solutions
变速器制造:汽车动力总成
Sciemetric 针对汽车动力总成采用的变速器制造和检测方法以过程检测和监控为基础,可提高质量和合格率,减少缺陷品。
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轴制造:汽车动力总成
在一条典型的轴生产线上,即从组装到最终检测,至少有 20 个点可用于监控检测过程。大多数制造商无法做到对上述每一个点进行监控,无形中导致缺陷产品的产生。Sciemetric 可解决这一问题。
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Functional Test of Assemblies and Subassemblies
Test the function and performance of complex sub-assemblies and parts with Sciemetric’s real-time process monitoring and multi-channel signature analysis.
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Use Data to Optimize Manufacturing Leak Test
Many manufacturers rely on leak test as an end-of-line test. However, Sciemetric’s approach to in-process testing allows you to monitor leak test at critical junctions along the manufacturing line so that defects are found as soon as possible.
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Centralize Process Data for Quick, Industry 4.0 Insight
Data left trapped in silos across the plant floor leaves a team blind to issues that impact quality, yield and profitability. Centralizing all process data into a common repository yields a powerful resource for achieving Industry 4.0 benchmarks.
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Improve Root-Cause Analysis With Manufacturing Data
Use manufacturing data from in-process testing, process monitoring and digital signature analysis to trace root cause and contain quality spills with Sciemetric.
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定制机器:整体制造工作站
Sciemetric 提供基于我司技术的定制机器,以满足客户的特定需求。
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Monitor Adhesive and Sealant Dispensing to Catch Defects
Sciemetric’s sigPOD uses digital process signature analysis to measure the dispensing of sealants, coatings and adhesives. It captures dispensing defects such as bubbles, air voids, partial hardening and issues such as low pressure, high pressure and dispensing time in real-time.
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Manufacturing Traceability
Collect, access, report and analyze part production data with a full traceability record for every manufactured part using Sciemetric’s data collection and analytics.
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Case Study: Manufacturer uses production data to overhaul quality on a global scale
End-of-line testing is costly and unreliable. What’s more, it doesn’t generate sufficient data to support advanced analytics. For one leading automotive OEM, a reliance on EoL meant that they weren’t fully realizing the data-driven potential of a connected Industry 4.0 environment. When this customer decided to take a harder look at standardizing quality across its international brands, it intensified testing beyond EoL and, in the process, unleashed a torrent of data that, once properly managed, made improvements they could only have hoped for. Read the case study to learn more.
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