scholarly journals The Technology Development and Management of Smart Manufacturing System: A Review On Theoretical and Technological Perspectives

2021 ◽  
Vol 17 (43) ◽  
pp. 170
Author(s):  
Al-Amin Al-Amin ◽  
Tanjim Hossain ◽  
Jahidul Islam
Keyword(s):  
Technology Management ◽  
Diffusion Of Innovation ◽  
Manufacturing Industry ◽  
Production Efficiency ◽  
Manufacturing System ◽  
Technology Development ◽  
Human Interaction ◽  
Smart Manufacturing ◽  
Future Research ◽  
Apparel Production

This paper encompasses a state-of-the-art review on smart manufacturing system (SMS), focusing on theoretical relevance to technology development and technology management. The theoretical foundation of technology development has been reviewed based on the Rogers’ Diffusion of Innovation (DoI) theory and technology management has been focused on the basis of Technology Strategy Model (TSM) of Rieck and Dickson to shape the paper with theory of Management of Technology (MOT). A patent on SMS has been discussed to show how different technologies are integrated into this system. The characteristics of SMS have discussed the overall aspects of this future technological system. The the global textile complex has been depicted with a proposed SMS model of the apparel production unit. This study integrates the latest articles and technology on future manufacturing system perspectives, which gives a robust idea of mintegration have been identified as the major components of SMS. A brief model of SMS in the apparel production system demonstrated how SMS works in the industry level. The researchers on smart manufacturing can take away the above insights into their future research to take SMS research more forward.inimizing human interaction and maximizing the production efficiency in the manufacturing industry. The cyber-physical system, AI, ERP, digital twin, big data, additive manufacturing, cloud manufacturing, simulation, and vertical and horizontal 

scholarly journals Sustainable and Smart Manufacturing: An Integrated Approach

2020 ◽  
Vol 12 (6) ◽  
pp. 2280 ◽  
Author(s):  
Mohamed Abubakr ◽  
Adel T. Abbas ◽  
Italo Tomaz ◽  
Mahmoud S. Soliman ◽  
Monis Luqman ◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Sustainability Assessment ◽  
Negative Impact ◽  
Manufacturing Sector ◽  
Sustainable Manufacturing ◽  
Smart Manufacturing ◽  
Future Research ◽  
Bottom Line ◽  
Manufacturing Performance ◽  
Sustainability Concept

The necessity for decreasing the negative impact of the manufacturing industry has recently increased. This is getting recognized as a global challenge due to the rapid increase in life quality standards, demand, and the decrease in available resources. Thus, manufacturing, as a core of the product provision system and a fundamental pillar of civilized existence, is significantly influenced by sustainability issues. Furthermore, current manufacturing modeling and assessment criteria require intensive revisions and upgrades to keep up with these new challenges. Nearly all current manufacturing models are based on the old paradigm, which was proven to be inadequate. Therefore, manufacturing technology, along with culture and economy, are held responsible for providing new tools and opportunities for building novel resolutions towards a sustainable manufacturing concept. One of such tools is sustainability assessment measures. Revising and updating such tools is a core responsibility of the manufacturing sector to efficiently evaluate and enhance sustainable manufacturing performance. These measures should be adequate to respond to the growing sustainability concerns in pursuit of an integrated sustainability concept. The triple bottom line (TBL) that includes environment, economic, and social dimensions has usually been used to evaluate sustainability. However, there is a lack of standard sets of sustainable manufacturing performance measures. In addition to the sustainability concept, a new concept of smart manufacturing is emerging. The smart manufacturing concept takes advantage of the recent technological leap in Artificial Intelligent (AI), Cloud Computing (CC), and the Internet of Things (IoT). Although this concept offers an important step to boost the current production capabilities to meet the growing need, it is still not clear whether the two concepts of smart manufacturing and sustainability will constructively or destructively interact. Therefore, the current study aims to integrate the sustainable smart manufacturing performance by incorporating sustainable manufacturing measures and discussing current and future challenges that are faced by the manufacturing sector. In addition, the opportunities for future research incorporating sustainable smart manufacturing are also presented.

scholarly journals “Industrie 4.0” and Smart Manufacturing – A Review of Research Issues and Application Examples

2017 ◽  
Vol 11 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Klaus-Dieter Thoben ◽  
◽  
Stefan Wiesner ◽  
Thorsten Wuest ◽  
◽  
...  
Keyword(s):  
Manufacturing Industry ◽  
Business Models ◽  
Industrial Revolution ◽  
The United States ◽  
Industrie 4.0 ◽  
Business Strategies ◽  
Smart Manufacturing ◽  
Future Research ◽  
Manufacturing Companies ◽  
Research Issues

