Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios

Lu, Yuqian; Wang, Lihui

doi:10.1016/j.jmsy.2020.06.010

Cited by 296 publications

(66 citation statements)

References 51 publications

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“…In contemporary manufacturing, there is an increasing need for high-volume production with extensive personalization [27]. This requires responsive yet autonomous operations together with end-to-end integration of the various processes.…”

Section: Problem Statementmentioning

confidence: 99%

Multitrack Compressed Sensing for Faster Hyperspectral Imaging

Kubal¹,

Lee²,

Tay³

et al. 2021

Preprint

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<div>Hyperspectral imaging (HSI) provides much more information than regular optical microscopy and spectroscopy, making it valuable in areas such as biomedicine, materials inspection and food safety. However HSI is challenging because of the large amount of data that has to be acquired, and large measurement times. Compressed sensing (CS) approaches to hyperspectral imaging have been developed to address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types objects or scenes.</div><div>Here, we develop improved compressed sensing approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. This is an augmentation of single-pixel-camera-style acquisition for HSI, where a single spectrum is measured per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing, and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased, while maintaining reconstruction speed as well as accuracy.</div><div>The methods are validated computationally, via noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a ~10 times smaller measurement plus reconstruction time as compared to full sampling HSI, without compromise to reconstruction accuracies across the different sample images tested. Multitrack non-adaptive CS (sparse recovery) suffers a large reconstruction time, but is the most robust to Poisson noise.</div><div><i><b>Note: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</b></i><br></div>

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Section: Problem Statementmentioning

confidence: 99%

Multitrack Compressed Sensing for Faster Hyperspectral Imaging

Kubal¹,

Lee²,

Tay³

et al. 2021

Preprint

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“…Similarly, the bounding points of the i-th slab segment can be assigned as P (4i+7) , P (4i+8) , P (4i+9) , and P (4i+10) , where i ∈ [2, n − 1]. The coordinate values of the i-th segment are deduced and calculated by Equations (8)- (11).…”

Section: Segmentation Processmentioning

confidence: 99%

“…Li et al and Jeong et al [9,10] indicated that the prefabricated component information in the industry foundation classes (IFC) data model can be effectively extracted and classified, which could improve production management efficiency. Among the recognized research gaps, standards-compliant interoperability and integration between manufacturing processes and design model systems are of considerable importance [8,11].…”

Section: Introductionmentioning

confidence: 99%

Automated Optimization for the Production Scheduling of Prefabricated Elements Based on the Genetic Algorithm and IFC Object Segmentation

Wang

Rao

2020

Processes

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Background: With the ever-increasing availability of data and a higher level of automation and simulation, production scheduling in the factory for prefabrication can no longer be seen as an autonomous solution. Concepts such as building information modelling (BIM), graphic techniques, databases, and interface development as well as heightened emphasis on overall-process optimization topics increase the pressure to connect to and interact with interrelated tasks and procedures. Methods: The automated optimization framework detailed in this study intended to generate optimal schedule of prefabricated component production based on the manufacturing process model and genetic algorithm method. An extraction and segmentation approach based on industry foundation classes (IFC) for prefabricated component production is discussed. During this process, the position and geometric information of the prefabricated components are adjusted and output in the extracted IFC file. Then, the production process and the completion time of each process have been examined and simulated with the genetic algorithm. Lastly, the automated optimization solution can be formed by the linking production scheduling database and the computational environment. Results: This shows that the implementation of the automated optimization framework for the production scheduling of the prefabricated elements improves the operability and accuracy of the production process. Conclusions: Based on the integration technique discussed above, the data transmission and integration in the mating application program is achieved by linking the Python-based application, the Structured Query Language (SQL) database and the computational environment. The implementation of the automated optimization framework model enables BIM models to play a better foundational role in patching up the technical gaps between prefabricated building designers and element producers.

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“…The mentioned capabilities come up in most industries but with various factors and speeds, i.e., their needs, based on suppliers and installation methods. Consequently, the common ground that exists between multiple industries can help to explain and improve the capabilities of specific industries [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

Shahbazi

Byun

2021

Sensors

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Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system’s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system’s quality control approach.

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Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios

Cited by 296 publications

References 51 publications

Multitrack Compressed Sensing for Faster Hyperspectral Imaging

Multitrack Compressed Sensing for Faster Hyperspectral Imaging

Automated Optimization for the Production Scheduling of Prefabricated Elements Based on the Genetic Algorithm and IFC Object Segmentation

Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

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