技术
- 分析与建模 - 计算机视觉软件
- 分析与建模 - 机器学习
适用行业
- 电子产品
适用功能
- 离散制造
- 质量保证
用例
- 计算机视觉
- 视觉质量检测
服务
- 系统集成
挑战
对于一家3C产品供应链企业来说,人工目检每月人工成本超过200万,质检人员占比20%~40%,存在漏检等质量问题。目前,人工质检面临质量、成本、特殊场景响应、信息整合等问题。详细情况如下:
- 质量:人工质检的主观因素对判断结果影响很大,会出现基于视觉疲劳的漏检问题;某3C产品供应链企业生产现状。
- 信息整合:生产数据没有有效积累和利用,无法推进后续流程再造和质量分析,对自动化生产流程的适应能力弱。
- 成本:人员流动性高,导致培训和人工成本高,招工难。
- 效率:传统机器视觉采用程序化计算逻辑进行视觉检测,对复杂的表面检测抗干扰能力差,误检率高,人员重新判断工作量大。
解决方案
5G+AI外观质检方案,融合AI深度视觉检测技术和5G通信技术。
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