Deep multistage multi-task learning for quality prediction of multistage manufacturing systems

Yan, Hao; Sergin, Nurettin Dorukhan; Brenneman, William A.; Lange, Stephen Joseph; Ba, Shan

doi:10.1080/00224065.2021.1903822

Cited by 21 publications

(4 citation statements)

References 49 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, 2020; Jin et al. , 2021; Yan et al. , 2021), it is no surprise that big data analytics in manufacturing is a popular topic.…”

Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%

“…These data points may be of different kinds, originating from diverse functional metrics as they go from one functional process to a more sophisticated one (Weng et al, 2019;Fang et al, 2020;Lian et al, 2021). With so much data available (Jang and Nemeh, 2017;Jiang et al, 2020;Jin et al, 2021;Yan et al, 2021), it is no surprise that big data analytics in manufacturing is a popular topic.…”

Section: Knowledge Clusters (Themes) Via Keyword Coupling Analysis (Ro1)mentioning

confidence: 99%

See 1 more Smart Citation

Deep learning applications in manufacturing operations: a review of trends and ways forward

Sahoo

Kumar

Abedin

et al. 2022

JEIM

View full text Add to dashboard Cite

PurposeDeep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.Design/methodology/approachUsing bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.FindingsThis research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.Research limitations/implicationsThis research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.Originality/valueThis research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

show abstract

“…, 2020; Jin et al. , 2021; Yan et al. , 2021), it is no surprise that big data analytics in manufacturing is a popular topic.…”

Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%

Section: Knowledge Clusters (Themes) Via Keyword Coupling Analysis (Ro1)mentioning

confidence: 99%

Deep learning applications in manufacturing operations: a review of trends and ways forward

Sahoo

Kumar

Abedin

et al. 2022

JEIM

View full text Add to dashboard Cite

show abstract

“…Zhao et al (2014) performed process optimization for grinding and polishing process parameters to improve the surface quality and decrease the surface roughness of products. Yan et al (2021) proposed a deep multistage multi-task model that considers different response variables at the same time.…”

Section: Literature Reviewmentioning

confidence: 99%

An artificial intelligent manufacturing process for high-quality low-cost production

Hassan

Hamdan

Shahin

et al. 2022

IJQRM

View full text Add to dashboard Cite

PurposeTo avoid the high cost of poor quality (COPQ), there is a constant need for minimizing the formation of defects during manufacturing through defect detection and process parameters optimization. This research aims to develop, design and test a smart system that detects defects, categorizes them and uses this knowledge to enhance the quality of subsequent parts.Design/methodology/approachThe proposed system integrates data collected from the deep learning module with the machine learning module to develop and improve two regression models. One determines if set process parameters would yield a defective product while the second model optimizes them. The deep learning model utilizes final product images to categorize the part as defective or not and determines the type of defect based on image analysis. The developed framework of the system was applied to the forging process to determine its feasibility during actual manufacturing.FindingsResults reveal that implementation of such a smart process would lead to significant contributions in enhancing manufacturing processes through higher production rates of acceptable products and lower scrap rates or rework. The role of machine learning is evident due to numerous benefits which include improving the accuracy of the regression model prediction. This artificial intelligent system enhances itself by learning which process parameters could lead to a defective product and uses this knowledge to adjust the process parameters accordingly overriding any manual setting.Research limitations/implicationsThe proposed system was applied only to the forging process but could be extended to other manufacturing processes.Originality/valueThis paper studies how an artificial intelligent (AI) system can be developed and used to enhance the yield of good products.

show abstract

“…Despite the DL model having higher computational time and complexity than the machine learning approach, DL provides robust results, particularly in the big dataset. Several studies also reported that DL can be used to build a high-performance prediction model in many areas such as agriculture [13], manufacturing [14], and even behavioral science [15].…”

Section: Introductionmentioning

confidence: 99%

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

et al. 2022

View full text Add to dashboard Cite

Recently, the development of a rapid detection approach for glaucoma has been widely proposed to assist medical personnel in detecting glaucoma disease thanks to the outstanding performance of artificial intelligence. In several glaucoma detectors, cup-to-disc ratio (CDR) and disc damage likelihood scale (DDLS) play roles as the major objects that are used to analyze glaucoma. However, using CDR and DDLS is quite difficult since every person has different characteristics (shape, size, etc.) of the optic disc and optic cup. To overcome this issue, we proposed an alternative way to detect glaucoma disease by analyzing the damage to the retinal nerve fiber layer (RNFL). Our proposed method is divided into two processes: (1) the pre-treatment process and (2) the glaucoma classification process. We started the pre-treatment process by removing unnecessary parts, such as the optic disc and blood vessels. Both parts are considered for removal since they might be obstacles during the analysis process. For the classification stages, we used nine deep-learning architectures. We evaluated our proposed method in the ORIGA dataset and achieved the highest accuracy of 92.88% with an AUC of 89.34%. This result is improved by more than 15% from the previous research work. Finally, it is expected that our model could help improve eye disease diagnosis and assessment.

show abstract

Deep multistage multi-task learning for quality prediction of multistage manufacturing systems

Cited by 21 publications

References 49 publications

Deep learning applications in manufacturing operations: a review of trends and ways forward

Deep learning applications in manufacturing operations: a review of trends and ways forward

An artificial intelligent manufacturing process for high-quality low-cost production

Retinal Nerve Fiber Layer Analysis Using Deep Learning to Improve Glaucoma Detection in Eye Disease Assessment

Contact Info

Product

Resources

About