A fourth industrial revolution is occurring in global manufacturing. It is based on the introduction ofInternet of thingsandservitizationconcepts into manufacturing companies, leading to vertically and horizontally integrated production systems. The resultingsmart factoriesare able to fulfill dynamic customer demands with high variability in small lot sizes while integrating human ingenuity and automation. To support the manufacturing industry in this conversion process and enhance global competitiveness, policy makers in several countries have established research and technology transfer schemes. Most prominently, Germany has enacted itsIndustrie 4.0program, which is increasingly affecting European policy, while the United States focuses onsmart manufacturing. Other industrial nations have established their own programs on smart manufacturing, notably Japan and Korea. This shows that manufacturing intelligence has become a crucial topic for researchers and industries worldwide. The main object of these activities are the so-called cyber-physical systems (CPS): physical entities (e.g., machines, vehicles, and work pieces), which are equipped with technologies such as RFIDs, sensors, microprocessors, telematics or complete embedded systems. They are characterized by being able to collect data of themselves and their environment, process and evaluate these data, connect and communicate with other systems, and initiate actions. In addition, CPS enabled new services that can replace traditional business models based solely on product sales. The objective of this paper is to provide an overview of the Industrie 4.0 and smart manufacturing programs, analyze the application potential of CPS starting from product design through production and logistics up to maintenance and exploitation (e.g., recycling), and identify current and future research issues. Besides the technological perspective, the paper also takes into account the economic side considering the new business strategies and models available.

Safety Strategy in the Smart Manufacturing System: A Human Robot Collaboration Case Study

2020 ◽  
Author(s):  
Xi Vincent Wang ◽  
Lihui Wang
Keyword(s):  
Manufacturing Industry ◽  
Industrial Robot ◽  
Working Environment ◽  
Smart Manufacturing ◽  
Future Research ◽  
Human Beings ◽  
Biometric Data ◽  
Action Strategy ◽  
Human Robot Collaboration ◽  
Safety Strategy

Abstract The next generation of the manufacturing industry calls for new approaches with smarter functionalities and better/safer working environment for human beings. The Human-Robot Collaboration (HRC) approach provides a feasible solution combing the flexibility and intelligence of a human, together with the accuracy and strength of an industrial robot. However, in the past years, despite the significant development of different HRC approaches, there is still a lack of clear safety strategy for an HRC system. Thus in this paper, the extensive taxonomy of the human-robot relations are first defined to provide a clear classification in different robotic scenarios. Then a comprehensive action strategy is developed toward different scenarios and human stakeholder’s roles. A dynamic HRC layout approach is also introduced based on the actual speed of human and robot and the distance between them. The feasibility of the proposed approaches in this paper is then evaluated via the implemenntation in an HRC-based assembly cell. The operator’s biometric data is also included in the HRC control loop. It is proven achievable to conduct personalised HRC safety strategy based on the human stakeholder’s role, physical conditions, speed and so forth. The future research outlooks and essential considerations are addressed at the end of the paper.

scholarly journals The research of hidden Markov models for overall equipment effectiveness analysis in smart manufacturing system

2018 ◽  
Vol 192 ◽  
pp. 01013
Author(s):  
Chia- Yu Hung ◽  
Chen- Yang Cheng
Keyword(s):  
Radio Frequency Identification ◽  
Manufacturing Industry ◽  
Production Management ◽  
Production Efficiency ◽  
Error Rates ◽  
Smart Manufacturing ◽  
Production Cycle ◽  
Production Quantity ◽  
Machine Utilization ◽  
Low Efficiency

In the manufacturing industry, excellent product quality and increased production flexibility can be achieved by eliminating waste and improving production efficiency. In the past, the manufacturing industry used manual records of production information, but this method is characterized by low efficiency and high error rates. Even if a programmable logic controller and radio-frequency identification are employed, problems still occur because of constraints such as different machine types and high costs. The use of a cyber–physical system and information visualization requires the collection of manufacturing information in order to facilitate the analysis of manufacturing data. Monitoring the machining status. This study proposes an approach for segmenting machine-processed signals. With plug-and-play noninvasive current-sensing equipment to collect machine production information, this approach can immediately determine the state of the manufacturing process and calculate the machine utilization, machine production cycle, and production quantity. The goal is to enable the use of this method with this equipment, improve machine utilization, instantly identify the production quantity, and reduce equipment idle time to reduce manufacturing waste, thus rendering production management more convenient and faster.

Characteristics Analysis of Flexible Manufacturing Systems

2013 ◽  
Vol 329 ◽  
pp. 172-175
Author(s):  
Jin Feng Wang ◽  
Guang Feng Zhang ◽  
Xian Zhang Feng
Keyword(s):  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Material Handling ◽  
Manufacturing Industry ◽  
Production Efficiency ◽  
Manufacturing System ◽  
Learning System ◽  
Machining Process ◽  
Characteristics Analysis

For the rigid automatic line, although its production efficiency is high, but the flexible is less in the machining process, the machine and the assembly line need be shut down to adjust or replace for machine tools, jigs, tools, and tooling equipment, etc. When the work pieces for the machining is changed. It caused a heavy workload, wasting a lot of time. Flexible Manufacturing Systems consisted of unified control system, material handling system and a set of digital control processing equipment; it is the automation machinery manufacturing system to adapt the processing object transform. It has become one of the important means of manufacturing industry to obtain the advantages of market competitiveness. This paper gives the composition, algorithm and application of learning system concept, composition, and classification, characteristics of the flexible manufacturing system, the development overview and its application are induced in this paper.

