Knowledge extract and ontology construction method of assembly process text

The actual product assembly process mainly relies on manual assembly by workers, and the personal experience of workers is difficult to effectively reuse. Ontology as a knowledge management and expression tool is gradually applied in the field of assembly. However, the manual construction of the ontology is time-consuming and labor-intensive, and the automatic construction of the ontology requires a large number of corpora for training, both of which are difficult to obtain a good assembly case ontology. This paper proposes a method in which automatically extracts relevant knowledge from case assembly process files to generates case database and integrates ontology framework of assembly domain to construct ontology. It shows that the accuracy can be guaranteed on the basis of the rapid construction of case ontology. The feasibility of this method is proved by a practical case.

From solo to duet, intersections of natural product assembly with self-resistance

It has become a ‘received wisdom’ that there are universal links between natural product (NP) self-resistance and biosynthesis, which needs interpretation. This review highlights evidence of intersection between NP self-resistance and biosynthesis.

Visualizing Standardized Model-based Design and Inspection Data in Augmented Reality

Augmented reality (AR) has already helped manufacturers realize value across a variety of domains, including assistance in maintenance, process monitoring, and product assembly. However, coordinating traditional engineering data representations into AR systems without loss of context and information remains a challenge. A major barrier is the lack of interoperability between manufacturing-specific data models and AR-capable data representations. In response, we present a pipeline for porting standards-based design and inspection data into an AR scene. As a result, product manufacturing information with three-dimensional (3D) model data and corresponding inspection results are successfully overlaid onto a physical part. We demonstrate our pipeline by interacting with annotated parts while continuously tracking their pose and orientation. We then validate the pipeline by testing against six fully toleranced design models, accompanied by idealized inspection results. Our work (1) pro-vides insight on how to address fundamental issues related to interoperability between domain-specific models and AR systems and (2) establishes an open software pipeline from which others can implement and further develop.

Knowledge creation and application of optimal tolerance distribution method for aircraft product assembly

Purpose This paper aims to illustrate the tolerance optimization method based on the assembly accuracy constrain, precession constrain and the cost of production of the assembly product. Design/methodology/approach A tolerance optimization method is an excellent way to perform product assembly performance. The tolerance optimization method is adapted to the process analysis of the hatch and skin of an aircraft. In this paper, the tolerance optimization techniques are applied to the tolerance allocation for step difference analysis (example: step difference between aircraft cabin door and fuselage outer skin). First, a mathematical model is described to understand the relationship between manufacturing cost and tolerance cost. Second, the penalty function method is applied to form a new equation for tolerance optimization. Finally, MATLAB software is used to calculate 170 loops iteration to understand the efficiency of the new equation for tolerance optimization. Findings The tolerance optimization method is based on the assembly accuracy constrain, machinery constrain and the cost of production of the assembly product. The main finding of this paper is the lowest assembly and lowest production costs that met the product tolerance specification. Research limitations/implications This paper illustrated an efficient method of tolerance allocation for products assembly. After 170 loops iterations, it founds that the results very close to the original required tolerance. But it can easily say that the different number of loops iterations may have a different result. But optimization result must be approximate to the original tolerance requirements. Practical implications It is evident from Table 4 that the tolerance of the closed loop is 1.3999 after the tolerance distribution is completed, which is less than and very close to the original tolerance of 1.40; the machining precision constraint of the outer skin of the cabin door and the fuselage is satisfied, and the assembly precision constraint of the closed loop is satisfied. Originality/value The research may support further research studies to minimize cost tolerance allocation using tolerance cost optimization techniques, which must meet the given constrain accuracy for assembly products.

The AAPF fault-tolerant method for small and complex product assembly

Fault-tolerant control should be considered during assembly to ensure stability and efficiency of the assembly process. The paper proposes a fault-tolerant method to improve stability and efficiency during the assembly of small and complex products. The fault-tolerant method model was initially constructed, then an adaptive artificial potential field control algorithm (AAPF) was introduced to control related assembly tasks based on changes in assembly information. Next, active and passive fault tolerance methods were integrated using a least squares support vector machine (LS-SVM). Finally, the assembly of a 2P circuit breaker controller assembly with leakage protection was used as an example to verify the proposed assembly method. The experimental results demonstrated that the AAPF fault-tolerant method showed promising fault-tolerance capabilities for the assembly of small and complex products. Not only could it coordinate the number of tasks for each assembly robot, but it also effectively reduced the number of tasks that accumulated due to faults. The method proposed in this paper could effectively guarantee assembly stability and efficiency during small and complex product assembly.

Research on Complex Product Parts Matching by using Improved Taguchi Method

With the development of intelligent manufacturing, the key strategic of complex equipment is becoming more and more obvious. How to realize the assembly of complex products has become the focus of intelligent manufacturing. This paper puts forward the improved Taguchi method to dimension chains measures, by using different quality loss function to different dimension chains, the cores are the Nominal-is-best, non-core is measured with the improved Smaller-is-better to improve convergence perusal and increase matching rate; General adopt Smaller-is-better to enhance assembly accuracy, reduce interference fit and assembly cost. Then the dimension chains quantitative model of complicated product assembly by using the signal-to-noise ratio and different weights is built up. The model contains modeling assumption, the objective function and the matching model. And this model is regard as the fitness function of genetic algorithm. Finally, the feasibility and efficiency of the scheme are verified by the case study.

Research on Modeling and Scheduling Methods of an Intelligent Manufacturing System Based on Deep Learning

Under the background of intelligent manufacturing, the modeling and scheduling of an intelligent manufacturing system driven by big data have attracted increasing attention from all walks of life. Deep learning can find more hidden knowledge in the process of feature extraction of the hierarchical structure and has good data adaptability in domain adaptation. From the perspective of the manufacturing system, intelligent scheduling is irreplaceable in intelligent production when the manufacturing quantity of workpieces is small or products are constantly changing. This paper expounds the outstanding advantages of deep learning in intelligent manufacturing system modeling, which provides an effective way and powerful tool for intelligent manufacturing system design, performance analysis, and running status monitoring and provides a clear direction for selecting, designing, or implementing the deep learning architecture in the field of intelligent manufacturing system modeling and scheduling. The scheduling of the intelligent manufacturing system should integrate intelligent scheduling of part processing and intelligent planning of product assembly, which is suitable for intelligent scheduling of any kind and quantity of products and resources.