東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Statistical procedures for developme...

Staudhammer, Christina.

Linked to FindBook

Google Book

Amazon

博客來

Statistical procedures for development of real-time statistical process control (SPC) in lumber manufacturing.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical procedures for development of real-time statistical process control (SPC) in lumber manufacturing./
Author:	Staudhammer, Christina.
Description:	228 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0640.
Contained By:	Dissertation Abstracts International66-02B.
Subject:	Agriculture, Wood Technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ99556
ISBN:	9780612995567

Statistical procedures for development of real-time statistical process control (SPC) in lumber manufacturing.
Staudhammer, Christina.

Statistical procedures for development of real-time statistical process control (SPC) in lumber manufacturing. - 228 p.

Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0640.

Thesis (Ph.D.)--The University of British Columbia (Canada), 2005.

High raw material costs and reduced allowable forest harvest levels have created challenges for the Canadian lumber industry. Sawlogs typically comprise 75% of all the costs in a sawmill and insufficient log availability is a widespread problem. Thus, maximum product value and yield from every log processed is an urgent priority.

ISBN: 9780612995567Subjects--Topical Terms:

1031154
Agriculture, Wood Technology.

Statistical procedures for development of real-time statistical process control (SPC) in lumber manufacturing.
LDR:03558nmm 2200337 4500 001 1823407
005 20061130142438.5
008 130610s2005 eng d
020 $a 9780612995567
035 $a (UnM)AAINQ99556
035 $a AAINQ99556
040 $a UnM $c UnM
100 1 $a Staudhammer, Christina. $3 1912516
245 1 0 $a Statistical procedures for development of real-time statistical process control (SPC) in lumber manufacturing.
300 $a 228 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0640.
500 $a Advisers: Thomas Maness; Robert Kozak.
502 $a Thesis (Ph.D.)--The University of British Columbia (Canada), 2005.
520 $a High raw material costs and reduced allowable forest harvest levels have created challenges for the Canadian lumber industry. Sawlogs typically comprise 75% of all the costs in a sawmill and insufficient log availability is a widespread problem. Thus, maximum product value and yield from every log processed is an urgent priority.
520 $a Effective statistical process control (SPC) procedures can greatly enhance product value and yield, ensuring accuracy and minimum waste. However, present procedures are manual in nature. The time and effort required means that only small data samples are collected at infrequent intervals, seriously limiting quality control effectiveness. Attempts to implement automated SPC with non-contact laser range sensors (LRS) have thus far had only limited success. Such systems have given frequent false alarms, prompting tolerances to be set excessively wide. Thus, real problems are often missed for extended periods.
520 $a The objective of this research was to establish a system for collecting and processing real-time LRS size control data for automated lumber manufacturing. An SPC system was developed that incorporated multi-sensor data filtering procedures, a model with complex structure, and new control charting procedures. The LRS data were first filtered for measurement errors using techniques from image processing. Non-sawing defects were then removed from the data using a sheet-of-light profiling system and defect recognition algorithm. Defect-free filtered data were modeled in a multi-stage process, which explicitly considered multiple sources of variation and a complex correlative structure. New SPC charts were developed that went beyond traditional size control methods, simultaneously monitoring multiple surfaces and specifically targeting common sawing defects.
520 $a Nineteen candidate control charts were evaluated. For some sawing defects (e.g., machine positioning errors and wedge), traditional X-bar and range charts are suggested. These charts were explicitly developed to take into account the components of variance in the model. For other sawing defects (e.g., taper, snipe, flare, and snake), control charts are suggested that are non-traditional. The charts that target these defects were based on the decomposition of LRS measurements into trend, waviness, and roughness.
520 $a Applying these methods will lead to process improvements in sawmills, so that machines producing defective material can be identified, allowing prompt repairs to be made.
590 $a School code: 2500.
650 4 $a Agriculture, Wood Technology. $3 1031154
650 4 $a Operations Research. $3 626629
690 $a 0746
690 $a 0796
710 2 0 $a The University of British Columbia (Canada). $3 626643
773 0 $t Dissertation Abstracts International $g 66-02B.
790 1 0 $a Maness, Thomas, $e advisor
790 1 0 $a Kozak, Robert, $e advisor
790 $a 2500
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NQ99556