Plant Factory: A New Playground of Industrial Communication and Computing

Liu, Yu; Mousavi, Sepehr; Pang, Zhibo; Ni, Zhongjun; Karlsson, Magnus; Gong, Shaofang

doi:10.3390/s22010147

Cited by 15 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…10). Reduced waste, increased production, improved product quality, and more customer satisfaction are the objectives of Industry 4.0 technology (Liu et al, 2022). The cutter locations are fixed in an automatic CNC machine's principal axis.…”

Section: Fig 9 Analysis Of Defect By Paretomentioning

confidence: 99%

Integrating Six Sigma into an Industry 4.0 System for Enhanced Productivity: A Case Study in CNC Processes

Hien,

DUC,

Tuan

2024

Management and Production Engineering Review

View full text Add to dashboard Cite

Nowaday, many manufacturing companies are integrating Industry 4.0 technology into their operational processes, particularly those aiming to enhance production operations. However, business decision-makers must remain vigilant about potential risks associated with adopting this technology. These risks include initial financial investments for testing and system installation, managing human resources to operate the new system, and concerns regarding data security. This study proposes designing an Industry 4.0 technology system to augment machining machine operations, leveraging Internet of Things (IoT) devices to facilitate connectivity and data transmission. Additionally, it aims to improve production process monitoring through visual management techniques. The machines under study are semi-automatic and lack operational digitization or expansion capacity. Through research on integrating low-cost Industry 4.0 technology into the production process, this study has achieved an annual reduction in production costs by $9593. Moreover, the defect rate for product length dimensions has plummeted from 54.90% per month to zero defects. The study employs the DMAIC method (Define-Measure-Analysis-Improve-Control) cycle within the Six Sigma methodology to investigate and apply low-cost Industry 4.0 technology to production process enhancement. This combined approach can be customized and applied to various business process improvement models, further enhancing the operation of machining machines originally equipped with Industry 3.0 technology.

show abstract

Section: Fig 9 Analysis Of Defect By Paretomentioning

confidence: 99%

Integrating Six Sigma into an Industry 4.0 System for Enhanced Productivity: A Case Study in CNC Processes

Hien,

DUC,

Tuan

2024

Management and Production Engineering Review

View full text Add to dashboard Cite

show abstract

“…There can be grave consequences resulting from an abnormal variation in HR [15,16]. In our smart cardio watch, we have tightly containerized all models for each example of real-time data so they can be encapsulated in their own data container [17,18], allowing real-time data to be updated from the client devices as required. Due to this, the Machine Learning modules had to be rebuilt every time the watch received data from the container.…”

Section: Proposed Frameworkmentioning

confidence: 99%

An Intelligent Cardiovascular Diseases Prediction System Focused on Privacy

Kolhar¹,

Misfer²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Machine learning (ML) and cloud computing have now evolved to the point where they are able to be used effectively. Further improvement, however, is required when both of these technologies are combined to reap maximum benefits. A way of improving the system is by enabling healthcare workers to select appropriate machine learning algorithms for prediction and, secondly, by preserving the privacy of patient data so that it cannot be misused. The purpose of this paper is to combine these promising technologies to maintain the privacy of patient data during the disease prediction process. Treatment of heart failure may be improved and expedited with this framework. We used the following machine learning algorithms to make predictions: Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbors (KNN), Decision Tree (DT) and Support Vector Machines (SVM). These techniques, combined with cloud computing services, improved the process of deciding whether to treat a patient with cardiac disease. Using our classifiers, we classified cardiac patients according to their features, which are grouped into single features, combinations of selected features, and all features. In experiments using all clinical features, machine learning classifiers SVM, DT, and KNN outperformed the rest, whereas in experiments using minimal clinical features, SVM and KNN were the most accurate. Internet of Things (IoT) devices allow family physicians to share diagnostic reports on the cloud in a secure manner. Ring signatures are particularly useful for verifying the integrity of data exchange. Our system keeps the physician's identity confidential from all authorized users, who can still access medical reports publicly. Our proposed mechanism has been shown to be both effective and efficient when it comes to obtaining patient reports from cloud storage.

show abstract

“…With the aim of minimizing interruption to the current production process, the digitization system must include additional capabilities to swiftly update and modify algorithms after they are placed in the production process [24]. Customer happiness is a factor in boosting customer satisfaction, and production lead time is vital in the manufacturing process [25]. Factors include enhancing the motivation to implement Industry 4.0 technology to improve the production processes and enhance decision-makers' perceptions of the industry at small and medium-sized businesses.…”

Section: State Of Artmentioning

confidence: 99%

Design and Implement Low-Cost Industry 4.0 System Using Hybrid Six Sigma Methodology for CNC Manufacturing Process

Duc,

Hlavaty,

Bilik

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The mechanical components manufacturing industry is carried out through turning processes using CNC machines. Improving productivity and improving product quality in the CNC process is necessary. Automating operations at the CNC process with Industry 4.0 technology at low cost is a direction in the right direction with the development trend of Industry 4.0 technology. Making decisions to improve CNC processes at operations and implementing continuous improvement is an urgent activity. Industry 4.0 technology helps connect data between customer companies, business sectors, and supply chain partners in a more specific and simple way. Commonly connected data between companies helps decision-makers at each company have a common source of analytical data to make more effective production and business development decisions. Industry 4.0 technologies are the subject of much research, but it is important to consider how to integrate them into the manufacturing world in the most efficient and effective way possible. Manufacturers will benefit from this article's additional insights to solve their problems more effectively. This paper proposes a Hybrid Six Sigma methodology based on Fuzzy TOPSIS and PLS-SEM methods. First, the Fuzzy TOPSIS method helps decision-makers to select problem points for improvement in an unknown environment to implement continuous improvement. Second, the PLS-SEM method evaluates the impact factors of the results of continuous improvement at the mechanical component factory. The Industry 4.0 technology is proposed to be designed, tested, and installed in the CNC manufacturing process. As a result of this study, the product length dimensional error rate decreased from 54.90% per month to zero defects, saving $9593 in annual production costs. Research machines are semiautomatic and cannot increase or digitize their performance because of studies on the application of low-cost Industry 4.0 technology systems to increase the capacity of industrial processes. This study contributes by using Industry 4.0 technology's low-cost way to improve manufacturing processes using the Hybrid DMAIC method in Six Sigma methodology. This hybrid approach is adaptable and can be used with different business process improvement models.

show abstract

Plant Factory: A New Playground of Industrial Communication and Computing

Cited by 15 publications

References 26 publications

Integrating Six Sigma into an Industry 4.0 System for Enhanced Productivity: A Case Study in CNC Processes

Integrating Six Sigma into an Industry 4.0 System for Enhanced Productivity: A Case Study in CNC Processes

An Intelligent Cardiovascular Diseases Prediction System Focused on Privacy

Design and Implement Low-Cost Industry 4.0 System Using Hybrid Six Sigma Methodology for CNC Manufacturing Process

Contact Info

Product

Resources

About