Multilayer Perceptron
Assembly Line
Using AI to Detect Faulty Crimps
Crimp force monitoring (CFM) has long been the standard for fault detection in wire assemblies. The technique can reliably detect many defects, including wrong strip length, missing strands, wrong wire cross section, wrong terminal, inconsistent terminal material, insulation in the crimp, wrong insertion depth, and wrong crimp height.
We propose a fault detection system that employs AI with regional selective data scaling (RSDS). RSDS generates synthetic abnormal data from reference data by performing upscaling or downscaling on specific regions of the data. This allows the fault detection system to efficiently train an AI model with a dataset comprised exclusively of normal operational data and still achieve high accuracy in detecting faults.
In this study, a multilayer perceptron (MLP) classification model was trained exclusively on normal data and was able to effectively distinguish between normal and abnormal conditions. To validate the system, 15 unique raw datasets from a real-world wire harness manufacturing facility were collected and tested with four anomaly detection algorithms: Isolation Forest, one-class autoencoders, k-means, and Histogram-Based Outlier Score (HBOS).
The Multi Crane Scheduling Problem: A Comparison Between Genetic Algorithms and Neural Network approaches based on Simulation Modeling
The internal logistics for warehouses of many industrial applications, based on the movement of heavy goods, is commonly solved by the installment of a multi-crane system. The job scheduling of a multi-crane is an interesting problem of optimization, solved in many ways in the past: this paper describes a comparison between the optimization by the use of Genetic Algorithms and the machine learning piloting driven by Neural Networks. A case-study for steel coil production is proposed as a test frame for two different simulation software tools, one based on heuristic solution and one on machine learning; performances and data achieved from reviews and simulations are compared.
Cooperation between Control Technology and AI Technology to Improve Plant Operation
As the manufacturing industry is shifting its production model from mass production to the production of multiple products in small or variable quantities, more sophisticated operation of production equipment is required. Yokogawa has a unique approach to this problem, which was adopted by the New Energy and Industrial Technology Development Organization (NEDO). This paper describes details of this NEDO project and its achievements, as well as a study on the effective use of AI technology, which is another theme of this project.
In the NEDO project, to create this time-series model, we used effective nonlinear methods: multilayer perceptron (MLP), BiLSTM, and QRNN. As a result, we obtained correlation coefficients greater than 0.7 in the model. To verify whether this time-series model can reproduce the behavior of the target process, we evaluated its accuracy index. In addition, we used the model to solve the optimization problem and automatically calculate the optimal control parameters (PID values).