结论：型式检验证明「能做到」，一致性数据证明「每批都做到」
型式检验用的是送检的那几台样品，它证明的是：这个设计、这个工艺，能够做出符合要求的产品。
但它不能证明后续生产的每一批都符合。 材料批次变化、设备状态变化、操作人员变化、环境变化，都可能导致批次之间的差异。
所以「型式检验合格」与「交付的这批产品合格」之间，存在一个需要填补的缺口。 填补这个缺口的，是批次一致性的证据。
一致性的含义
批次一致性关注的是：
批内一致性。 同一批产品之间的差异。
批间一致性。 不同批次之间的差异。
随时间的稳定性。 长期生产中质量是否稳定。
关键特性的波动范围。 重要参数的分散程度。
与规格限值的关系。 波动范围与规格限值之间的余量。
最后一项是实用的判断依据。 如果某个参数的批次波动范围接近规格限值的宽度，说明余量已经很小，偶尔超出规格是可以预期的。
可用的数据类型
| 数据来源 | 反映什么 |
|---|---|
| 出厂检验记录 | 每批的关键参数实测值 |
| 过程控制数据 | 生产过程中的参数波动 |
| 定期型式检验 | 周期性的全面验证 |
| 抽样检测报告 | 特定批次的第三方数据 |
| 原材料检验记录 | 输入端的一致性 |
| 不合格品记录 | 问题的发生率与类型 |
「出厂检验记录」是最直接的数据源，前提是检验项目覆盖了关键特性，且记录的是实测值而不只是合格与否。
只记录「合格」的检验记录，在一致性分析上价值有限，因为看不出波动范围和趋势。
抽样与统计的考虑
抽样的代表性。 覆盖不同批次、不同时间、不同设备或班次。
样本量。 足够反映波动。
参数的选择。 选择对产品性能影响大的、且有波动的参数。
统计量。 均值、标准差、极差、以及与规格限值的关系。
趋势。 随时间的变化，而不只是当前状态。
异常值的处理。 出现异常值时要分析原因，而不是简单剔除。
「趋势」比「当前状态」更有信息量。 当前所有批次都合格，但某参数的均值在逐批偏移，说明过程在漂移，迟早会超出规格。
关键特性的识别
不是所有参数都需要做一致性跟踪，应当聚焦关键特性：
影响安全的特性。 强度、稳定性、制动。
影响主要功能的特性。 续航、调节范围。
容易波动的特性。 受材料或工艺影响大的。
历史上出过问题的特性。 有前车之鉴的。
顾客关注的特性。 采购方明确要求的参数。
建议做一份关键特性清单，明确哪些参数需要逐批记录、哪些定期验证、哪些只在型式检验时做。清单做出来，检验资源的分配就有依据。
在采购中的应用
采购方可以通过几种方式获得一致性的信息：
要求提供出厂检验记录。 特定批次的实测数据。
要求提供历史数据。 一段时间内的质量记录。
约定定期抽检。 由采购方或第三方定期抽样检测。
现场审核。 查看生产和检验的实际运行。
建立质量档案。 记录历次交付的质量情况。
「要求提供出厂检验记录」是成本较低的做法，但前提是记录中有实测值。建议在合同中明确要求提供含实测数据的检验记录，而不只是合格证明。
供应商角度的准备
对供应商来说，能提供一致性数据是竞争优势：
建立完整的检验记录。 实测值而非仅结论。
做统计分析。 定期分析关键参数的波动。
建立过程控制。 在过程中控制而不只是终检。
主动提供数据。 在投标或交付时提供一致性证据。
持续改进。 用数据发现问题并改进。
「主动提供一致性数据」在采购比较中能形成差异，因为多数供应商只提供型式检验报告。
与检测委托的关系
第三方检测在一致性证明中的作用：
定期的型式检验。 周期性地全面验证。
批次抽检。 特定批次的独立验证。
对比检测。 不同批次的样品同时送检，直接比较。
问题批次的验证。 出现质量疑问时的独立判断。
「对比检测」是证明一致性的直接方法 ——把不同批次的样品在相同条件下同时测试，结果的差异直接反映批次一致性。这比分别送检然后比较报告更可靠，因为排除了不同时间测试带来的条件差异。
过程能力的概念
一致性分析中常用的一个概念是过程能力——它描述的是过程的自然波动与规格限值之间的关系。
波动远小于规格宽度。 过程能力好，偶发超标的概率低。
波动接近规格宽度。 过程能力勉强，超标是可以预期的。
波动超出规格宽度。 过程能力不足，必然有不合格品。
中心偏移。 即使波动小，如果均值偏离规格中心，靠近限值的一侧仍会超标。
这个概念的实用价值在于：它能预测问题而不只是发现问题。 过程能力不足的参数，即使当前批次合格，后续出现不合格也是可预期的。
建议对关键参数做过程能力的评估，据此决定是改进工艺还是加严检验。
引起批次差异的常见原因
原材料批次变化。 同一牌号的不同批次性能不完全一致。
供方变更。 二级供方换了而下游不知情。
设备状态。 模具磨损、夹具松动、参数漂移。
人员变动。 操作手法的差异。
环境变化。 温度湿度对某些工艺的影响。
工艺调整。 为改善效率或成本做的调整。
「二级供方变更」是最难追踪的一项，因为一级供方未必会告知。建议在采购合同中约定关键材料的供方变更告知义务。
我们的做法
承接批次相关的委托时，我们建议把不同批次的样品同时送检，在相同的条件、相同的设备、相同的时间段内测试 ——这样测出的差异才能归因于批次本身，而不是测试条件的差异。
报告方面，我们会给出每个样品的实测值而不只是批次的合格结论，因为一致性分析需要的是数据的分散情况，不是合格与否。
如果你需要做批次一致性的验证，或者在采购中需要相关的技术支持，可以把产品和关键参数发过来一起讨论，或者直接联系：132 4819 8029。检测能力见服务介绍，产品分类见手动轮椅检测与电动轮椅检测，案例见案例。
English version
Conclusion. Type testing uses the few samples submitted and demonstrates that this design and this process can produce conforming product. It cannot demonstrate that every subsequent batch conforms: changes in material batches, equipment condition, operators and environment all produce differences between batches. A gap therefore exists between type testing passed and this delivered batch conforms, and what fills that gap is evidence of batch consistency.
