1,721,157 research outputs found
Advanced IT Methods of Signal Processing and Decision Making for Zero Defect Manufacturing in Machining
No full textAbstractIn the cutting zone of a machining process, several variables are influenced by process conditions: cutting force, vibrations, temperature, acoustic emission, power absorption. Some variables, useful for process monitoring, can be measured by sensors installed on the machine tool. However, when assessing a particular process variable, a single sensory source may not be able to meet all the requirements. A solution is sensor data fusion, the purpose of which is to combine sensory information from disparate sources so that the resulting intelligence is reinforced. Multi-sensor signal processing provides for the extraction and selection of signal features, relevant for the machining monitoring scope, that are assembled into sensor fusion pattern feature vectors functional for pattern recognition through knowledge based methods. Cognitive paradigms, such as artificial neural networks, can map input information fed by pattern feature vectors to output determinations for decision making on machining process conditions, including the adoption of corrective actions. Application cases of multi-sensor monitoring of machining process conditions investigated at the Fh-J_LEAPT Naples are reported with reference to: (a) workpiece residual stress assessment in turning of nickel base alloys; (b) tool wear state identification in machining of fiber reinforced composites; (c) chip form control in turning of C steel
Machine learning for in-process end-point detection in robot-assisted polishing using multiple sensor monitoring
No full textThe decision on polishing operation stopping time when employing a robot-assisted polishing machine is a critical issue for the full automation of the polishing process. In this paper, a machining learning approach based on artificial neural networks was developed using multiple sensor monitoring data to realize an intelligent system capable to determine the state of the polishing process in terms of target surface roughness achievement. During the experimental tests, surface roughness measurements were performed on each polished workpiece and the acquired sensor signals were analyzed and processed by applying two kinds of feature extraction procedures: statistical features extraction and principal component analysis. By feeding diverse types of feature pattern vectors to artificial neural networks, a highly accurate classification of the polishing process state was obtained using the principal component feature pattern vectors
Ciclaggio termico di provini riportati con tecnologia laser:analisi mediante emissione acustica
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Wavelet transform feature extraction for chip form recognition during carbon steel turning
No full textCutting force sensor monitoring and wavelet decomposition signal processing were implemented for feature extraction and pattern recognition of chip form typology during turning of 1045 carbon steel. The wavelet packet transform was applied for the analysis of the detected cutting force signals by representing them in a time-frequency domain and providing for the extraction of wavelet packet statistical features. The latter were used to construct wavelet packet feature vectors, ranked according to the number of overlapping elements related to favourable or unfavourable chip forms that cause noise in the pattern recognition procedure (lower number, lower noise, higher rank). The eight highest ranked wavelet packet feature vectors were selected as inputs to a neural network decision-making system on chip form acceptability. Subsequently, a data refinement procedure was employed to improve the neural network performance in the chip form identification process
Microbial-based cutting fluids as bio-integration manufacturing solution for green and sustainable machining
No full textMetal working fluids in machining operations, also called cutting fluids (CFs), accomplish the main functions of lubrication between the tool and the work material, cooling down the cutting zone, and washing away the chips from the cutting area. Traditional CFs are either entirely based on mineral oils or, for water-based CFs, contain up to 10% mineral oils. Over time, CFs become contaminated by foreign substances, including bacteria and fungi, causing rancid odour in the work environment and health hazards for the machinists; this contamination is countered by adding biocides, which in turn can be polluting and unhealthy. Conventional CFs, therefore, are potentially pathogenic for humans, deteriogenic for the environment, and costly to dispose of due to the mineral oil and biocide contents. As the global CF consumption amounts to over two million tons/year, the development of greener, more sustainable CFs is highly desired in the manufacturing industry. In this paper, the replacement of mineral oil in CFs with suitable microorganisms providing the lubrication function is studied within a bio-integrated manufacturing approach, with the aim to markedly reduce the negative impact conventional CFs on environment and human health. The turning trials were performed on AISI 1045 steel bars under Small Quantity Lubrication (SQL) conditions using a microbial-based CF containing a microalgae species as lubricant component. The viability and effectiveness of utilising the novel microbial-based CF was positively demonstrated. The cutting forces, tool wear level, surface finish and dimensional accuracy achieved with the microbial-based CF were comparable to or better than for dry cutting and conventional CF
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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