The Convergence of Technologies and Standards Across the Electronic Products Manufacturing Industry (SEMI, OSAT, and PCBA) to Realize Smart Manufacturing

2020 ◽  
Author(s):  
Ranjan Chatterjee ◽  
Daniel Gamota
Keyword(s):  
Manufacturing Industry ◽  
Smart Manufacturing ◽  
Electronic Products ◽  
Convergence Of Technologies

Industrial internet of things for smart manufacturing applications using hierarchical trustful resource assignment

Work ◽  
2021 ◽  
pp. 1-11
Author(s):  
Duan Pingli ◽  
Bala Anand Muthu ◽  
Seifedine Nimer Kadry
Keyword(s):  
Internet Of Things ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
The Internet ◽  
Resource Assignment ◽  
Industrial Internet Of Things ◽  
Assignment Method ◽  
Industrial Internet

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the industrial internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other.

Smart Manufacturing Through a Framework for a Knowledge-Based Diagnosis System

2017 ◽  
Author(s):  
Michael P. Brundage ◽  
Boonserm Kulvatunyou ◽  
Toyosi Ademujimi ◽  
Badarinath Rakshith
Keyword(s):  
Manufacturing Industry ◽  
Ad Hoc ◽  
Root Cause Analysis ◽  
Medical Community ◽  
Smart Manufacturing ◽  
Cause Analysis ◽  
Diagnosis System ◽  
Standard Representation ◽  
Root Cause ◽  
Knowledge Based

Various techniques are used to diagnose problems throughout all levels of the organization within the manufacturing industry. Often times, this root cause analysis is ad-hoc with no standard representation for artifacts or terminology (i.e., no standard representation for terms used in techniques such as fishbone diagrams, 5 why’s, etc.). Once a problem is diagnosed and alleviated, the results are discarded or stored locally as paper/digital text documents. When the same or similar problem reoccurs with different employees or in a different factory, the whole process has to be repeated without taking advantage of knowledge gained from previous problem(s) and corresponding solution(s). When discussing the diagnosis, personnel may miscommunicate over terms used in the root cause analysis leading to wasted time and errors. This paper presents a framework for a knowledge-based manufacturing diagnosis system that aims to alleviate these miscommunications. By learning from diagnosis methods used in manufacturing and in the medical community, this paper proposes a framework which integrates and formalizes root cause analysis by categorizing faults and failures that span multiple organizational levels. The proposed framework aims to enable manufacturing operations by leveraging machine learning and semantic technologies for the manufacturing system diagnosis. A use case for the manufacture of a bottle opener demonstrates the framework.

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.

Smart Production System Modeling: Bernoulli Serial Line Case

2020 ◽  
Author(s):  
Yuting Sun ◽  
Tianyu Zhu ◽  
Liang Zhang
Keyword(s):  
Manufacturing Industry ◽  
Ad Hoc ◽  
New Technologies ◽  
System Modeling ◽  
Critical Issue ◽  
Smart Manufacturing ◽  
Serial Production ◽  
Modeling Process ◽  
Serial Line

Abstract The manufacturing industry has entered the era of Industry 4.0/Smart Manufacturing. New technologies have dramatically changed the way manufacturing activities are carried out on the factory floor. In addition to an enhanced level of equipment automation, automation of decision-making has been one of the key objectives of these new initiatives. On the other hand, a critical issue that has been overlooked is the construction of mathematical models in manufacturing research and studies, which are typically done manually. This manual, ad-hoc nature of mathematical modeling is quite problematic when modeling the job flow in a manufacturing process. As a result, the quality of the models obtained may heavily depend on the experience and personal preference of the modeler. The goal of this paper is to develop a method to standardize and automate the modeling process using standard manufacturing key performance indices in the framework of Bernoulli serial production line model.

scholarly journals Systemic Functions Evaluation based Technological Innovation System for the Sustainability of IoT in the Manufacturing Industry

