ISO/TR 16015:2003
Geometrical product specifications (GPS) — Systematic errors and contributions to measurement uncertainty of length measurement due to thermal influences
发布时间:2003-04-03 实施时间:


在进行长度测量时,热影响是一个常见的问题。温度变化会导致测量设备和被测物体的尺寸发生变化,从而导致测量误差。这些误差可能会对产品的质量和可靠性产生负面影响。因此,需要对这些误差进行评估和控制。

ISO/TR 16015:2003提供了一种方法,以确定由于温度变化而引起的长度测量误差。该方法包括以下步骤:

1. 确定测量设备和被测物体的温度范围。
2. 确定测量设备和被测物体的温度系数。
3. 计算温度变化引起的长度变化。
4. 计算由于温度变化引起的测量误差。

该标准还提供了一些建议,以减少由于温度变化引起的测量误差。这些建议包括:

1. 在测量之前,将测量设备和被测物体的温度稳定在一个恒定的温度下。
2. 使用温度补偿技术来减少测量误差。
3. 对测量设备进行定期校准,以确保其准确性和稳定性。

ISO/TR 16015:2003还提供了一些示例,以说明如何使用该方法来评估由于温度变化而引起的测量误差。这些示例包括:

1. 使用热源来模拟温度变化,并测量由于温度变化而引起的长度变化。
2. 在不同温度下测量相同的被测物体,并比较测量结果。

总之,ISO/TR 16015:2003提供了一种方法,以确定由于温度变化而引起的长度测量误差,并提供了一些建议,以减少这些误差。这些方法和建议可以帮助企业提高产品的质量和可靠性,从而提高客户的满意度。

相关标准
- ISO 14253-1:1998 Geometrical product specifications (GPS) — Inspection by measurement of workpieces and measuring equipment — Part 1: Decision rules for proving conformity or non-conformity with specifications
- ISO 14253-2:2011 Geometrical product specifications (GPS) — Inspection by measurement of workpieces and measuring equipment — Part 2: Guidance for the estimation of uncertainty in GPS measurement, in calibration of measuring equipment and in product verification
- ISO 14253-3:2011 Geometrical product specifications (GPS) — Inspection by measurement of workpieces and measuring equipment — Part 3: Guidelines for achieving agreements on measurement uncertainty statements
- ISO 14253-4:2010 Geometrical product specifications (GPS) — Inspection by measurement of workpieces and measuring equipment — Part 4: Background on functional limits and specification limits in decision rules for verifying conformity or nonconformity with specifications
- ISO 14253-5:2018 Geometrical product specifications (GPS) — Inspection by measurement of workpieces and measuring equipment — Part 5: Decision rules for proving conformity or non-conformity with specifications for features of size based on maximum material requirement (MMR), least material requirement (LMR) and regardless of feature size (RFS)