Using Basic Quality Tools to Improve Production Yields and Product Quality in Manufacturing

As the U.S. and world economies emerge from years of recession, the hardwood flooring market is currently enjoying strong growth. With this growth come new challenges for hardwood flooring manufacturers. Strong competition from foreign markets and rising log prices are reducing product margins and forcing companies to think lean, while improving product quality. QEP Wood Flooring division, who struggled through the worst of the U.S. economic down turn is now regaining ground as a strong competitor in the hardwood flooring market. This turnaround is due to internal changes to decrease waste and increase product quality. This is accomplished by using the quality control department as a tool to aid manufacturing. To accomplish these changes, QEP implemented the use of quality tools and employee awareness training; as a result QEP increased overall product quality and yields while reducing customer claim pay outs

Sensors in agriculture and forestry

Agriculture and Forestry are two broad and promising areas demanding technological solutions with the aim of increasing production or accurate inventories for sustainability while the environmental impact is minimized by reducing the application of agro-chemicals and increasing the use of environmental friendly agronomical practices. In addition, the immediate consequence of this “trend” is the reduction of production costs. Sensors-based technologies provide appropriate tools to achieve the above mentioned goals. The explosive technological advances and development in recent years enormously facilitates the attainment of these objectives removing many barriers for their implementation, including the reservations expressed by the farmers themselves. Precision Agriculture is an emerging area where sensor-based technologies play an important role.RHEA project [42], which is funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement NO.245986, which has been the platform for the two international conferences on Robotics and associated High-technologies and Equipment mentioned above.Peer Reviewe

Development of a 3D log processing optimization system for small-scale sawmills to maximize profits and yields from central appalachian hardwoods

The current status of log sawing practices in small hardwood sawmills across West Virginia was investigated and the effects of log sawing practices on lumber recovery evaluated. A total of 230 logs two species, red oak (Quercus rubra) and yellow-poplar (Liriodendron tulipifera), were measured in five typical hardwood sawmills in the state. Log characteristics such as length, diameter, sweep, taper, and ellipticality were measured. Additionally, the characteristics of sawing equipment such as headrig type, headrig kerf width, and sawing thickness variation were recorded. A general linear model (GLM) was developed using Statistical Analysis System (SAS) to analyze the relationship between lumber recovery and the characteristics of logs and sawing equipment for small sawmills in West Virginia. The results showed that the factors of log grade, log diameter, species, log sweep, log length, different sawmills, the interaction between log species and grade, and the interaction between log species and log length had significant impacts on volume recovery. Log grade, log species and headrig type had significant effects on value recovery.;Hardwood lumber production includes a sequence of interrelated operations. Methods to optimize the entire lumber production process and increase lumber recovery are important issues for forest products manufacturers. Therefore, a 3D log sawing optimization system was developed to perform 3D log generation, opening face determination, headrig log sawing simulation, cant resawing, and lumber grading. External log characteristics such as length, largeend and small-end diameters, diameters at each foot, and external defects were collected from five local sawmills in central Appalachia. The positions and shapes of internal log defects were predicted using a model developed by the USDA Forest Service. 3D modeling techniques were applied to reconstruct a 3D virtual log that included internal defects. Heuristic and dynamic programming algorithms were developed to determine the opening face and grade sawing optimization. The National Hardwood Lumber Association (NHLA) grading rules were computerized and incorporated into the system to perform lumber grading. Preliminary results have shown that hardwood sawmills have the potential to increase lumber value by determining the optimal opening face and optimizing the sawing patterns. Our study showed that without flitch edging and trimming, the average lumber value recovery in the sawmills could be increased by 10.01 percent using a heuristic algorithm or 14.21 percent using a dynamic programming algorithm, respectively. An optimal 3D visualization system was developed for edging and trimming of rough lumber in central Appalachian. Exhaustive search procedures and a dynamic programming algorithm were employed to achieve the optimal edging and trimming solution, respectively.;An optimal procedure was also developed to grade hardwood lumber based on the National Hardwood Lumber Association (NHLA) grading rules. The system was validated through comparisons of the total lumber value generated by the system as compared to values obtained at six local sawmills. A total of 360 boards were measured for specific characteristics including board dimensions, defects, shapes, wane and the results of edging and trimming for each board. Results indicated that lumber value and surface measure from six sawmills could be increased on average by 19.97 percent and 6.2 percent, respectively, by comparing the optimal edging and trimming system with real sawmill operations.;A combined optimal edging and trimming algorithm was embedded as a component in the 3D log sawing optimization system. Multiple sawing methods are allowed in the combined system, including live sawing, cant sawing, grade sawing, and multi-thickness sawing. The system was tested using field data collected at local sawmills in the central Appalachian region. Results showed that significant gains in lumber value recovery can be achieved by using the 3D log sawing system as compared to current sawmill practices. By combining primary log sawing and flitch edging and trimming in a system, better solutions were obtained than when using the model that only considered primary log sawing. The resulting computer optimization system can assist hardwood sawmill managers and production personnel in efficiently utilizing raw materials and increasing their overall competitiveness in the forest products market

