Development of an Intelligent and Automated System for Lean Industrial Production, Adding Maximum Productivity and Efficiency in the Production Process

Araújo, A. F.; Varela, Leonilde; Gomes, Marivan S.; Barreto, Raissa C. C.; Trojanowska, Justyna

doi:10.1007/978-3-319-68619-6_13

Cited by 23 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the evolutionary methods have recently been employed successfully in various engineering sectors. Traditionally, the effectiveness and efficiency of selecting parameters of machining processes are determined by the trial-and-run method or by the experience of the machine operators [4,5]. As per the requirements of smart manufacturing as in ‘turning’ systems, the cutting parameters i.e., cutting speed, feed rate, and depth-of-cut need to be optimized.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

et al. 2019

View full text Add to dashboard Cite

Recently, the concept of smart manufacturing systems urges for intelligent optimization of process parameters to eliminate wastage of resources, especially materials and energy. In this context, the current study deals with optimization of hard-turning parameters using evolutionary algorithms. Though the complex programming, parameters selection, and ability to obtain the global optimal solution are major concerns of evolutionary based algorithms, in the present paper, the optimization was performed by using efficient algorithms i.e., teaching–learning-based optimization and bacterial foraging optimization. Furthermore, the weighted sum method was used to transform the diverse responses into a single response, and then multi-objective optimization was performed using the teaching–learning-based optimization method and the standard bacterial foraging optimization method. Finally, the optimum results reported by these methods are compared to choose the best method. In fact, owing to better convergence within shortest time, the teaching–learning-based optimization approach is recommended. It is expected that the outcome of this research would help to efficiently and intelligently perform the hard-turning process under automatic and optimized environment.

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Table 4, summarizes the results of this TC bibliometric study. strategies to increase productivity (Charrua-Santos et al, 2020;Irfan & Wang, 2019;Varela et al, 2019), such as Lean Production (Charrua-Santos et al, 2020;Varela et al, 2019) and automatization of processes (Araújo et al, 2018), sustainability aspects (Bag et al, 2018(Bag et al, , 2021 and study of disruption drivers (Mithun Ali et al, 2021). Some of main general aspects of this documents are synthesized next.…”

Section: Innovation and Entrepreneurshipmentioning

confidence: 99%

“…They affirm that the cloud-based sharing of data can improve the operational performance of channel members while reducing their overall cost of operations. Also a documented way to improve performance and productivity is processes automatization: Araújo et al (2018) worked on an automatization project based on Lean Production with the "leagile" emerging concept, focused on the prompt response to turbulent and dynamic markets.…”

Section: Innovation and Entrepreneurshipmentioning

confidence: 99%

Flexibility of operations in developing countries with Industry 4.0. A systematic review of literature

Tascón

Mejía

Rojas-Sánchez

2022

Prod.

View full text Add to dashboard Cite

Paper aims: To provide guidelines on current state of the literature on aspects related to the implementation of Industry 4.0 (I4.0) for operations flexibility in emerging economies, and derived from this, propose a conceptual framework for the analysis of the main topics studied. Originality:Based on previous literature reviews on related topics (I4.0 and flexibility), we recognized an opportunity to focus on developing countries which until now has not been documented. Research method:We carried out a systematic review of the literature together with a topic cluster analysis. Main findings:The study revealed that there is a perceived consensus on how Industry 4.0 technologies can impact flexibility in emerging countries. Cloud computing is by far the most adopted technology despite the fact that there is a wide concern about security issues. The use of other technologies and their impact appears to be incipient. Implications for theory and practice:The study presented here can be used as a starting point for new directions of research in terms of adoption of these technologies and new applications developed and/or customized to the realities of emerging countries.

show abstract

“…Emerging Industry 4.0 technologies enabling the collection of production management data offer the opportunity to receive accurate information and feedback for reliable production planning and control (Dallasega et al, 2017;Reuter et al, 2016). Paper (Araújo et al, 2018) presents the development of an intelligent and automated system for lean industrial production, ensuring maximum productivity and efficiency of the production process. The study (Hrušecká et al, 2018) presents the discrete-event simulation model for increasing the efficiency of warehouse management, which had a direct positive impact on production management.…”

Section: Cluster 1 Author's Abstract: Learning Factory Lean Management Lean Production Management Optimization Discrete Event Simulationmentioning

confidence: 99%

Towards ‘Lean Industry 4.0ʹ – Current trends and future perspectives

Ejsmont

Gładysz

Corti

et al. 2020

Cogent Business & Management

View full text Add to dashboard Cite

The enterprises to be competitive are constantly looking for continuous increase of productivity, quality and level of services. With the development of Industry 4.0 concept, manufacturers are more confident about new advantages of automation and systems integration. Lean management is well developed and empirically proven effective managerial approach. Combining Lean and Industry 4.0 practices seems to be necessary evolutionary step for further raise the level of operational excellence (exploitation of finance, workload, materials, machines/ devices). There is an increasing number of Industry 4.0 solutions used to reduce waste (as known from Lean Management). Therefore, the main objective of this article aims at presenting the results of a literature review on the concept of 'Lean Industry 4.0ʹ. Dynamic methodology called "Systematic Literature Network Analysis (SLNA)" was used. It combines the Systematic Literature Review approach with the quantitative analysis of bibliographic networks to detect emerging topics and the dynamic evolution of the topics. The paper is a comprehensive systematization and rationalization of knowledge about the integration of LM and I4.0 concepts, identifies the most important research trends and defines directions for future research. The article contains a framework that presents the current state of knowledge in the area of Lean Industry 4.0.

show abstract

Development of an Intelligent and Automated System for Lean Industrial Production, Adding Maximum Productivity and Efficiency in the Production Process

Cited by 23 publications

References 20 publications

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing

Flexibility of operations in developing countries with Industry 4.0. A systematic review of literature

Towards ‘Lean Industry 4.0ʹ – Current trends and future perspectives

Contact Info

Product

Resources

About