Scientometric analysis and systematic review of smart manufacturing technologies applied to the 3D printing polymer material extrusion system
As the 3D printing polymer material extrusion process is moving beyond niche markets and into large-scale manufacturing, still commercial systems employed by this process work in an open-loop environment where no feedback or control solution is ...
Particle swarm optimization service composition algorithm based on prior knowledge
In order to quickly find an appropriate composition of services that meet the individual user’s requirements in the Internet big data, this paper proposes an improved particle swarm service composition method based on prior knowledge. This method ...
Online porosity prediction in laser welding of aluminum alloys based on a multi-fidelity deep learning framework
Pore is one kind of the typical defects in aluminum alloys laser welding. Porosity is an important indicator for evaluating welding quality, and porosity assessment has attracted increasing attention. This paper presents a multi-fidelity deep ...
Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling
With the development of Internet of manufacturing things, decentralized scheduling in flexible job shop is arousing great attention. To deal with the challenges confronted by personalized manufacturing, such as high level of flexibility, agility ...
Surface defect detection method for air rudder based on positive samples
In actual industrial applications, the defect detection performance of deep learning models mainly depends on the size and quality of training samples. However, defective samples are difficult to obtain, which greatly limits the application of ...
Material removal rate prediction in chemical mechanical planarization with conditional probabilistic autoencoder and stacking ensemble learning
Chemical mechanical planarization (CMP) is a complex and high-accuracy polishing process that creates a smooth and planar material surface. One of the key challenges of CMP is to predict the material removal rate (MRR) accurately. With the ...
In-process prediction of weld penetration depth using machine learning-based molten pool extraction technique in tungsten arc welding
Even though arc welding is widely utilized to join metallic parts with high reliability, the prediction and control of welding quality is challenging owing to difficulties in the prediction of weld penetration depth and the backside bead. In this ...
A digital twin-based framework for selection of grinding conditions towards improved productivity and part quality
Determining grinding conditions to achieve part quality and production rate requirements is a challenging task. Due to the complexity of the process and many affecting factors, grinding conditions are chosen conservatively, mostly based on ...
A semantic-driven tradespace framework to accelerate aircraft manufacturing system design
- Xiaochen Zheng,
- Xiaodu Hu,
- Rebeca Arista,
- Jinzhi Lu,
- Jyri Sorvari,
- Joachim Lentes,
- Fernando Ubis,
- Dimitris Kiritsis
During the design phase of an aircraft manufacturing system, different industrial scenarios need to be evaluated according to key performance indicators to achieve the optimal system performance. It is a highly complex process involving ...
A spatio-temporal fault diagnosis method based on STF-DBN for reciprocating compressor
Reciprocating compressor is the core equipment of petrochemical industry and its stable running is very important for productions in the offshore drilling platform. The reason why it is difficult to extract features from vibration signals to ...
A normal weld recognition method for time-of-flight diffraction detection based on generative adversarial network
Time-of-flight diffraction (TOFD) has become a widely used nondestructive testing (NDT) technique, owing to its wide coverage, fast detection speeds, and high defect detection rates. However, compared with nondestructive radiographic testing ...
Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0
Industry 4.0 focuses on the realization of smart manufacturing based on cyber-physical systems (CPS). However, emerging Industry 5.0 and Society 5.0 reaches beyond CPS and covers the entire value chain of manufacturing, and faces economic, ...
Data-driven cymbal bronze alloy identification via evolutionary machine learning with automatic feature selection
This paper aims to implement four machine learning models using Differential Evolution to tune internal parameters and for feature selection in a problem involving the classification of drum cymbals according to their bronze alloys via their ...
Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study
Wafer probe test plays a crucial role to distinguish the good dies from the remaining defected dies on the wafers via the probe card as the testing signal interface between the tester and the integrated circuits on the fabricated wafers. ...
Digital modeling-driven chatter suppression for thin-walled part manufacturing
Thin-walled parts are widely used in various industries such as aerospace and automotive, but the manufacturing processes are often harmed by chatter which is a self-excited vibration because of the poor rigidity in the direction perpendicular to ...
Intelligent inventory management with autonomation and service strategy
The manufacturer’s service to the customer is one of the critical factors in maximizing profit. This study proposes the innovative (Q, r) inventory policy integrated with autonomated inspection and service strategy for service-dependent demand. ...
A quality improvement method for complex component fine manufacturing based on terminal laser beam deflection compensation
The multi-axis laser manufacturing equipment is a piece of standard equipment suitable for complex components with hard and brittle material. The laser beam direction inevitably deviates from the coordinate axis of equipment due to the location of ...
Interpolation-based virtual sample generation for surface roughness prediction
Surface roughness is an essential technical indicator for the surface quality of machined parts and significantly affects the service performance of the products. Accurate prediction of the surface roughness in the machining process can play an ...
Dynamic spatial–temporal graph-driven machine remaining useful life prediction method using graph data augmentation
It is beneficial to maintain the normal operation of machines by conducting remaining useful life (RUL) prediction. Recently, graph data-driven machine RUL prediction methods have made a great success, since graph can model spatial and temporal ...
Cross-scale fusion and domain adversarial network for generalizable rail surface defect segmentation on unseen datasets
Surface quality control is a crucial part of rail manufacturing. Deep neural networks have shown impressive accuracy in rail surface defect segmentation under the assumption that the test images have the same distribution as the training images. ...
In-situ prediction of machining errors of thin-walled parts: an engineering knowledge based sparse Bayesian learning approach
Thin-walled parts such as blades are widely used in aerospace field, and their machining quality directly affects the service performance of core components. Due to obvious time-varying nonlinear effect and complex machining system, it is a great ...
A novel automatic classification approach for micro-flaws on the large-aperture optics surface based on multi-light source fusion and integrated deep learning architecture
Micron-level flaws on component surface can seriously affect the optical and mechanical properties of optics, so it is significant to accurately classify and repair surface flaws. However, the small scale, multiple categories, and complex ...
Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing
For an increasing number of applications, the quality and the stability of manufacturing processes can be determined via image and video-image data analysis and new techniques are required to extract and synthesize the relevant information content ...
Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model
The demand for industrial development toward advanced and precision manufacturing has sparked interest in ultrafast laser-based micromachining methods, particularly emerging advanced machining methods, such as laser-induced plasma micromachining (...
