Abstract
The proposed model of the neural network describes the task of planning the assembly sequence on the basis of predicting the optimal assembly time of mechanical parts. In the proposed neural approach, the k-means clustering algorithm is used. In order to find the most effective network, 10,000 network models were made using various training methods, including the steepest descent method, the conjugate gradients method, and Broyden–Fletcher–Goldfarb–Shanno algorithm. Changes to network parameters also included the following activation functions: linear, logistic, tanh, exponential, and sine. The simulation results suggest that the neural predictor would be used as a predictor for the assembly sequence planning system. This paper discusses a new modeling scheme known as artificial neural networks, taking into account selected criteria for the evaluation of assembly sequences based on data that can be automatically downloaded from CAx systems.
Highlights
Sequence Planning UsingThe technological assembly process is the final and the most important stage of the production process, which determines its labor consumption and the final production costs
The results show that the immune particle swarm algorithm can be effective and useful in solving the problem of planning the assembly sequence
The article describes a mechanical assembly time prediction system operating in a neural network, determined by the criteria: the number of tool changes, the number of assembly direction changes, or the stability of the assembly units
Summary
Sequence Planning UsingThe technological assembly process is the final and the most important stage of the production process, which determines its labor consumption and the final production costs. One of the most important problems at this level is the determination of the most advantageous sequence [1,2,3,4,5] of the assembly and components of the production cycle and the problem of assembly line balancing (ALB) in linear systems, which in principle are part of activities occurring at the production process stage. These issues are fundamentally related to the degree of process automation and to the production conditions in a given enterprise. It should be emphasized that in recent times the issues of determining the assembly sequence based on artificial intelligence methods were not very frequent, despite the rapid development of this field of knowledge and the significance of the problem [2,6,7]
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