Proposal to improve productivity in companies of a wooden furniture cluster in Peru

Wood is a natural polymeric material that is an important constituent of many heritage collections. Because of its susceptibility to biodegradation, it is often chemically treated with substances that can be harmful to human health. One of the most widely used wood preservatives was pentachlorophenol (PCP), which is still present in museum objects today, although its use has been restricted for about forty years. The development of non-destructive methods for its determination, suitable for the analysis of valuable objects, is therefore of great importance. In this work, two non-destructive solid-phase microextraction (SPME) methods were developed and optimized, using either headspace or contact mode. They were compared with a destructive solvent extraction method and found to be suitable for quantification in the range of 7.5 to 75 mg PCP/kg wood at room temperature. The developed semi-quantitative methods were applied in the wooden furniture depot of National Museum of Slovenia. PCP was detected inside two furniture objects using headspace mode. The pesticide lindane was also detected in one object. The indoor air of the depot with furniture was also sampled with HS SPME, and traces of PCP were found. According to the results, SPME methods are suitable for the detection of PCP residues in museum objects and in the environment.

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Export Performance on the Malaysian Wooden Furniture Industry: An Empirical Study

Journal of International Food & Agribusiness Marketing ◽

10.1080/08974430903372773 ◽

2010 ◽

Vol 22 (1-2) ◽

pp. 52-69 ◽

Cited By ~ 2

Author(s):

Azaze-Azizi Abdul Adis

Keyword(s):

Empirical Study ◽

Export Performance ◽

Furniture Industry ◽

Wooden Furniture

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Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Sensors ◽

10.3390/s21113633 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3633

Author(s):

Rytis Augustauskas ◽

Arūnas Lipnickas ◽

Tadas Surgailis

Keyword(s):

Neural Network ◽

Network Architecture ◽

Proposed Model ◽

Drilling Quality ◽

Drilling Operations ◽

Spatial Pyramid Pooling ◽

Automated Object Detection ◽

Wooden Furniture ◽

Product Assembly ◽

Small Bump

Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufacturing process, milling cutouts, and other kinds of defects. Intelligent computer vision methods can be engaged for global contextual analysis with local information attention for automated object detection and segmentation. In this paper, we propose blind and through drilled holes segmentation on textured wooden furniture panel images using the UNet encoder-decoder modifications enhanced with residual connections, atrous spatial pyramid pooling, squeeze and excitation module, and CoordConv layers for better segmentation performance. We show that even a lightweight architecture is capable to perform on a range of complex textures and is able to distinguish the holes drilling operations’ semantical information from the rest of the furniture board and conveyor context. The proposed model configurations yield better results in more complex cases with a not significant or small bump in processing time. Experimental results demonstrate that our best-proposed solution achieves a Dice score of up to 97.89% compared to the baseline U-Net model’s Dice score of 94.50%. Statistical, visual, and computational properties of each convolutional neural network architecture are addressed.

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Life Cycle Assessment (LCA)-based tools for the eco-design of wooden furniture

Journal of Cleaner Production ◽

10.1016/j.jclepro.2021.129249 ◽

2021 ◽

Vol 324 ◽

pp. 129249

Author(s):

Isabella Bianco ◽

Francesca Thiébat ◽

Corrado Carbonaro ◽

Simonetta Pagliolico ◽

Gian Andrea Blengini ◽

...

Keyword(s):

Life Cycle Assessment ◽

Life Cycle ◽

Wooden Furniture

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Factors Related to Lung Function In Wood Furniture Workers In Oesapa Village, Kelapa Lima Sub District Kupang City

Timorese Journal of Public Health ◽

10.35508/tjph.v2i2.2785 ◽

2020 ◽

Vol 2 (2) ◽

pp. 64-72

Author(s):

Diana Juniati Nabuasa ◽

Noorce Ch Berek ◽

Agus Setyobudi

Keyword(s):

Lung Function ◽

Nutritional Status ◽

Working Environment ◽

Step Test ◽

Smoking Habits ◽

Wood Dust ◽

Height Measurement ◽

Cross Sectional ◽

Wood Furniture ◽

Wooden Furniture

Workers of wooden furniture is the workers who are at risk for decreased lung function caused by exposure to wood dust in the working environment. Wood dust will enter the respiratory organs, thereby affecting lung function. Decreased pulmonary function can be seen by the method of Harvard Step Test. This research aims to analyse the relationship between age, working period, nutritional status, smoking habits, long exposure to dust, and use of PPE with lung function in wooden furniture workers in Oesapa Village, Kelapa Lima Sub District, Kupang City. The study was analytic survey with Cross Sectional approach. This research was conducted in the wood furniture industry in Oesapa Vilage, Kelapa Lima District, Kupang City in July 2020. The population in this study was 33 workers of wooden furniture. Data collection is done by questionnaire, weight measurement, height measurement and the Harvard Step Test to determine the level of lung fitness workers. Data analysis technique used is the Chi Square test with level of significance . The results showed that there was a significant relationship between age (0.002), working period (0.023), nutritional status (0.039), and use of PPE (0.016) with lung function in workers of wooden furniture. There is no relationship smoking habits (0.093), long exposureto dust (0.057) and lung function in workers of wooden furniture.

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