An Automated Assembly Process Planning System

Cheng

et al. 2021

Preprint

Current assembly process planning of complex products depends largely on the experience of technical personnel, which results in low design efficiency, low intelligence, and difficult to extract the assembly knowledge. To address these problems, this paper proposes an intelligent decision-making system for complex products assembly process planning through a comprehensively improved BP neural network with considering the assembly unit variability. Firstly, the characteristics, variations and similarities of assembly process for complex products are analyzed. A hierarchical model of product assembly process is established, which decomposes assembly process decision-making for overall structure into several units. A training set of each assembly unit is built considering the effect of assembly unit variability, based on which, an assembly process structure tree can be constructed quickly by connecting the nodes in the assembly process hierarchy model and the assembly process decision results. Secondly, to improve the efficiency and accuracy of decision-making, an ant lion optimizer (ALO)-BP neural network is proposed for complex products assembly process planning, which can optimize the selection of initial weights, thresholds, learning rate and feedback process of BP neural network automatically. Finally, with the case study of assembly process of the marine diesel engines, the proposed approach is compared with other algorithm models. The comparison results demonstrated that the proposed intelligent decision-making system is more precise than the other models.

Section: Related Previous Workmentioning

confidence: 99%

Section: Related Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Decision-making System for Complex Products Assembly Process Planning

Zhang

Cheng

et al. 2021

Preprint

“…The assembly stage is one of the most important stages in the product life cycle, consuming many resources and time. 1,2 If problems occur in the assembly process, it will add extra costs and assembly time, directly affecting the overall production cycle and product quality. As the assembly process’s information source and guiding principle, assembly design profoundly impacts on the assembly cycle length and the quality of product assembly, more evident in the assembly process of complex products.…”

Section: Introductionmentioning

confidence: 99%

A feature-based assembly information modeling method for complex products’ 3D assembly design

Zhang

Proceedings of the Institution of Mechanical Engineers, Part B:

et al. 2022

During the assembly design of complex products, the process data management is disordered and the data expression is not standardized, and depends mainly on the experience of technical personnel resulting in a lack of unified data source, low design efficiency, and low automation. This paper proposes a feature-based assembly information model to address these problem, providing standardized original design data for complex product 3D-assembly design. Taking the geometric feature of the mating faces as the study object, the overall structure of complex products and the information requirements of each assembly design stage were analyzed to construct the assembly feature information model. By focusing on the composition and expression form of information in the assembly feature information model, a geometric feature classification criterion applicable to assembly design is presented, and the matching rules of feature pairs, assembly relationship, and assembly constraint instructions are established. Based on these, assembly design information is extracted and expressed in a standardized form. As a validation, the modeling method was conducted on a marine diesel engine cylinder cap as a case study; the performance and applicability of the established rules and the extraction and expression methods were verified through the comparison with the assembly process of the cylinder cap.

“…Furthermore, the proposed control solution monitors the productivity of the workers and the cobots to balance the task assignment process. An automatic assembly planning of complex products has been considered in [19], the research provides a method which utilizes the information stored into a Computer-Aided Design (CAD) model to generate the assembly sequence, and then monitors the assembly execution in order to support the assembly joining plans. cooperative tasks scheduling is the problem of selecting the right sequence of executing a group of standing tasks.…”

mentioning

confidence: 99%

An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing

Sadik

Urban

2017

Future Internet

Abstract:There is no doubt that the rapid development in robotics technology has dramatically changed the interaction model between the Industrial Robot (IR) and the worker. As the current robotic technology has afforded very reliable means to guarantee the physical safety of the worker during a close proximity interaction with the IR. Therefore, new forms of cooperation between the robot and the worker can now be achieved. Collaborative/Cooperative robotics is the new branch of industrial robotics which empowers the idea of cooperative manufacturing. Cooperative manufacturing significantly depends on the existence of a collaborative/cooperative robot (cobot). A cobot is usually a Light-Weight Robot (LWR) which is capable of operating safely with the human co-worker in a shared work environment. This is in contrast with the conventional IR which can only operate in isolation from the worker workspace, due to the fact that the conventional IR can manipulate very heavy objects, which makes it so dangerous to operate in direct contact with the worker. There is a slight difference between the definition of collaboration and cooperation in robotics. In cooperative robotics, both the worker and the robot are performing tasks over the same product in the same shared workspace but not simultaneously. Collaborative robotics has a similar definition, except that the worker and the robot are performing a simultaneous task. Gathering the worker and the cobot in the same manufacturing workcell can provide an easy and cheap method to flexibly customize the production. Moreover, to adapt with the production demands in the real time of production, without the need to stop or to modify the production operations. There are many challenges and problems that can be addressed in the cooperative manufacturing field. However, one of the most important challenges in this field is the representation of the cooperative manufacturing environment and components. Thus, in order to accomplish the cooperative manufacturing concept, a proper approach is required to describe the shared environment between the worker and the cobot. The cooperative manufacturing shared environment includes the cobot, the co-worker, and other production components such as the product itself. Furthermore, the whole cooperative manufacturing system components need to communicate and share their knowledge, to reason and process the shared information, which eventually gives the control solution the capability of obtaining collective manufacturing decisions. Putting into consideration that the control solution should also provide a natural language which is human readable and in the same time can be understood by the machine (i.e., the cobot). Accordingly, a distributed control solution which combines an ontology-based Multi-Agent System (MAS) and a Business Rule Management System (BRMS) is proposed, in order to solve the mentioned challenges in the cooperative manufacturing, which are: manufacturing knowledge representation, sharing, and reasoning.