Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509492 | PMC |
| http://dx.doi.org/10.3390/ma14195473 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Integrating rule-based NLP and large language models for statin information extraction from clinical notes.
Int J Med Inform
September 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address:
Background: Identifying patient-specific barriers to statin therapy, such as intolerance or deferral, from clinical notes is a major challenge for improving cardiovascular care. Automating this process could enable targeted interventions and improve clinical decision support (CDS).
Objective: To develop and evaluate a novel hybrid artificial intelligence (AI) framework for accurately and efficiently extracting information on statin therapy barriers from large volumes of clinical notes.
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFMachine Learning and Lexical Rule-Based Cost-Efficient Emotion Annotation of Hinglish Utterances.
J Vis Exp
August 2025
Chitkara University Institute of Engineering & Technology, Chitkara University.
Emotion annotation in code-mixed languages like Hinglish (Hindi-English) presents unique challenges due to linguistic complexity and resource constraints. This study introduces a hybrid active learning framework that combines lexical rules, machine learning, and iterative expert feedback to achieve cost-efficient, high-accuracy emotion annotation. Grounded in psychological theories of emotion, including Discrete Emotions Theory and Cognitive Appraisal Theory, the framework employs bilingual emotion dictionaries (e.
View Article and Find Full Text PDFTexts Are More than Notes, They Are Data: A Glimpse into How Machines Understand Text.
Radiology
August 2025
Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio.
Natural language processing (NLP) has undergone extensive transformation since its infancy from rule-based systems to the sophisticated architectures of today's machine learning models. Initially, NLP relied on hard-coded grammar rules and dictionaries, which were labor-intensive and lacked flexibility. With the introduction of statistical NLP in the late 20th century, machines began learning language patterns from large datasets, improving fluency and scalability.
View Article and Find Full Text PDFFrontiers Shaping the Next Generation of Transformation Product Prediction and Toxicological Assessment.
Environ Sci Technol
September 2025
Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), P.P. Box 7050, SE-750 07 Uppsala, Sweden.
The characterization of transformation products (TPs) is crucial for understanding chemical fate and potential environmental hazards. TPs form through (a)biotic processes and can be detected in environmental concentrations comparable to or even exceeding their parent compounds, indicating toxicological relevance. However, identifying them is challenging due to the complexity of transformation processes and insufficient data.
View Article and Find Full Text PDF