Prognostics and health management for induction machines: a comprehensive review
Induction machines (IMs) are utilized in different industrial sectors such as manufacturing, transportation, transmission, and energy due to their ruggedness, low cost, and high efficiency. If IMs fail without advanced warning, unscheduled ...
Machine learning approach to packaging compatibility testing in the new product development process
The paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in ...
Research on digital twin monitoring system for large complex surface machining
With the rapid development of aerospace, the large complex curved workpiece is widely used. However, the lack of digital monitoring and detection in the current manufacturing process leads to the low efficiency of the parts produced and processed, ...
Powder bed monitoring via digital image analysis in additive manufacturing
Due to the nature of Selective Laser Melting process, the built parts suffer from high chances of defects formation. Powders quality have a significant impact on the final attributes of SLM-manufactured items. From a processing standpoint, it is ...
Development of grinding intelligent monitoring and big data-driven decision making expert system towards high efficiency and low energy consumption: experimental approach
Grinding has been extensively applied to meet the urgent need for tight tolerance and high productivity in manufacturing industries. However, grinding parameter settings and process control still depend on skilled workers’ engineering experience. ...
Nash equilibrium as a tool for the Car Sequencing Problem 4.0
This paper introduces a new concept to solve car sequencing problem called the Car Sequencing Problem 4.0, focuses the paint shop. The problem of effective car sequencing in the paint shop is caused by the specifics of the production process ...
Monitoring and control of biological additive manufacturing using machine learning
The goal of this work is the flaw-free, industrial-scale production of biological additive manufacturing of tissue constructs (Bio-AM). In pursuit of this goal, the objectives of this work in the context of extrusion-based Bio-AM of bone tissue ...
Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network
Deep learning-based data-driven methods have been successfully developed in tool wear condition monitoring (TWCM), relying on the massive available labeled samples and the same probability distribution between training and testing data. However, ...
A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups
As an essential scheduling problem with several practical applications, the parallel machine scheduling problem (PMSP) with family setups constraints is difficult to solve and proven to be NP-hard. To this end, we present a deep reinforcement ...
Hierarchical multi-scale network for cross-scale visual defect detection
Nowadays, an increasing number of researchers apply deep-learning-based object detection methods to implement visual defect detection in industrial manufacturing. However, large-scale variation in visual defect detection impedes the improvement of ...
A novel approach for tool condition monitoring based on transfer learning of deep neural networks using time–frequency images
Traditional tool condition monitoring methods developed in an ideal environment are not universal in multiple working conditions considering different signal sources and recognition methods. This paper presents a novel tool condition monitoring ...
Industrial-oriented machine learning big data framework for temporal-spatial error prediction and control with DTSMGCN model
The thermal error reduces the machining accuracy of machine tools, and should be effectively controlled. But the accurate prediction of the thermal error is challenging because of the complex and dynamic running conditions. In previous studies, ...
Development of adaptive safety constraint by predicting trajectories of closest points between human and co-robot
Safety is a critical component for human–robot cohabitation. The control barrier function (CBF) provides an effective tool to build up the constraint and ensure the safety of human–robot interaction. However, since the human and robot keep moving ...
Attention mechanism-based deep learning for heat load prediction in blast furnace ironmaking process
Heat load prediction is essential to discover blast furnace (BF) anomalies in time and take measures in advance to reduce erosion in the ironmaking process. However, owing to the redundancy of the high dimensional data and the multi-granularity ...
Machining tool identification utilizing temporal 3D point clouds
The manufacturing domain is regarded as one of the most important engineering areas. Recently, smart manufacturing merges the use of sensors, intelligent controls, and software to manage each stage in the manufacturing lifecycle. Additionally, the ...
PreAugNet: improve data augmentation for industrial defect classification with small-scale training data
With the prevalence of deep learning and convolutional neural network (CNN), data augmentation is widely used for enriching training samples to gain model training improvement. Data augmentation is important when training samples are scarce. This ...
Method for monitoring and controlling penetration of complex groove welding based on online multi-modal data
In industrial production, there are problems of hand polishing error and the thermal deformation of weldment, resulting in unstable groove welding. In this article, we propose a model for online monitoring of the penetration of complex groove ...
A texture-aware one-stage fabric defect detection network with adaptive feature fusion and multi-task training
Fabric defect detection is an indispensable process to guarantee product quality in industrial production. With the proposal of industry 4.0, manufacturing enterprises have been endeavoring to develop automatic fabric defect detection systems to ...
Automatic detection and characterization of porosities in cross-section images of metal parts produced by binder jetting using machine learning and image augmentation
In binder jetting followed by sintering, the porosity characterization is critical to understand how the process affects the structure of the printed parts. Image-based porosity detection methods are widely used but the current solutions are ...
Comparison and explanation of data-driven modeling for weld quality prediction in resistance spot welding
- Matthew Russell,
- Joseph Kershaw,
- Yujun Xia,
- Tianle Lv,
- Yongbing Li,
- Hassan Ghassemi-Armaki,
- Blair E. Carlson,
- Peng Wang
Resistance spot welding (RSW) is an important manufacturing process across major industries due to its high production speed and ease of automation. Though conceptually straightforward, the process combines complex electrical, thermal, fluidic, ...
Root cause analysis of an out-of-control process using a logical analysis of data regression model and exponential weighted moving average
Control charts are widely used as a tool in process quality monitoring to detect anomalies and to improve the quality of a process and product. Nevertheless, their limitations have increased in the face of increasingly complex manufacturing ...
Real-time defect detection of TFT-LCD displays using a lightweight network architecture
- Ping Chen,
- Mingfang Chen,
- Sen Wang,
- Yanjin Song,
- Yu Cui,
- Zhongping Chen,
- Yongxia Zhang,
- Songlin Chen,
- Xiang Mo
The mura defects of thin film transistor-liquid crystal display (TFT-LCD) panels have low contrast and random locations, which makes it impossible for us to correctly evaluate the number and type of mura defects on the image in the field ...
Bilateral matching for collaborative remanufacturing services based on multi-attribute preferences and mutual interactions
The traditional partner selection in reverse logistics focuses on selecting recyclers for remanufacturers, but neglects the selection of remanufactures for recyclers and the mutual matching of the two parties. This is not conducive to forming a ...
DESMILS: a decision support approach for multi-item lot sizing using interactive multiobjective optimization
We propose a decision support approach, called DESMILS, to solve multi-item lot sizing problems with a large number of items by using single-item multiobjective lot sizing models. This approach for making lot sizing decisions considers multiple ...
A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data
This work presents an in-situ quality assessment and improvement technique using point cloud and AI for data processing and smart decision making in Additive Manufacturing (AM) fabrication to improve the quality and accuracy of fabricated ...
