Computer Vision Based Quality Control for Additive Manufacturing Parts

Nascimento, Rui; Martins, Isabel Pavão; Dutra, Thiago Assis; Moreira, Luís

doi:10.1007/s00170-022-10683-5

Cited by 22 publications

(6 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This toolkit involves the application of an unsupervised machine-learning algorithm on a moderately sized training dataset of image patches to enable effective anomaly detection and in situ optimization [ 183 ]. Moreover, computer-vision approaches can be used for quality control of parts fabricated using AM [ 184 ], as well as to predict the powder flowability in metal AM [ 185 ]. Different ML algorithms, listed in Table 6 , can be implemented at various stages of AM guided by the adaptability of the predictive techniques.…”

Section: Computational Approachesmentioning

confidence: 99%

Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys

Adak,

Sreeramagiri,

Roy

et al. 2023

Materials

View full text Add to dashboard Cite

We present a scrutiny on the state of the art and applicability of predictive methods for additive manufacturing (AM) of metals, alloys, and compositionally complex metallic materials, to provide insights from the computational models for AM process optimization. Our work emphasizes the importance of manufacturing parameters on the thermal profiles evinced during processing, and the fundamental insights offered by the models used to simulate metal AM mechanisms. We discuss the methods and assumptions necessary for an educated tradeoff between the efficacy and accuracy of the computational approaches that incorporate multi-physics required to mimic the associated fluid flow phenomena as well as the resulting microstructures. Finally, the current challenges in the existing approaches are summarized and future scopes identified.

show abstract

Section: Computational Approachesmentioning

confidence: 99%

Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys

Adak,

Sreeramagiri,

Roy

et al. 2023

Materials

View full text Add to dashboard Cite

show abstract

“…For instance, CV algorithms can be used to detect defects in products and components, such as surface cracks [22], weld defects [23], and misalignments [24]. Monitoring and analyzing sensor data from the production line can enhance quality control and defect prevention [25]. Automation of inspection tasks, such as measuring dimensions, identifying parts, and verifying assemblies, has been achieved using CV technologies [26].…”

Section: Vision Systemsmentioning

confidence: 99%

A production interface to enable legacy factories for industry 4.0

Kwok,

Gaasenbeek

2023

Eng. Res. Express

View full text Add to dashboard Cite

Due to the recent pandemic, our factory operations have experienced significant setbacks, prompting the need for factory automation to maintain productivity. However, most of our factories rely heavily on human input and oversight and cannot operate remotely. Automating our factories has revealed technological gaps that fall short of our expectations, needs, and vision. Therefore, the purpose of this paper is to bridge this gap by introducing practical methodologies and applied technology that can enhance legacy factories and their equipment. Our proposed solution is the ORiON Production Interface (OPI) unit, which can function as a smart networked edge device for virtually any machine, allowing the factory to operate efficiently. We have incorporated various computer vision algorithms into the OPI unit, enabling it to autonomously detect errors, make decentralized decisions, and control quality. Despite the concept of Industry 4.0 (I4.0) being known, many machines in use today are closed source and unable to communicate or join a network. Our research offers a viable solution to implement Industry 4.0 in existing factories, and experimental results have demonstrated various applications such as process monitoring, part positioning, and broken tool detection. Our intelligent networked system is novel and enables factories to be more innovative and responsive, ultimately leading to enhanced productivity. All manufacturing companies interested in adopting Industry 4.0 technology can benefit from it, and the OPI, being an IoT device, is also an appealing option for developers and hobbyists alike.

show abstract

“…These include automation (inspection takes place without human intervention), speed, accuracy, repeatability (the machine does not lose concentration like a human does), the possibility of inspecting many parameters (one photo can detect many types of defects), cost reduction (vision systems are flexible), and traceability and documentation (it is easy to document defects, which can make it easier to look for their causes). Examples of applications of machine vision in industrial quality control can be found in [ 3 , 4 , 5 , 6 , 7 ].…”

Section: Related Workmentioning

confidence: 99%

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Kocon,

Malesa,

Rapcewicz

2023

Sensors

View full text Add to dashboard Cite

Quality inspection in the pharmaceutical and food industry is crucial to ensure that products are safe for the customers. Among the properties that are controlled in the production process are chemical composition, the content of the active substances, and visual appearance. Although the latter may not influence the product’s properties, it lowers customers’ confidence in drugs or food and affects brand perception. The visual appearance of the consumer goods is typically inspected during the packaging process using machine vision quality inspection systems. In line with the current trends, the processing of the images is often supported with deep neural networks, which increases the accuracy of detection and classification of faults. Solutions based on AI are best suited to production lines with a limited number of formats or highly repeatable production. In the case where formats differ significantly from each other and are often being changed, a quality inspection system has to enable fast training. In this paper, we present a fast method for image anomaly detection that is used in high-speed production lines. The proposed method meets these requirements: It is easy and fast to train, even on devices with limited computing power. The inference time for each production sample is sufficient for real-time scenarios. Additionally, the ultra-lightweight algorithm can be easily adapted to different products and different market segments. In this work, we present the results of our algorithm on three different real production data gathered from food and pharmaceutical industries.

show abstract

Computer Vision Based Quality Control for Additive Manufacturing Parts

Cited by 22 publications

References 35 publications

Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys

Advances and Challenges in Predictive Modeling for Additive Manufacturing of Dissimilar Metals and Complex Alloys

A production interface to enable legacy factories for industry 4.0

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Contact Info

Product

Resources

About