Process monitoring and machine learning for defect detection in laser-based metal additive manufacturing
Over the past several decades, metal Additive Manufacturing (AM) has transitioned from a rapid prototyping method to a viable manufacturing tool. AM technologies can produce parts on-demand, repair damaged components, and provide an increased ...
Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application
Machine learning with considering data privacy-preservation and personalized models has received attentions, especially in the manufacturing field. The data often exist in the form of isolated islands and cannot be shared because of data privacy ...
Multidomain variance-learnable prototypical network for few-shot diagnosis of novel faults
A multidomain variance-learnable prototypical network (MVPN) is proposed to learn transferable knowledge from a large-scale dataset containing sufficient samples of multiple faults for few-shot diagnosis of novel faults (i.e., disjoint with fault ...
Coupling of an analytical rolling model and reinforcement learning to design pass schedules: towards properties controlled hot rolling
Rolling is a well-established forming process employed in many industrial sectors. Although highly optimized, process disruptions can still lead to undesired final mechanical properties. This paper demonstrates advances in pass schedule design ...
Power spectral density moment of having defective 3D printed plastic beams under moving load based on deep learning
3D printing and 3D printing technology are increasingly popular in today’s world. However, there have not been many studies evaluating the quality of 3D printed products in real-life applications. This manuscript proposes a parameter for ...
Flexible job shop scheduling with preventive maintenance consideration
In highly automated manufacturing systems running 24/7, preventive maintenance activities need to be executed during production times. Flexible job shops with several identical machines generally bear the potential to compensate temporary machine ...
Anticipatory analysis of AGV trajectory in a 5G network using machine learning
A new generation of Automatic Guided Vehicles (AGV) virtualises their Programmable Logic Controller (PLC) in the cloud deploying 5G-based communication infrastructures to provide ultra-fast and reliable links between the AGV and its PLC. Stopping ...
A framework and method for equipment digital twin dynamic evolution based on IExATCN
Dynamic evolution is the most typical feature of a digital twin, making it different from a traditional digital model. Dynamic evolution is also the core technology for building equipment digital twins because it ensures consistency between ...
A novel 2.5D machining feature recognition method based on ray blanking algorithm
Feature recognition (FR) is one of the main tasks involved in computer-aided design, computer aided process planning, and computer-aided manufacturing systems. Conventional FR methods have topology, voxel, and pixel as model input data, which are ...
Indirect porosity detection and root-cause identification in WAAM
Due to the complexity of the Wire-arc Additive Manufacturing (WAAM) process, it is prone to the occurrence of defects in the product. One of the most common defects is porosity, which is detrimental to the mechanical and fatigue properties of the ...
Genetic algorithm optimization based on manufacturing prediction for an efficient tolerance allocation approach
The tolerance allocation is an extremely sensitive task due to the complex effects on quality, product, and cost. Thus, tolerance allocation optimization covers design and manufacturing aspects and can help to bridge the gap between tolerance ...
Visual analytics for digital twins: a conceptual framework and case study
The new generation of intelligent manufacturing systems requires a deep integration of human-cyber-physical spaces. Visual analytics plays a critical role in effectively navigating humans through twin data, enabling them to make better decisions ...
A deep learning framework for defect prediction based on thermographic in-situ monitoring in laser powder bed fusion
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during ...
A dual-attention feature fusion network for imbalanced fault diagnosis with two-stream hybrid generated data
Deep learning-based fault diagnosis models achieve great success with sufficient balanced data, but the imbalanced dataset in real industrial scenarios will seriously affect the performance of various popular deep learning models. Data generation-...
Data-driven indirect punch wear monitoring in sheet-metal stamping processes
The wear state of the punch in sheet-metal stamping processes cannot be directly observed, necessitating the use of indirect methods to infer its condition. Past research approaches utilized a plethora of machine learning models to infer the punch ...
A fast spatio-temporal temperature predictor for vacuum assisted resin infusion molding process based on deep machine learning modeling
- Runyu Zhang,
- Yingjian Liu,
- Thomas Zheng,
- Sarah Eddin,
- Steven Nolet,
- Yi-Ling Liang,
- Shaghayegh Rezazadeh,
- Joseph Wilson,
- Hongbing Lu,
- Dong Qian
The manufacture of large wind turbine blades requires well-controlled processing conditions to prevent defect formation and thus produce high-quality composite blades. While the physics-based models provide accurate computational capabilities for ...
High-precision point cloud registration system of multi-view industrial self-similar workpiece based on super-point space guidance
The demand for 3D information in intelligent manufacturing makes complete point cloud of large workpiece increasingly important in the industrial field. However, due to the limited measurement range, the existing 3D reconstruction methods are ...
A multi-subpopulation genetic algorithm-based CNN approach for ceramic tile defects classification
Classifying and grading the product relied on human vision has caused the poor quality products and low productivity. Due to their complicated defects, most ceramic tile factories still have relied on human vision to deal with the problem. ...
Developing an explainable hybrid deep learning model in digital transformation: an empirical study
Automated inspection is an important component of digital transformation. However, most deep learning models that have been widely applied in automated inspection cannot objectively explain the results. Their resulting outcome, known as low ...
GAN-based statistical modeling with adaptive schemes for surface defect inspection of IC metal packages
Metal packaging is an alternative technology to guarantee the environmental resistance and the performance reliability of ICs. Surface defect inspection of IC metal packages is an indispensable process during manufacturing. Here, a statistical ...
Process parameter optimization for reproducible fabrication of layer porosity quality of 3D-printed tissue scaffold
- Andrew Chung Chee Law,
- Rongxuan Wang,
- Jihoon Chung,
- Ezgi Kucukdeger,
- Yang Liu,
- Ted Barron,
- Blake N. Johnson,
- Zhenyu Kong
Bioprinting, or bio-additive manufacturing, is a critical emerging field for transforming tissue engineering regenerative medicine to produce biological constructs and scaffolds in a layerwise fashion. Geometric accuracy and spatial distribution ...
Learning the manufacturing capabilities of machining and finishing processes using a deep neural network model
In this work, we present a deep neural network model to automatically learn the capabilities of discrete manufacturing processes such as machining and finishing from design and manufacturing data. By concatenating a 3D Convolutional Neural Network ...
Attention-driven transfer learning framework for dynamic model guided time domain chatter detection
Online chatter detection is crucial to ensure the quality and safety of the high-speed milling process. The rapid development of the deep learning community provides a promising tool for chatter detection. However, most previous chatter detection ...
A multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties
Optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These ...