Computer vision-based monitoring of abrasive loading during wood machining

Surface quality is an important characteristic commonly assessed in wooden products. Sanding relies on coated abrasives as tooling for both dimensioning and surface finishing but their performance is dependent on chip loading and grit wear. Traditionally, the useful life of abrasive belts in sanding operation has been manually assessed. This type of inspection is highly subjective and dependent upon individual expertise, consequently leading to under utilization or over utilization of the abrasive. This, in turn, affects the production costs and quality of the product. In this work, an intelligent classification method that determines the optimal replacement policy for a belt exposed to known manufacturing parameters is developed. Controlled experiments were conducted to develop abrasive belts of known exposure, followed with digital microscopy to capture images and process them with pattern recognition and classification algorithms. Grit size and machining time were the parameters of interest while response of the experiments included image information from the abrasive sheets after every experimental run. These images were used in training an artificial neural network that in turn, help in determining data to categorize the useful life of the abrasive. The results show a 95% success rate in accurately classifying abrasive images of similarly conditioned abrasives. Also, the results show that the classification of interpolated and extrapolated times of abrasive usage are classified with a 95% success rate. A classification of abrasive images is proposed to be used as one of the inputs to a decision system that would help in evaluating the life of the abrasive and replacement policies. Further research on the relationship between the different parameters affecting the useful life of the abrasive is proposed

Production Engineering and Management

The annual International Conference on Production Engineering and Management takes place for the sixth time his year, and can therefore be considered a well - established event that is the result of the joint effort of the OWL University of Applied Sciences and the University of Trieste. The conference has been established as an annual meeting under the Double Degree Master Program ‘Production Engineering and Management’ by the two partner universities. The main goal of the conference is to provide an opportunity for students, researchers and professionals from Germany, Italy and abroad, to meet and exchange information, discuss experiences, specific practices and technical solutions used in planning, design and management of production and service systems. In addition, the conference is a platform aimed at presenting research projects, introducing young academics to the tradition of Symposiums and promoting the exchange of ideas between the industry and the academy. Especially the contributions of successful graduates of the Double Degree Master Program ‘Production Engineering and Management’ and those of other postgraduate researchers from several European countries have been enforced. This year’s special focus is on Direct Digital Manufacturing in the context of Industry 4.0, a topic of great interest for the global industry. The concept is spreading, but the actual solutions must be presented in order to highlight the practical benefits to industry and customers. Indeed, as Henning Banthien, Secretary General of the German ‘Plattform Industrie 4.0’ project office, has recently remarked, “Industry 4.0 requires a close alliance amongst the private sector, academia, politics and trade unions” in order to be “translated into practice and be implemented now”. PEM 2016 takes place between September 29 and 30, 2016 at the OWL University of Applied Sciences in Lemgo. The program is defined by the Organizing and Scientific Committees and clustered into scientific sessions covering topics of main interest and importance to the participants of the conference. The scientific sessions deal with technical and engineering issues, as well as management topics, and include contributions by researchers from academia and industry. The extended abstracts and full papers of the contributions underwent a double - blind review process. The 24 accepted presentations are assigned, according to their subject, to one of the following sessions: ‘Direct Digital Manufacturing in the Context of Industry 4.0’, ‘Industrial Engineering and Lean Management’, ‘Management Techniques and Methodologies’, ‘Wood Processing Technologies and Furniture Production’ and ‘Innovation Techniques and Methodologies

