Decision support models for production ramp-up: a systematic literature review
Production ramp-up is a critical step in the life cycle of a new product, and efficiently
managing ramp-ups is a key to business success and market leadership. To support the …
managing ramp-ups is a key to business success and market leadership. To support the …
A review of data mining with big data towards its applications in the electronics industry
S Lv, H Kim, B Zheng, H ** - Applied Sciences, 2018 - mdpi.com
Featured Application This review not only benefits researchers to develop strong research
themes and identify gaps in the field but also helps practitioners for DM and Big Data …
themes and identify gaps in the field but also helps practitioners for DM and Big Data …
[HTML][HTML] An explainable deep-learning approach for job cycle time prediction
Deep neural networks (DNNs) have been applied to predict the cycle times of jobs in
manufacturing accurately. However, the prediction mechanism of a DNN is complex and …
manufacturing accurately. However, the prediction mechanism of a DNN is complex and …
Towards realistic evaluation of industrial continual learning scenarios with an emphasis on energy consumption and computational footprint
Incremental Learning (IL) aims to develop Machine Learning (ML) models that can learn
from continuous streams of data and mitigate catastrophic forgetting. We analyse the current …
from continuous streams of data and mitigate catastrophic forgetting. We analyse the current …
A two-stage explainable artificial intelligence approach for classification-based job cycle time prediction
T Chen, YC Wang - The International Journal of Advanced Manufacturing …, 2022 - Springer
Recently, many methods based on artificial neural networks (ANNs) or deep neural
networks (DNNs) have been proposed to accurately predict the cycle time of a job. However …
networks (DNNs) have been proposed to accurately predict the cycle time of a job. However …
[HTML][HTML] A modified random forest incremental interpretation method for explaining artificial and deep neural networks in cycle time prediction
T Chen, YC Wang - Decision Analytics Journal, 2023 - Elsevier
Methods based on artificial neural network (ANN) or deep neural network (DNN)
applications have been proposed to predict job cycle time effectively. However, the …
applications have been proposed to predict job cycle time effectively. However, the …
Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time
In this paper, a novel stacked encoded cascade error feedback deep extreme learning
machine (SEC-E-DELM) network is proposed to predict order completion time (OCT) …
machine (SEC-E-DELM) network is proposed to predict order completion time (OCT) …
Tool steel material selection using PROMETHEE II method
In the recent century, tools for machining, forming, or other types of metalworking industries
have consumed several tons of steel materials. Although the earliest tool steels were plain …
have consumed several tons of steel materials. Although the earliest tool steels were plain …
Fuzzified deep neural network ensemble approach for estimating cycle time range
TCT Chen, YC Lin - Applied Soft Computing, 2022 - Elsevier
Because of the high uncertainty associated with predicting the cycle time of a job in a
complex manufacturing system, this task is a challenge for production planners. To replace …
complex manufacturing system, this task is a challenge for production planners. To replace …
Applications of XAI for forecasting in the manufacturing domain
TCT Chen - … Intelligence (XAI) in Manufacturing: Methodology, Tools …, 2023 - Springer
This chapter focuses on forecasting, which is an important function of manufacturing
systems. Many operations and production activities such as cycle time forecasting, sales …
systems. Many operations and production activities such as cycle time forecasting, sales …