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Please use this identifier to cite or link to this item: http://hdl.handle.net/10112/6780

Title: 多工程組み立て作業に対する作業分析の研究 : 第2報:作業困難さを考慮する組み立て順序決定法
Other Titles: Analysis of Work Operations for Multi-Step Assembly Tasks : 2nd Report: Procedure to Determine Assembly Sequence Considering Work Difficulties
Authors: 荒川, 雅裕
田伏, 顕也
冬木, 正彦
Author's alias: ARAKAWA, Masahiro
TABUSHI, Akinari
FUYUKI, Masahiko
Keywords: Assembly
Operation sequence
Genetic algorithm
Operation difficulty
Neural network
Issue Date: 15-Jun-2007
Publisher: 日本経営工学会
Shimei: 日本経営工学会論文誌
Volume: 58
Issue: 2
Start page: 136
End page: 146
Abstract: 本研究では,作業者の手作業による部品組み立て作業に対して,作業困難さを考慮する部品組み立て順序の決定問題を取り扱う.既報の実験結果では,実際の作業時間はMODAPTS法によって算出される標準作業時間に比べて大きくそれらの時間差は部品組み立て作業における作業時間は作業者の作業姿勢,作業者の部品や中間構造物の支持方法によって影響することを示した.この結果を踏まえて,組み立て作業の過程における作業困難さを評価し,ニューラルネットワークによって標準作業時間の補正を組み込んだ,遺伝的アルゴリズムによる部品組み立て作業順序決定方法を提案する.本論文では,はじめに部品組み立て作業に対する一般的動作の特徴の分類,および動作と作業の構造化を議論する.その後に,提案法の特徴を示し,具体的な構造物の組み立て作業に対する順序決定問題への適用例を示す. The operation time of multi-step assembly tasks is affected by work difficulties. When an assembly process includes difficult operations, a long processing time is required to complete all operations. Therefore, considering operation difficulties in the task is required to accurately predict the operation time of an assembly task. Our previous paper shows that the operation time in multi-step assembly tasks is affected by a combination of work difficulties such as worker's posture, worker's status of supports for a semi-final product, and the number of bolts required to affix the parts. In this paper, we review a problem for determining the operation sequence of an assembly task and propose a method to search for the optimal sequence by evaluating different types of work difficulties. The proposed method is based on a genetic algorithm and it includes a function to modify a standard operation time obtained using the MODAPTS method. A neural network is introduced to calculate the rating to modify the standard operation time. Evaluations of work difficulties are used as input data of the neural network to calculate the operation time of each operation separated in the sequence. To apply the proposed method, operations and worker motions appearing in an assembly process are categorized and related to each other. Then, the proposed method is applied to an actual assembly process to evaluate its performance. The results show that the best assembly sequences obtained by the proposed method give shorter operation times than the other sequences, and the operation times of the best sequences approximate to the measured operation times.
type: Journal Article
Rights: Copyright: 日本経営工学会
URI: http://hdl.handle.net/10112/6780
ISSN: 13422618
NCID: AN10561806
Text Version: publisher
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