2019 ◽  
Vol 11 (8) ◽  
pp. 2342 ◽  
Author(s):  
Kao ◽  
Nawata ◽  
Huang
Keyword(s):  
Decision Making ◽  
Technological Innovation ◽  
Manufacturing Industry ◽  
Innovation System ◽  
Government Procurement ◽  
Smart Manufacturing ◽  
Technological Innovations ◽  
Industrial Competitiveness ◽  
Industrial Sustainability ◽  
Study Results

Technological innovations are regarded as the tools that can stimulate economic growth and the sustainable development of technology. In recent years, as technologies based on the internet of things (IoT) have rapidly developed, a number of applications based on IoT innovations have emerged and have been widely adopted by various public and private sectors. Applications of IoT in the manufacturing industry, such as manufacturing intelligence, not only play a significant role in the enhancement of industrial competitiveness and sustainability, but also influence the diffusion of innovative applications that are based on IoT innovations. It is crucial for policy makers to understand these potential reasons for stimulating IoT industrial sustainability, as they can facilitate industrial competitiveness and technological innovations using supportive means, such as government procurement and financial incentives. Therefore, there is a need to ascertain different factors that may affect IoT industrial sustainability and further explore the relationship between these factors. However, finding a set of factors that affects IoT industrial sustainability is not easy. Recently, the robustness of a theoretical framework, termed the technological innovation system (TIS), has been verified and has been used to explore and analyze technological and industrial development. Thus, it is suitable for this research to use this theoretical model. In order to find out appropriate factors and accurately analyze the causality among factors that influence IoT industrial sustainability, this research presents a Bayesian rough Multiple Criteria Decision Making (MCDM) model based on TIS functions by integrating random forest (RF), decision making trial and evaluation (DEMATEL), Bayesian theory, and rough interval numbers. The proposed analytical framework is validated by an empirical case of defining the causality between TIS functions to enable the industrial sustainability of IoT in the Taiwanese smart manufacturing industry. Based on the empirical study results, the cause group consists of entrepreneurial activities, knowledge development, market formation, and resource mobilization. The effect group is composed of knowledge diffusion through networks’ guidance of the search, and creation of legitimacy. Moreover, the analytical results also provide several policy suggestions promoting IoT industrial sustainability that can serve as the basis for defining innovation policy tools for Taiwan and late coming economies.

scholarly journals Blockchain Reference System Architecture Description for the ISA95 Compliant Traditional and Smart Manufacturing Systems

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6456 ◽  
Author(s):  
Erkan Yalcinkaya ◽  
Antonio Maffei ◽  
Mauro Onori
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Research Effort ◽  
Smart Manufacturing ◽  
Reference Architecture ◽  
Systematic Assessment ◽  
Blockchain Technology ◽  
Design Requirements ◽  
Manufacturing Technologies ◽  
Smart Manufacturing Systems

The next-generation technologies enabled by the industry 4.0 revolution put immense pressure on traditional ISA95 compliant manufacturing systems to evolve into smart manufacturing systems. Unfortunately, the transformation of old to new manufacturing technologies is a slow process. Therefore, the manufacturing industry is currently in a situation that the legacy and modern manufacturing systems share the same factory environment. This heterogeneous ecosystem leads to challenges in systems scalability, interoperability, information security, and data quality domains. Our former research effort concluded that blockchain technology has promising features to address these challenges. Moreover, our systematic assessment revealed that most of the ISA95 enterprise functions are suitable for applying blockchain technology. However, no blockchain reference architecture explicitly focuses on the ISA95 compliant traditional and smart manufacturing systems available in the literature. This research aims to fill the gap by first methodically specifying the design requirements and then meticulously elaborating on how the reference architecture components fulfill the design requirements.