Study On Simulation Of The Main Shaft Production Line

2012 ◽  
Vol 472-475 ◽  
pp. 2076-2079 ◽  
Author(s):  
Shu Feng Chai ◽  
Su Jun Luo ◽  
Li Jie Zhang
Keyword(s):  
Production System ◽  
Production Line ◽  
Manufacturing Industry ◽  
Production Efficiency ◽  
Manufacturing System ◽  
Main Shaft ◽  
Building Model ◽  
Discrete Manufacturing System ◽  
Discrete Manufacturing ◽  
Improve Production Efficiency

Since modern production system is a highly complicated discrete manufacturing system, it is very difficult to design the production line by traditional means. However, through building model of production system in virtual environment, analyzing and evaluating production system performance based on system simulating technology, the production system’s parameter and configuration can be optimized ahead of production plan to optimize production process and improve production efficiency. In this paper, the main shaft production line simulation model is constructed based on the object oriented discrete system software eM-Plant. The production line throughput, utilization and bottleneck operations are analyzed. Based on this, it can support the configuration of production line optimized. The example verifies that the modeling and simulation technology could be successfully used in manufacturing industry.

scholarly journals Scoping research on sustainability performance from manufacturing industry sector

2019 ◽  
Vol 17 (2) ◽  
pp. 134-146 ◽  
Author(s):  
Ismail Hussien Droup Adam ◽  
Ahmad Jusoh ◽  
Abbas Mardani ◽  
Dalia Streimikiene ◽  
Khalil Md Nor
Keyword(s):  
Manufacturing Industry ◽  
Manufacturing System ◽  
Empirical Studies ◽  
Manufacturing Firms ◽  
Future Research ◽  
Sustainable Operations ◽  
Sustainable Performance ◽  
Sustainable Value ◽  
Sustainability Practices ◽  
The Impact

Sustainability is a key area of concern for manufacturing firms’ long-term success. However, the manufacturing industry has not been fully conscious of the potential sustainable values across manufacturing system. There is a need to better understand how companies can improve sustainable value creation. Recent research and practices have shown that sustainable operations can be one way to create sustainable values (e.g. economic, environmental and social). This review article focuses on the available empirical studies on the impact of lean and sustainability practices on sustainable performance from 2000 to 2018 in the context of manufacturing firms. Integrating lean and sustainability practices into manufacturing system confrontы operations managers with paradoxical tensions of sustainability objectives. Theoretically having paradoxical mindset will help firms’ managers make sense of and responв to such paradoxical tensions. In the context of sustainable operations studies, the issue of paradoxical mindset has been given less emphasis. Therefore, through the lens of the paradox theory, this study has developed a new conceptual framework for future research to investigate how paradoxical mindset moderates the impact of lean and sustainability practices on the sustainable performance of manufacturing industry. This study may add to the understanding of the circumstances, under which lean and sustainability practices impact sustainable outcomes.

scholarly journals Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 13
Author(s):  
Yuhang Yang ◽  
Zhiqiao Dong ◽  
Yuquan Meng ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
High Performance ◽  
Sampling Design ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Driven ◽  
Surface Measurement ◽  
Smart Manufacturing ◽  
Future Research ◽  
3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

scholarly journals Identification of Technology Diffusion by Citation and Main Paths Analysis: The Possibility of Measuring Open Innovation

2021 ◽  
Vol 7 (1) ◽  
pp. 104
Author(s):  
Wei-Hao Su ◽  
Kai-Ying Chen ◽  
Louis Y. Y. Lu ◽  
Ya-Chi Huang
Keyword(s):  
Research And Development ◽  
Technology Development ◽  
Surgical Navigation ◽  
Future Research ◽  
Integrated Analysis ◽  
Reference Network ◽  
Research Topics ◽  
Academic Literature ◽  
Development Trajectory ◽  
Historic Development

This study collected literature on augmented reality (AR) from academic and patent databases to plot the historic development trajectory of AR and forecast its future research and development trends. A total of 3193 and 13,629 papers were collected from academic and patent databases, respectively. First, a network was established using references from the academic literature; main path analysis was conducted on this reference network to plot the overall development trajectory. Subsequent cluster and word cloud analyses revealed the following five major groups of AR research topics: AR surgical navigation applications, AR education applications, AR applications in manufacturing, AR applications in architecture, and AR applications in visual tracking. Subsequently, the relationships between the overall development trajectory and the five AR research topics were compared. Next, the title and abstract of AR-related academic and patent papers were subjected to text mining to identify keywords with a high frequency of occurrence. The results can provide a reference for industry, government, and academia when planning future development strategies for the AR field. This research adopted an integrated analysis procedure to plot the trajectory of AR technology development and applications successfully and effectively, predict future patent research and development directions and produce technological forecasts.

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