Design of a High Speed Optical Production Line

1976 ◽  
Vol 15 (5) ◽  
Author(s):  
James J. Bohache ◽  
Gih-Horng Chen ◽  
Duncan T. Moore
2014 ◽  
Vol 945-949 ◽  
pp. 121-126 ◽  
Author(s):  
Feng Wei Xue ◽  
Ji Ping Zhou

The conveying manipulator is an indispensable transmission system of JM31-160 automatic stamping production line, and structural characteristics of the manipulator directly affect the productivity of auto stamping production line. Using virtual prototyping technology, basing on the Multi-body dynamics theory, explored the technical line of dynamic design theory to apply on the transmission system. Reaching a conclusion the function of optimized structure is improved, and manufacturing cost brings down.


Author(s):  
KALMAN PELEG ◽  
URI BEN-HANAN

We have developed an algorithm for unsupervised adaptive classification based on a finite number of “prototype populations” with distinctly different feature distributions, each representing a typically different source population of the inspected products. Intermittently updated feature distributions, of samples collected from the currently classified products, are compared to the distributions of pre-stored prototype populations, and accordingly the system switches to the most appropriate classifier. The goal of our approach is similar to the objectives of the previously proposed “Decision Directed” adaptive classification algorithms but our solution is particularly suitable for automatic inspection and classification on a production line, when the inspected items may come from a finite number of distinctly different sources. The recognition of prototype populations as well as the classification task proper may be implemented by conventional classifiers, however neural networks (NN) are advantageous in two respects: There is no need to develop separate mathematical models for each classifier because the NN does it automatically during the training stage. The parallel structure of NN has the potential for very fast classification in real time, if implemented by dedicated parallel hardware. This is particularly important for high speed automatic sorting on a production line. The practical feasibility of the approach was demonstrated by two applied examples, wherein two prototype populations of apples are recognized and sorted by size and color derived by machine vision. Three “Boltzmann-Perceptron Networks” (BPN) were used, one to recognize the prototype populations, while switching between the other two, for optimally classifying apples into two size and color categories. It is shown that misclassifications by adaptive classification are reduced, in comparison to non-adaptive classification.


2015 ◽  
Vol 719-720 ◽  
pp. 284-290
Author(s):  
Yan Ming Song ◽  
Yang Yang

Take-up roll is a key component employed in amorphous ribbon production line. The roll should supply enough adhesion fore to take up the flying ribbon with a high speed. In this paper, a novel magnetic take-up roll (MTUR) is proposed. Optimize the structure parameters with a finite element method to obtain enough and stable magnetic adhesion force (MAF). The different simulation models are established to calculate the performances of the MTUR. A prototype of the MTUR was manufactured and tested. Experiments prove that the optimal MTUR can generate MAF to overcome the resistant force that amorphous ribbon suffering in take-up process in certain air gap range


2018 ◽  
Vol 15 (5) ◽  
pp. 172988141879787 ◽  
Author(s):  
Haojian Zhang ◽  
Tingting Su ◽  
Shaohong Wu ◽  
Jun Zheng ◽  
Yunkuan Wang

This article presents a strategy for efficient sorting path planning and trajectory optimization with multiple constraints. The proposed strategy is applicable to typical high-density sorting applications. It plans the sorting path while optimizing each trajectory. The total stroke traveled by the end effector to complete the sorting task is shortened, and the efficiency of the production line is correspondingly facilitated. Thus, this article designs bow-shaped trajectories by analyzing the work process of the production line to ensure the smooth transition of the end effector and bow-shaped trajectories are based on the starting point and the end point of each sorting action. The modified trapezoidal acceleration and deceleration algorithm is used to optimize each sorting trajectory, which ensures that each sorting action is executed quickly and stably. Simultaneously, the greedy strategy is applied to plan the sorting task, which further shortens the total stroke of the end effector and improves the efficiency of the production line. A specific cost function is also designed to improve the planning strategy to prevent the occurrence of missing materials and enhance the adaptability of the sorting system. In consideration of the efficiency of the sorting system is significantly improved by this approach, the effectiveness of the proposed strategy in this article is thus verified compared with existing ones by experiments. In addition, the impact of the conveyor speed on the sorting system is also discussed.


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