Data mining for quality control: Burr detection in the drilling process

Ferreiro, Susana; Sierra, Basilio; Irigoien, Itziar; Gorritxategi, Eneko

doi:10.1016/j.cie.2011.01.018

Cited by 46 publications

(25 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lessons learned from knowledgeable and experienced production teams can help to design, install and carry out planned routine maintenance programmes and plant operation which leave the human resources free to focus on their core activities. Accordingly, taking all of the above factors into account, in the context of the continuous improvement the data mining for quality control is useful to optimize the industrial process and reduce economic cost (Ferreiro, Sierra, Irigoien, & Gorritxategi, 2011).…”

Section: Analyzing Results and Extracting Knowledgementioning

confidence: 99%

Mining association rules for the quality improvement of the production process

Kamsu-Foguem

Rigal

Mauget

2013

Expert Systems with Applications

132

View full text Add to dashboard Cite

To cite this version:Bernard Kamsu-Foguem, Fabien Rigal, Félix Mauget. Mining association rules for the quality improvement of the production process. Expert Systems with Applications, Elsevier, 2013, vol. 40, pp. 1034-1045 b s t r a c tAcademics and practitioners have a common interest in the continuing development of methods and computer applications that support or perform knowledge-intensive engineering tasks. Operations management dysfunctions and lost production time are problems of enormous magnitude that impact the performance and quality of industrial systems as well as their cost of production. Association rule mining is a data mining technique used to find out useful and invaluable information from huge databases. This work develops a better conceptual base for improving the application of association rule mining methods to extract knowledge on operations and information management. The emphasis of the paper is on the improvement of the operations processes. The application example details an industrial experiment in which association rule mining is used to analyze the manufacturing process of a fully integrated provider of drilling products. The study reports some new interesting results with data mining and knowledge discovery techniques applied to a drill production process. Experiment's results on real-life data sets show that the proposed approach is useful in finding effective knowledge associated to dysfunctions causes.

show abstract

Section: Analyzing Results and Extracting Knowledgementioning

confidence: 99%

Mining association rules for the quality improvement of the production process

Kamsu-Foguem

Rigal

Mauget

2013

Expert Systems with Applications

132

View full text Add to dashboard Cite

show abstract

“…The monitoring and prediction of tool wear and breakage in drilling are mainly done indirectly through thrust force [3] to [5]. Ferreiro et al [6] and [7] and Peña et al [8] completed the burr monitoring by extracting the features from the spindle torque signal in the drilling process. Ramirez et al [9] established a temperature model for the drilling tool and combined the cutting force signal and temperature signal characteristics to evaluate the surface quality of the drilled surface.…”

Section: Introductionmentioning

confidence: 99%

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Zhou¹,

Li²,

Liu³

2019

SV-JME

View full text Add to dashboard Cite

To address the difficulty in extracting the features of vibration signals under intense background noise, a new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. First, it is assumed that the spindle vibration signal of machine idling and the vibration signal caused when the tool cuts the workpiece are independent of each other, and the machine's idling signal is perceived as the 'additive noise' of the monitoring signal in light of the spectral subtraction principles. Secondly, in line with the characteristics of vibration signals in the drilling process, the 'additive noise' and monitoring signal are split into multiple frequency bands via wavelet packet decomposition. Eventually, spectral subtraction is performed independently in each band, and the vibration signals are reconstructed. The simulations and experimental results indicate that the new method should effectively eliminate the impact of environmental noise on the process of feature extraction to intensify the features of the monitoring signal. Highlights• A new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. • We have addressed the difficulty in extracting the weak features of vibration signals under intense background noise. • Compared with the traditional threshold de-noising method, the problem of threshold selection can be avoided.

show abstract

“…Industries today need to stay ahead in competition by servicing and satisfying customer's needs. At the moment, the process to ensure the quality of the product in manufacturing is based on the visual inspection, and these operations increase the cost and the resources during the process [15]. The application of data mining can help in identifying not only the defective products but can also simultaneously determine the significant factors that influence the success or failure of the process.…”

Section: Quality Improvement Based On Data Miningmentioning

confidence: 99%

“…A product produced with variation in characteristics, than the anticipated are called as defect. Ferreiro et al (2011) proposed the system to detect automatically the quality of material [15]. The material for the tests was aluminum Al 7075-T6, commonly used in aeronautical structures.…”

Section: Quality Improvement Based On Data Miningmentioning

confidence: 99%

“…Data mining techniques provide promising potential for improving quality control in manufacturing systems [73], especially in complex manufacturing environments wherein detection of causes of problems is difficult [16]. Currently, the process to ensure the quality of the product in manufacturing is based on the visual inspection, and these operations increase the cost and the resources during the process [15]. The proposed OSRDP architecture framework utilized data mining algorithm to detect quality of the product in real-time.…”

mentioning

confidence: 99%

See 1 more Smart Citation

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

Syafrudin

Fitriyani

et al. 2017

Sustainability

View full text Add to dashboard Cite

Currently, the manufacturing industry is experiencing a data-driven revolution. There are multiple processes in the manufacturing industry and will eventually generate a large amount of data. Collecting, analyzing and storing a large amount of data are one of key elements of the smart manufacturing industry. To ensure that all processes within the manufacturing industry are functioning smoothly, the big data processing is needed. Thus, in this study an open source-based real-time data processing (OSRDP) architecture framework was proposed. OSRDP architecture framework consists of several open sources technologies, including Apache Kafka, Apache Storm and NoSQL MongoDB that are effective and cost efficient for real-time data processing. Several experiments and impact analysis for manufacturing sustainability are provided. The results showed that the proposed system is capable of processing a massive sensor data efficiently when the number of sensors data and devices increases. In addition, the data mining based on Random Forest is presented to predict the quality of products given the sensor data as the input. The Random Forest successfully classifies the defect and non-defect products, and generates high accuracy compared to other data mining algorithms. This study is expected to support the management in their decision-making for product quality inspection and support manufacturing sustainability.

show abstract

Data mining for quality control: Burr detection in the drilling process

Cited by 46 publications

References 31 publications

Mining association rules for the quality improvement of the production process

Mining association rules for the quality improvement of the production process

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

Contact Info

Product

Resources

About