement of user preferences on information systems by seven properties and three levels for each property. To apply the approach, two surveys were designed: the first survey investigates the main properties affecting the end-users' satisfaction for using the current information systems based upon literature review and expert interview in Phase I. The second survey weighed the preferences by the virtual profiles of the information systems, which represent the ideal systems.

The results show that speed-related properties are critical to achieve the end-user satisfaction. The next important property group is related to the access control and system integration between various business modules. From the measurements, the best system file should be constructed with the highly weighed levels of the key attributes.

The virtual profiles are complex for respondents to understand the attributes and the compounds of the virtual products. Moreover, the respondents were overwhelmed by the number of virtual profiles. The attributes were required to enable communication between researchers and respondents.

Information system designers can achieve user satisfaction by combining the highest utility levels of all attributes. Nonetheless, the trade-offs between attributes and their levels should be considered in order to apply the results to the business, depending on the system environments and business practices, by updating the importance of the determinants regularly.

This study applied the conjoint analysis to information management systems in order to design and maintain user satisfaction for the targeted company. This paper will provide alternative ways for the system engineers and developers of the case company by considering the critical attributes, which will affect both user performance and satisfaction.

This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers.

The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space.

The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques.

The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations.

Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.