IoH Technologies into Indoor Manufacturing Sites
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AMPS 2019 Session: Smart Factory and IOT Location Chair: Thorsten Wuest This paper focuses on introducing measurement technologies into manufactur-ing sites regarding the worker-oriented part of 6M, which consists of Man, Ma-chine, Material, Method, Mother Nature, and Money. First, we introduce in-door positioning and work motion recognition systems that we have developed as key components of Internet of Humans (IoH) technologies. Next, we briefly report on two case examples of manufacturing sites where worker behavior measurement, analysis, and visualization are promoted. Then, we conclude this paper with discussion about the costs and benefits on the introduction of indoor positioning technologies into manufacturing sites.
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IoH Technologies into Indoor Manufacturing Sites
- 1. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved IoH Technologies into Indoor Manufacturing Sites Takeshi Kurata12 Takashi Maehata1, Hidehiko Hashimoto1, Naohiro Tada1, Ryosuke Ichikari2, Hideki Aso3, Yoshinori Ito3 1 IoT R&D Center, SEI, Japan 2Human Augmentation Research Center, AIST, Japan 3IoT Acceleration Lab, J-Power Systems Co. Ltd., Japan IoH: Internet of Humans
- 2. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved Visualizing 6Ms in manufacturing sites IoT ✔ ✔✔ ✔ ✖ ✖ IoT: Internet of Things IoH: Internet of Humans IoH ✔ ✔ 2
- 3. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved Flow line Measurement for Kaizen (Improvement) Key IoH tech: Indoor positioning Outdoor: Free of charge • Positioning: GNSS (GPS, Michibiki) • Maps: Google Maps, OSM GNSS: Global Navigation Satellite System OSM: OpenStreetMap 3 Indoor: Benefit principle • Positioning: Stationary-node installation needed • Maps: Floor-plan creation needed Manufacturing sites: Often occupied by indoor environments • Mass production: • Repetitive work process in a specific area • High-mix low-volume production: • Combination of moving and working • Workers/Operators’ positions: Strong correlation with operation contents
- 4. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved Floor Map in Cable Manufacturing Line • Floor maps (Mother-Nature [Environment] in 6M) are necessary for positioning programs and visualization tools. • Many sites lack a CAD model, or have the outdated one. • Calculation of geospatial coordinates for BLE beacons is difficult with inaccurate floor maps. • To avoid such issues, • A 3D model of the indoor environment was automatically generated using LIDAR or/and an omnidirectional camera -> Converted into a 2D floor map. • Geospatial coordinates of BLE beacons placed on the floor map were automatically obtained. 4
- 5. National Institute of Advanced Industrial Science and Technology Indoor positioning: Pros and Cons 5 xDR: PDR xDR: VDR Low cost, Weaving positioning methods Integrated positioning • Combining methods suitable for each site • Balancing precision/accuracy and cost Indoorpositioning technologymap Who? Occlusion Occlusion Occlusion High power consumption High cost Low precision
- 6. National Institute of Advanced Industrial Science and Technology xDR weaving various positioning methods 6
- 7. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved 7 Battery-free setup: Maintenance free. Combination of xDR and BLE positioning: Less number of BLE beacons compared with only BLE positioning. Tracing flow lines with xDR even outside the AoI (Area of Interest) where BLE beacons are placed. Integrated indoor positioning system in SEI xDR: Dead Reckoning for X PDR: Pedestrian Dead Reckoning VDR: Vehicle Dead Reckoning
- 8. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved Qualitative comparison: Continual vs One-time Advantages of continual data record • Capable of natural behavior measumerent • Capable of going back in time such as dashboard cameras • MTT (Make Time Tangible), Virtual time machine • Less time lag from problem finding to cause analysis w/ data 8
