Research on intelligent expert system of green cutting process and its application

Deng, Zhaohui; Zhang, Hua; Fu, Yahui; Wan, Linlin; Lv, Lishu

doi:10.1016/j.jclepro.2018.02.246

Cited by 21 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Process optimization encompasses applications which perfectionate the execution of production processes, except scheduling problems. Deng et al (2018) elaborated an optimized cutting process for a machine tool depending on the material to be cut. Hong and Lee (2018) used sensors to detect the need for a cleaning operation.…”

Section: Resultsmentioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

View full text Add to dashboard Cite

Advanced analytics applications in smart manufacturing provide beneficiary effects such as predictive maintenance, energy optimized factories, and automized quality management of products. While the benefits and thus the sustainability effects are well investigated, the research field is characterized by many single applications with little conceptual synthesis. Therefore, this research aims to systematize the field and to identify specific sustainability themes in context of advance analytics applications. This chapter provides an overview of existing advanced analytics applications in smart manufacturing and clusters the addressed sustainability themes. Applying a systematic literature review, following the PRISMA statement, 65 different applications within four research perspective could be identified. Furthermore, ten different sustainability-related effects of the applications in smart manufacturing unfold. The content analysis resulted in 27 sustainability themes, which holistically cover all spheres of sustainability, with a focus on the social and economic spheres.

show abstract

Section: Resultsmentioning

confidence: 99%

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Ullrich¹,

Buchmin²

2022

Digitization of the Work Environment for Sustainable Production

View full text Add to dashboard Cite

show abstract

“…The previous works in this regard have either not considered all the dimensions or included a limited number of control parameters in their models. Deng et al [ 20 ] have developed their intelligent expert system for reduction in consumption of specific energy in a cutting process. Although the authors have claimed a potential of 7–15% reduction in energy consumption, the other important sustainability measures related to resources and environment are not included.…”

Section: Discussionmentioning

confidence: 99%

“…The approach incorporating real-time cutting force signals is reported to have outperformed the one utilizing length of cut as the indicator of the tool wear’s state. Deng et al [ 20 ] have presented an expert system for optimizing green cutting process. A specific emphasis is placed on estimating cutting tool’s energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Machining Sustainability Using Fuzzy Rule-Based System

et al. 2021

View full text Add to dashboard Cite

Quantification of a highly qualitative term ‘sustainability’, especially from the perspective of manufacturing, is a contemporary issue. An inference mechanism, based on approximate reasoning, is required to tackle the complexities and uncertainties of the manufacturing domain. The work presents development of a fuzzy rule-based system to quantify sustainability of the most widely utilized manufacturing process: machining. The system incorporates the effects of key control parameters of machining on several sustainability measures, as reported in the literature. The measures are categorized under the three dimensions of sustainability and contribute to the sustainability scores of the respective dimensions with different weightages. The dimensions’ scores are added up in different proportions to obtain the holistic sustainability score of the process. The categories of the control parameters incorporated into the system include type of the process, work material, material hardness, tool substrate and coating, tool geometry, cutting fluids, and cutting parameters. The proposed method yields sustainability scores, ranging between 0 and 100 of machining processes against the given values of their prominent control parameters. Finally, the rule-based system is applied to three different machining processes to obtain the measures of their accomplishment levels regarding economic, environmental, and societal dimensions of sustainability. The sustainability score of each process is then obtained by summing up the three accomplishment levels under the respective weightages of the dimensions. The presented approach holds immense potentials of industrial application as it can conveniently indicate the current sustainability level of a manufacturing process, leading the practitioners to decide on its continuation or improvement.

show abstract

“…Deficiencies are still featured in currently common OW methods, which include entropy theory [47,48], technique for order preference by similarity to ideal solution method (TOPSIS) [49,50], gray relational analysis (GRA) [51] and the criteria importance though intercriteria correlation method (CRITIC) [52,53]. These traditional methods could fetch objective information of sample data using self-contained mathematical theory and analysis; however, they depend on sample data excessively and are easy to get disturbed by data fluctuation, resulting in many deficiencies including complicated calculations, poor relevance and even overlooking practical situations [54].…”

Section: Introductionmentioning

confidence: 99%

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

Wang

Bai

et al. 2018

Water

View full text Add to dashboard Cite

Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin.

show abstract

Research on intelligent expert system of green cutting process and its application

Cited by 21 publications

References 22 publications

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Advanced analytics applications in smart manufacturing – A systematic literature review on their perspectives, effects, and sustainability

Estimation of Machining Sustainability Using Fuzzy Rule-Based System

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

Contact Info

Product

Resources

About