Within-Tree Variability of Wood Color in Paper Birch in Québec

Color variations in paper birch wood were examined in boards sawn from sawlogs from 168 trees harvested from two different stands. Approximately 2250 boards were sawn from the logs. The within-tree variability was considered by looking at the effect of log quality and log height class on board color. Results show that neither the log quality nor the log height class had a significant effect on the proportion of discolored wood on the surface of the board. However, these log parameters had an effect on the wood colorimetric variables. Log position in the tree was found to significantly influence sapwood yellowness as well as discolored wood luminosity and redness. Log quality on the other hand significantly influenced only one colorimetric factor, sapwood redness

Image analysis to assess wood variability in longleaf pine cross-sectional disks

Image analysis is an important method for rapidly measuring wood property variation, but it is infrequently applied to disks collected from forestry studies. The objective of this study was to compare image estimated wood and bark volumes and diameters to reference measurements, and to extract more information from the images including the shape (out of round index, eccentric pith) and the amount and location of severe compression wood. A total of 1,120 disks were cut from multiple height levels of 48 defect-free and 56 defect-containing longleaf pine (Pinus palustris) trees from 16 stands across Georgia (U.S.). Disks were machined on one transverse surface using a computer numeric controlled router to prepare a clean surface for imaging. Three images; one under white light, second under blue light, and third under blue light with a green longpass filter, were taken for each disk. Volumes and diameters estimated from images were in close agreement with reference methods. Linear models fitted as measured versus image volumes for wood and bark had coefficient of determination (R2) values of >0.99 and 0.96. Linear models fitted as measured versus image diameters had R2 values of >0.99. Out of round index and pith eccentricity values calculated from images showed a moderate positive correlation (R=0.43). Algorithms developed were able to correctly identify severe compression wood, but not mild to moderate compression wood. Severe compression wood was moderately correlated to out of round index (R=0.54) and pith eccentricity (R=0.48). More than 98% of the disks having severe compression wood came from defect-containing trees

Thermally modified hybrid aspen

This thesis is focused on thermal modification of hybrid aspen (Populus tremula L. x Populus tremuloides Michx.), its mechanical properties, wood defects and potential use in construction. In the recent years, wood as a building material has become increasingly popular. It is preferred because of its aesthetic appeal; it is renewable and substitutes fossil-based energy consuming materials. However, greater use of wooden products can also put more pressure on our natural forests. The main problems discussed in this paper were: mechanical characteristics after intense thermal modification and overall quality characteristics of thermally modified hybrid aspen. The present thesis conducts an empirical research of harvesting model trees, band sawing them into boards and kiln-drying, in order to prepare samples for thermal modification. Specimens were prepared for compressive strength, bending strength and wood density testing. Mechanical tests were carried out following the standards of ISO 13061-17, ISO 13061-3 and ISO 13061-2, accordingly. Furthermore, thermally treated sample boards from hybrid aspen (Populus tremula L. x Populus tremuloides Michx.), were evaluated according to 14 predetermined quality characteristics. Thermally modified hybrid aspen showed lower wood density and lower bending strength, but higher compressive strength in comparison with untreated hybrid aspen material. Wood quality evaluation showed that hybrid aspen had almost two times more wood defects compared to European aspen. Thermal modification process had statistically insignificant effect on measured wood quality. As a result, it can be said, that despite the decrease of some mechanical properties, thermally modified hybrid aspen still has a potential in areas where load bearing abilities are not so important, for example sauna interiors and interior panelling

Statistical Process Control based on Multivariate Image Analysis: A new proposal for monitoring and defect detection

The monitoring, fault detection and visualization of defects are a strategic issue for product quality. This paper presents a novel methodology based on the integration of textural Multivariate image analysis (MIA) and multivariate statistical process control (MSPC) for process monitoring. The proposed approach combines MIA and p-control charts, as well as T2 and RSS images for defect location and visualization. Simulated images of steel plates are used to illustrate the monitoring performance of it. Both approaches are also applied on real clover images.The authors want to thank Ole Mathis Kruse and Prof. Cecilia Futsaether, from the Norwegian University of Life Sciences (Dept. of Mathematic Sciences and Technology), for providing the real image data set. This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI 2011-28112-C04-02.Prats Montalbán, JM.; Ferrer Riquelme, AJ. (2014). Statistical Process Control based on Multivariate Image Analysis: A new proposal for monitoring and defect detection. Computers and Chemical Engineering. 71:501-511. https://doi.org/10.1016/j.compchemeng.2014.09.014S5015117