What consistency means. Within-batch consistency covers variation among units of one batch. Between-batch consistency covers variation across batches. Stability over time covers whether quality holds through prolonged production. The range of variation in key characteristics covers the spread of important parameters. And the relationship to specification limits covers the margin between that spread and the limits. The last is the practical criterion: where a parameter's batch-to-batch spread approaches the width of the specification, the margin is small and occasional excursions are to be expected.
Usable data sources. Final inspection records show measured values of key parameters per batch. Process control data show parameter variation during production. Periodic type testing provides comprehensive verification at intervals. Sampling test reports give third-party data on particular batches. Incoming material inspection records show consistency at the input end. And nonconformity records show the rate and type of problems. Final inspection records are the most direct source, provided the items cover the key characteristics and the records carry measured values rather than pass or fail alone. Records stating only conformity have limited value for consistency analysis, since neither spread nor trend can be seen.
Sampling and statistics. Sampling must be representative across batches, times, equipment and shifts. Sample size must suffice to reflect variation. Parameter selection should favour those that strongly affect performance and that do vary. Statistics should cover mean, standard deviation, range and the relationship to specification limits. Trends should be examined over time rather than only current state. And outliers should be investigated for cause rather than simply discarded. Trend carries more information than current state: all batches conforming while a parameter's mean drifts from batch to batch indicates process movement that will eventually exceed specification.
Identifying key characteristics. Not every parameter needs consistency tracking; focus on characteristics affecting safety, such as strength, stability and braking; characteristics affecting principal functions, such as range and adjustment range; characteristics prone to variation through material or process sensitivity; characteristics with a history of problems; and characteristics the customer cares about, meaning parameters the purchaser specified. Produce a key characteristic list stating which parameters are recorded per batch, which are verified periodically and which only at type testing. With the list, inspection resources have a basis for allocation.
Application in procurement. Purchasers can obtain consistency information in several ways. Requesting final inspection records gives measured data for particular batches. Requesting historical data gives quality records over a period. Agreeing periodic sampling allows the purchaser or a third party to test batches at intervals. On-site audit examines how production and inspection actually operate. And a quality file records the condition of successive deliveries. Requesting inspection records is the lowest-cost approach, provided the records carry measured values; specify in the contract that inspection records with measured data are required rather than conformity certificates alone.
Preparation from the supplier's side. For suppliers, being able to provide consistency data is a competitive advantage. Keep complete inspection records with measured values rather than conclusions. Perform statistical analysis of key parameter variation at intervals. Establish process control rather than relying on final inspection. Offer the data proactively when bidding or delivering. And improve continuously using the data to find problems. Offering consistency data proactively differentiates a supplier in procurement comparison, since most provide type test reports alone.
Role of third-party testing. Periodic type testing provides comprehensive verification at intervals. Batch sampling provides independent verification of particular batches. Comparative testing submits samples from different batches together for direct comparison. And verification of a suspect batch provides independent judgement when quality is questioned. Comparative testing is the direct method for demonstrating consistency: testing samples from different batches under identical conditions at the same time means that observed differences can be attributed to the batches themselves. It is more reliable than submitting separately and comparing reports, which carries differences in test conditions between occasions.
How we handle it. For batch-related work we suggest submitting samples from the different batches together, tested under the same conditions on the same equipment within the same period, so that measured differences can be attributed to the batches rather than to testing conditions. In reporting we give measured values for each sample rather than a batch conformity conclusion, because consistency analysis needs the spread of the data rather than pass or fail.
Send us the product and key parameters and we will work through the verification. Phone or WeChat: +86 132 4819 8029.