工业4.0已经得到了很多关注,因为他们希望利用连接加工信息和生产信息的优势,高效地生产出高质量的零件。随着4.0文化在模具行业的不断发展,模具维修部门开始问:“这对我有什么好处?”?”

维护专业人员需要了解4.0文化如何或是否能帮助他们更好地解决故障和维护模具,以及这些广泛的电子数据中哪一部分在短期和长期维护计划中都有用。这里是一个看电子效益,是可能的模具维修。

数据设备

在典型的消防维修文化中,工具室技术人员并不总是知道从哪里开始,所以他们从最后开始,然后向后工作。他们检查通过螺栓或电线连接到模具或压力机上的产品获得的有关成型参数的实时电子数据。挑战在于了解哪些数据有助于改进故障排除工作和纠正措施解决方案。

这些数据设备的五个基本功能是模具启动和停止的日期和时间、喷丸或循环计数(在使用寿命内或最近)、循环时间(最小/最大/平均)、模具/型腔传感器(注射压力、温度、熔体粘度和夹持压力)以及传感器或微动开关故障。这些特征通过一个电子信号捕获并反馈给许多主机系统,这些主机系统分析信息,然后以报告、图表或图表的形式反馈给用户,以提供当前模具或零件状况的线索。每个供应商配置数据,以“信号”信息的有用性,以成型操作的各个部分。

From there, a technician must determine the value of this data. At first glance, the data can serve as an alert function when something is amiss or when a technician is attempting to solve an issue with the mold, part, press or people. For example, the data indicates when a preventive maintenance (PM) run is due or overdue, how many cycles are run during a specific timeframe, if a mold cycle time is in or out of an accepted range, when the mold starts or stops, how long a machine is idle or down and when process conditions exceed a set range.

Once this data is implemented and tested, and technicians have determined a verified range, they can maintain a more consistent process, which helps them troubleshoot mold function and part quality issues and more accurately forecast tooling life. Without process consistency, it can be difficult—if not impossible—for technicians to determine a root cause, leaving them with few options, like simply cleaning the mold or sticking in new tooling and hoping the issue magically disappears.

有时这个问题确实消失了,这就为一个昂贵的消防维护策略提供了持续的支持。实际上,问题消失是因为过程中发生了一些变化,而不是新工具的加入。例如,注射压力在运行过程中出现峰值并导致飞边,有人增加夹紧压力并消除飞边,技术人员将喷嘴加热器调得过高,模具闲置时间过长,或者模具超过其指定的PM循环计数。电子数据将提供对这些情况的洞察,帮助技术人员确定正确的纠正措施。然而,仅仅依靠电子数据是不够的。

不仅仅是数据

It takes human interaction to fill in the blanks that these live data signals create. Before a toolroom tackles an issue, the technicians need to know exactly which issue that they should examine. It takes skilled technicians to prioritize the issues that they must investigate. Some larger companies have thousands of documented mold and part issues in their databases. Imagine trying to chase all the “out-of-range” issues that live electronic signals provide.

For example, what if during only one run, a host system was flagged indicating that a mold experienced a clamp or an injection pressure increase along with a cycle time and mold temperature change? What if the result of these changes were negligible and the mold still produced good parts during the run? What if the mold had a couple of cavities blocked for non-fill, flash or a burn during a run? The answer is unclear. The technician either sets up a DOE (Design of Experiment) to chase these process-related issues or hopes that they go away during the next production run. Perhaps the data is so confusing that the best answer is to stick in new tooling, give it a good cleaning and hope for the best.

尽管有些电子数据很有用,但这些信号不会告诉技术人员:

为什么霉菌停止了。

Where and when a runner or part got hung up.

物料状态。

The occurrence of the tooling or the component galling or locking up.

The corrective action that was taken, when it was performed, the technician who was responsible or the length of time that the action took.

纠正措施所需的工具。

缺陷发生的模具或型腔位置。

压力机门打开的原因(关闭操作)。

Which operator was on break, leaving the door open.

零件质量问题。

模具寿命.

The frequency and maintenance costs of the issues.

人类的参与将澄清这些问题。技术人员必须手动输入数据,以记录停止或减缓生产并产生不合格零件的问题。一旦问题被记录在案,技术人员就必须监控这些问题,以便根据频率、工具和劳动力成本以及关键的客户要求,准确地确定目标的优先级。

Typically, when a mold lands on a bench for PM and repair, a work order spells out the current issues that the repair technician must address. A continuous improvement culture requires technicians to identify what they need to fix now and the high-frequency or high-cost issues that have plagued the mold during past runs and the typical corrective actions. Technicians should also perform defect -osition analysis to identify defect patterns and trends quickly. This analysis is at the heart of a continuous improvement maintenance strategy, arming repair technicians with the right data to make more informed decisions.

归根结底,包括工具室经理在内的大多数模具工都忙于日常活动,无法追踪电子信号显示“超出范围”、“可疑”或“可能”导致问题的每个参数。工具室团队必须根据实际的模具性能问题(手动输入)设定目标和指标,以确保他们正在追踪的问题或根本原因分析值得努力。

Electronic data and the range of connectivity surely will improve a toolroom’s ability to produce quality parts on-time and efficiently, but in the world of maintenance, entries that are accurate and manual will always be required to give technicians the necessary information to attack real issues and measure real improvement.