How do I call this tool?

POST a JSON-RPC 2.29 tools/call request to https://mackorn.cn/ai/api/mcp.php with name "mackorn_calibrate". No installation is required.

Is the model based on published literature?

Yes. The particle-size and flow models are independently implemented from Whiten (1972) and Bond (1952); the implementation is MIT-licensed and auditable. Vendor data comes from MACKORN parameter tables. Every number carries its source; unknown values return null rather than an estimate.

mackorn_calibrate — How do I calibrate the crusher model with our own plant sieve data?

What it answers

输入某工况的给料筛析与产品筛析（累积通过率 %）+ 该工况 CSS，用最小二乘（有界网格 + 局部细化）反算破碎函数参数 φ/γ/β 与啮合系数，给出拟合优度、逐点残差与可用性判断。

Input parameters

Parameter	Type	Description
`css_mm`	number	该工况的紧边排矿口 CSS mm（必填）。
`feed_points`	array	给料筛析：至少 2 个点。每点 {size_mm, cum_pct}（cum_pct 为累积通过 %）。
`product_points`	array	产品筛析：至少 3 个点（建议 ≥5）。
`throw_mm`	number	偏心距 mm（默认 25）。
`roughness`	number	拟合优度门槛（RMSE，默认 0.06），超过即告警。

Worked example (real output, not a mock-up)

Run via: local plugin

Input:

{
  "css_mm": 16,
  "feed_points": [
    {
      "size_mm": 20,
      "cum_pct": 80
    },
    {
      "size_mm": 120,
      "cum_pct": 10
    }
  ],
  "product_points": [
    {
      "size_mm": 5,
      "cum_pct": 40
    },
    {
      "size_mm": 10,
      "cum_pct": 70
    },
    {
      "size_mm": 20,
      "cum_pct": 92
    },
    {
      "size_mm": 30,
      "cum_pct": 99
    },
    {
      "size_mm": 40,
      "cum_pct": 100
    }
  ]
}

Output (first fields):

Field	Value
`calibrated.phi`	0.25
`calibrated.gamma`	1.7
`calibrated.beta`	7.6
`calibrated.throwFactor`	0.75
`calibrated.css_mm`	16
`calibrated.throw_mm`	25
`calibrated.identifiability`	参数不可辨识：有参数贴搜索边界，可能存在等效参数组
`fit.rmse_pct_points`	5.781
`fit.max_abs_error_pp`	12.37
`fit.evaluations`	1375
`fit.n_points`	5
`fit.quality`	可用（参数不可辨识，多解等效）
`fit.identifiability`	non-identifiable
`residuals`	[5 项]
`library_defaults.phi`	0.45
`library_defaults.gamma`	0.7
`library_defaults.beta`	3.5
`library_defaults.throwFactor`	0.8

How to call it

POST a JSON-RPC 2.0 request to the public MCP endpoint — no installation, no API key:

curl -s https://mackorn.cn/ai/api/mcp.php \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
       "params":{"name":"mackorn_calibrate","arguments":{"css_mm":16,"feed_points":[{"size_mm":20,"cum_pct":80},{"size_mm":120,"cum_pct":10}],"product_points":[{"size_mm":5,"cum_pct":40},{"size_mm":10,"cum_pct":70},{"size_mm":20,"cum_pct":92},{"size_mm":30,"cum_pct":99},{"size_mm":40,"cum_pct":100}]}}}'

Endpoint: https://mackorn.cn/ai/api/mcp.php · Registry: cn.mackorn/mackorn-cone-crusher · npm: mackorn-cone-crusher

Data sources and limits

Particle-size and flow models are independently implemented from published literature (Whiten 1972; Bond 1952). Vendor parameters come from MACKORN NH/NS tables. Every output carries assumptions[] and warnings[]; unknown values are returned as null, never estimated. Literature parameters must be calibrated against on-site sieve data before use in a formal proposal.

A hydraulic cone crusher is a secondary/tertiary machine: feed above roughly 350 mm is refused.