Making Circuits More than Once: The Manufacturing Challenges of Electronics Intensive Products

1993 ◽  
Vol 207 (2) ◽  
pp. 83-90 ◽  
Author(s):  
D J Williams ◽  
P P Conway ◽  
D C Whalley
Keyword(s):  
Manufacturing Industry ◽  
Future Generation ◽  
Academic Community ◽  
Manufacturing Processes ◽  
Electronic Products ◽  
Research Areas

This paper is a review of some of the problems facing manufacturers of current and future generation electronic products. It presents a brief overview of work carried out by the Interconnection Group at Loughborough in the support of manufacturing industry, and in particular defines some of the limitations of the manufacturing processes. The paper closes by identifying research areas that can be addressed by the academic community to assist industry in its efforts to maintain competitiveness. Research areas focused on are conventional packaging, multi-chip modules and some aspects of the organization of international manufacture.

scholarly journals A Critical Review of Smart Manufacturing & Industry 4.0 Maturity Models: Applicability in the O&G Upstream Industry

2021 ◽  
Author(s):  
Chinedu Onyeme ◽  
Kapila Liyanage
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Oil And Gas ◽  
Manufacturing Sector ◽  
Transition Process ◽  
Smart Manufacturing ◽  
Maturity Model ◽  
Business Organizations ◽  
Initial State ◽  
Maturity Models

The shift towards Industry 4.0 is a fundamental driver of improved changes observed in today’s business organizations. The difficulties in adapting to this new approach pose challenges for many companies especially in the oil and gas (O&G) upstream sector. To make this path much feasible for companies in this industry, Maturity Models (MMs) are very useful tools in achieving this following their use in evaluation of the initial state of a company for planned development journey towards Industry 4.0 (I4.0) readiness and implementation. Study shows that only a limited number of O&G specific roadmaps, MMs, frameworks and readiness assessments are available today. This paper aims to review the currently available Industry 4.0 MMs for manufacturing industries and analyze their applicability in the O&G upstream sector using the systematic literature review (SLR) methodology, recognizing the specific requirements of this industry. The study looks at the key characteristic for O&G sector in relation to the manufacturing sector and identifies research gaps needed to be addressed to successfully support the O&G sector in readiness for Industry 4.0 implementation. An Industry 4.0 maturity model that reflects the industrial realities for the O&G upstream sector more accurately from insights drawn from the reviews of existing MMs is proposed. This reduces the challenges of the transition process towards Industry 4.0 and provides support for the critical change desired for improved efficiency in the sector.

scholarly journals A Gap Study between Employers’ Expectations in Thailand and Current Competence of Master’s Degree Students in Industrial Engineering under Industry 4.0

2021 ◽  
Vol 27 (1) ◽  
pp. 50-57
Author(s):  
Sirorat Pattanapairoj ◽  
Krisanarach Nitisiri ◽  
Kanchana Sethanan
Keyword(s):  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Master's Degree ◽  
Big Data Analytics ◽  
Industrial Engineering ◽  
Smart Manufacturing ◽  
Educational Institutions ◽  
Time Data ◽  
Analytic Hierarchy ◽  
Master’S Degree

Abstract Industry 4.0 is an era in which the manufacturing industry has adopted digital technologies and the Internet to enable smart manufacturing system, machines used in the production now can communicate with each other and exchange information between each other, and the machinery used in the manufacturing process is more modern and precise. Therefore, educational institutions should develop the curriculum to produce qualified graduates with the knowledge required for the Industry 4.0 era, especially Industrial Engineering graduates who are directly related to the industry sector. The purpose of this research is to collect the data for the Master of Industrial Engineering (MSIE) curriculum development. The Analytic Hierarchy Process (AHP) technique is used to rank the indicators of knowledge that is important to the employment of graduates with a master’s degree in Industrial Engineering, and study the gap between the expectations of employers and the ability of the current MSIE students of Khon Kaen University. The results of the study reveal that the first indicators that are most important to the employment of MSIE graduates is the knowledge of Industry 4.0 strategy and the knowledge that the students should have developed are the collaboration of humans and robots, big data analytics, real time data usage and databased decision making.

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 

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