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_wear_design". 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_wear_design — How should liner hardness be graded along the crushing chamber to even out wear?

What it answers

沿破碎腔高度离散截面评估衬板磨损均匀度（均匀度 = 1 − σ/μ），给出多梯度分区硬度设计 H_i = H_base × k^i 与均匀度提升量。

Input parameters

Parameter	Type	Description
`sections`	integer	沿腔高离散截面数（3-12，默认 5）。
`wear_rates`	array	各截面实测磨损速率（mm/1000h 或任意一致单位），长度应等于截面数。
`base_hardness`	number	基础分区硬度 HB（默认 58）。
`gradient_factor`	number	硬度梯度因子（默认 1.12）。

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

Run via: local plugin

Input:

{
  "sections": 5,
  "base_hardness": 58,
  "gradient_factor": 1.12
}

Output (first fields):

Field	Value
`sections`	5
`wear_rates_before`	[5 项] [1.8,3.07,3.6,3.07]
`wear_rates_graded_design`	[5 项] [2.75,2.72,2.67,2.61]
`uniformity_before.uniformity`	0.7246
`uniformity_before.cv`	0.2754
`uniformity_before.max_min_ratio`	2
`uniformity_after_gradient_design.uniformity`	0.977
`uniformity_after_gradient_design.cv`	0.023
`uniformity_after_gradient_design.max_min_ratio`	1.062
`improvement.uniformity_gain`	0.2524
`improvement.cv_reduction`	0.2524
`liner_hardness_schedule`	[5 项] [58,64.96,68,68]
`hardness_params.base`	58
`hardness_params.gradient_factor`	1.12
`recommendations`	[3 项] ["按 H_i 分区选材：下部细碎区用高梯度硬度，上部给料区保留一定韧性抗冲击","每 500 h 测厚一次并记录，用实测 wearRates 复算均匀度，形成本项目自学习基线","腔型一致性优先于单点寿命：磨损均匀度提升可减少 CSS 漂移与产品粒形波动"]
`assumptions`	[4 项]
`warnings`	[3 项] ["输入为合成示例数据：请用现场测厚记录填入 wearRates 后重算","分区硬度设计末段原始值 91.26 超过物理上限 68，已截断至上限（物理不可实现的梯度无工程意义）","最大/最小磨损速率比 2 偏大，衬板寿命由最薄处决定，须做分区硬度或腔型修型"]

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_wear_design","arguments":{"sections":5,"base_hardness":58,"gradient_factor":1.12}}}'

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.