Complexity of products and their assembly systems
Notice bibliographique
Résumé
Many manufacturing and assembly challenges emerged due to the increased demand for products variety. Increased product variety caused by product evolution, customization and changes in their manufacturing systems. Variety allows manufacturers to satisfy a wide range of customer requirements, but it can also be a major contributing factor to complexity of assembly. Complexity is generally believed to be one of the main causes of the present challenges in manufacturing systems. Complex assembly systems are costly to implement, run, control and maintain. Complexity of assembly is an important characteristic worth exploring and modeling in the early design stage. Assessing complexity of a product is essential in being able to predict the cost and time needed to implement it. There is a relationship between the complexity of assembled products and the complexity of their assembly equipment and systems. The main objective of this research is to the complexity of assembly by: (1) Assessing the complexity of assembled products, (2) Assessing the complexity of their assembly systems, and (3) Derive the relationship between products and assembly systems complexities. First, a product complexity model has been developed by incorporating the information amount, content and diversity as well as the Design for Ease of Assembly (DFA) principles for assembled products. The new product complexity model assesses the total product assembly complexity using aggregated index for individual parts complexity. The new measure accounts for the different parts’ assembly attributes as well as their number and variety. Second, a structural classification coding (SCC) scheme has been extended to measure assembly systems complexity. It considers the inherent structural complexity of typical assembly equipment. The derived assembly system’s complexity accounts for the number, diversity and information content within each class of assembly system modules. Third, a dependency matrix which represents the interactions between parts assembly attributes and assembly system functions has been developed. It is used to predict the complexity of corresponding assembly equipment used for a certain product. A relationship between parts complexity and assembly equipment complexity has been developed using regression analysis. This research is applicable to the mechanical assembly of medium size products. An automobile piston, a domestic appliance drive, a car fan motor and a family of three-pin electric power plugs and their assembly systems were used as case studies to demonstrate the proposed approach and complexity assessment tools. The significance and importance of these research contributions is that: the developed complexity metrics can be used as decision support tools for products and systems designers to compare and rationalize various alternatives and select the design that meets the requirements while reducing potential assembly complexity and associated cost. Assessing complexity of assembly helps and guides designers in creating assembly-oriented product designs and following steps to reduce and manage sources of assembly complexity. On the other hand, reducing complexity of assembly helps lower assembly cost and time, improve productivity and quality, and increase profitability and competitiveness.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».