Implementing Industry 4.0 by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9783030672706
Publisher: Springer International Publishing

In order to have predictive OEE in data analytics-based prediction, the assumptions have been made. First, frequency of quality measurement was reduced from 4 times to 1 time per day with quality measurement done offline after shafts are produced. Second, the durations of spindle unbalance and tailstock misalignment which caused the equipment unplanned down time were decreased from 8 and 16 h to 4 and 8 h quarterly respectively.

The OEE comparison has been conducted before and after data analytics-based prediction with cutting speed 70 m/min. Figure 8.16a, b show the detail process step timing change for each operation before and after prediction based on the assumptions. With prediction, the tool inspection and quality inspection timings were reduced which improved the planned availability loss. Equipment failure such as spindle unbalance and tailstock misalignment were also reduced from sensors data triggering which reduced the unplanned down time. Quality reject rate was slightly improved due to the prediction of trigger tool change on time. It can be observed that the data-driven tool inspection and quality inspection solutions can improve the availability from 49% to 56%, the performance from 95% to 97%, the quality from 95% to 98%, respectively, in this experiment. The OEE eventually was also improved from 45% to 53% before and after data-driven prediction.

Fig. 8.16a Availability, Performance and Quality comparison before and after prediction with cutting speed 70 m/min, b OEE comparison before and after prediction with cutting speed at 70 m/min, feed rate at 0.1, depths of cut for rough and finishing at 0.23 and 0.07, respectively

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