Complexity of products and their assembly systems
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".