Milling sticks : Evolving methodologies to fabricate complex traditional Japanese timber joints for use in light timber structures
Bibliographic record
Abstract
The role of a carpenter in traditional Japanese architecture is as much an architect as a craftsman. Japanese joinery created by miyadaiku carpenters (carpentry techniques using interlocking wood without nails) allows timber connections to be resolved by sophisticated joints without relying on mechanical fastenings. This approach to timber design has been largely lost with the adoption of mechanical fastenings such as nail or screw connections, especially in the construction of structures with many small timber members. Essentially, this is the result of the expensive labour cost associated with making timber joinery, leaving this craft to be the reserve of high-end furniture; it is no longer seen in the realm of 21st-century building. Sophisticated timber joints are still used in structures with large timber members, costing thousands of dollars. Using a CNC (computer numerical control) to mill the joints accurately with timber of this scale, the time and set up of the CNC milling is justified by the cost of the member being cut. However, a gap in the application of this technology exists regarding small timber members, costing a fraction of what larger members do. As a result of this, the cost to set up the CNC cannot be justified, and screws are used. The question focusing the research is ‘How can simple three-axis CNC milling be utilised to fabricate traditional Japanese timber joints, creating viable timber-to-timber connections on small members?’ and secondly ‘How might this methodology be applied to a live project?’ Research into this field has been undertaken by previous timber structures thesis supervised by Andrew Barrie, such as Dylan Waddell’s Shadow Pavilion (2019), which devised a jig to hold and consistently cut many small pieces of timber by locating sticks on the CNC. This massively reduced the set-up time associated with milling each stick. Kanade Konoshi’s Watari-Ago Shelter (2020) evolved this jig using toggles to make moving the stick within the jig even faster and more accurate. Both projects successfully milled one face of the timber to create lapped and cog joints; the issue is the limitation of what type of joints can be fabricated when only one face of the timber is cut. In this thesis, a new jig methodology is devised to cut multiple face of the timber so that more sophisticated and complex joints can be fabricated. To interrogate and test the methodology, a 12m2 forest classroom constructed from a lattice of small timber members, connected by Kashigi-orie joints, has been built for a client in Papamoa. This serves as a built example of how the technology can be applied to live projects, dispelling the notion that structures of this scale and budget must rely on mechanical fastenings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".