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Dec 8, 2016

The Analysis of Variation Factors in Impregnated Retardants to Improve Quality in Retardant...

(English follows Japanese)


東京理科大学理工学部経営工学科 助教 安井 清一
東京理科大学理工学部経営工学科 教授 大宮 喜文




本研究では、ある生産者での過去約2年間にわたる加圧注入加工(バッチ処理)に関する実績データに対して、JMPを用いて探索的なデータ解析を実施し、含浸量のばらつきの要因を解析した。バッチにおける平均効果(location effects)、分散効果(dispersion effects)を特定することができ、加工条件を定めるための統計モデルが構築できた。また、当該生産者が過去に行った対策効果をデータから検証している。



The Analysis of Variation Factors in Impregnated Retardants to Improve Quality in Retardant Lumber Production

Seiichi Yasui, Assistant Professor, Department of Management Engineering, Faculty of Science, Tokyo University of Science
Yoshifumi Ohmiya, Professor, Department of Architecture, Faculty of Science, Tokyo University of Science (Co-Author)


The use of lumber in various buildings has been expected in recent years from both regional economy and environmental protection perspectives. However, lumber is combustible, and it is necessary to adequately ensure that the materials are fireproof. To that end, pressure injection is performed to impregnate retardants into lumber under high pressure. Lumber treated in this way is referred to as retardant lumber. Under ideal circumstances, the target amount of the retardant is impregnated; however, impregnation variation is widely reported in actual production settings. If a high target value is established in pressure injection processing, the rate of non-conforming products decreases; however, the percentage of excess also increases, which can decrease functionality outside of fire resistance (i.e. quality). Thus, decreasing variation in the amount of retardant that is impregnated through pressure injection is an important issue in the production of retardant lumber. We took actual data related to pressure injection processing (batch treatment) over the past two years from a certain producer, and using JMP to perform exploratory data analysis and analyze impregnation variation factors. We identified location effects and dispersion effects in the batches, and established a statistical model to stipulate processing conditions. With this data, we are validating the effects of the measures that the producer took in the past. Our report shows the relationship between the JMP database operations and analysis tools, and provides a data analysis case example for improving quality in retardant lumber production.

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