- 9. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved Cost Comparison: Manual vs Automatic 9 Case Num of Workers Num of Observers H>S (days) H>D (days) Company P (TS-Macro) 1 1 35 3 Company Q (WS-Mezzo) 11 5 76 24 Company R (TS-Micro) 3 2 101 25 • Company P: Time study by video recording, 1 observer, 1 worker recorded, and macro-positioning granularity (TS-Macro) • Company Q: Work sampling, 5 observers, 33 workers recorded, and mezzo-positioning granularity (WS-Mezzo) • Company R: Time study by video recording, 2 observers, 3 workers recorded, and micro-positioning granularity (TS-Micro) H: Cost of observation by human-wave tactics S: Cost of indoor positioning system usage D: Divided (1/3) Cost of S by cost sharing
- 10. Cost sharing of indoor positioning system 10 Forklift dispatch optimizationCAD for separating workers and vehicles Kaizen support Waiting for a folklift Overtime setup by unnecessary discussion 屋内外シームレストレース Solo inspection /Remote monitoring Safety management support Remote collaborative work support Waiting for a water spider (Mizusumashi) Indoor and outdoor seamless tracing
- 11. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved11 Cost-effective arrangements of transmitters and receivers
- 12. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved Conclusion 12
- 13. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved Future Works: Beyond positioning • Micro-motion recognition with motion capture in an assembly Line • For precisely making operation standard • For efficiently evaluating the variation • Expensive • Work of interest (Material/Environment) moves a little bit at a time. • Not enough to simply obtain a static floor map • Quirks in movement to each individual • Efficient training data collection • Motion recognition not affected by such individual differences 13
- 14. National Institute of Advanced Industrial Science and Technology Motion and Operation Recognition • Typically, 10 to 20 IMUs attached all over the body • Reduce the number of IMUs (Partial body measurement) ✔Less cumbersome for workers and hardware cost reduction ✖Precision reduction (Around 10 to 20%) • Whole-body: Micro-positional data of each body part based on the skeleton model • Partial-body: Only local movements for the available sensors • IoH sensor module with a wearable passive RFID reader and a 10- axis sensor – Micro-positional data: Obtainable once more 14 IMU: Inertia Measurement Unit
- 15. ©2017 Sumitomo Electric Industries, Ltd. All Rights Reserved Future Works: Wide and Deep with Pier data 15 In-depth survey (Narrow but deep) Subject screening with 6M big data (Wide but shallow)
Editor's Notes
- To comprehensively understand and specifically improve the situation of manufacturing sites, it should be effective to aggregate big data regarding 6M (Man, Machine, Material, Method, Mother Nature, and Money). This research is currently continuing with the aims of realizing 6M ‘mieruka’, which means visualization or vision control, as well as providing technologies which support continual kaizen and work/safety/health-care management. With the proliferation of IoT products and services, visualization in terms of tangible is progressing rapidly for grasping the present situation and confirming the result of kaizen. However, the development of visualization technologies and methodologies is still ongoing when it comes to intangible things (Man, Method) such as worker conditions and workflow processes. The lack of relevant data on human behavior could be considered a major disincentive for progress on this front.
- It is relatively easy to collect individual worker data in cases where the work is repetitive in a specific area of mass production. In cellular manufacturing or high-mix low-volume production, however, moving and working are often combined, and it poses a major barrier to data collection on each worker. Therefore, it is crucial to further develop and utilize Internet of Humans (IoH) technology for collecting data on human behavior. As there is often a high correlation between worker positions and operation contents, and also manufacturing sites are mainly occupied by indoor environments, indoor positioning technology is regarded as one of key IoH components. Accordingly, this paper especially focuses on measurement technologies of the worker-oriented part of 6M, and briefly reports on two case examples of manufacturing sites where worker behavior measurement, analysis, and visualization are promoted. It also discusses the costs and benefits of the introduction of indoor positioning technologies into manufacturing sites.
- We have often needed to persuade stakeholders involved in actual sites,
- Needless to say, even if massive amount of 6M data can be gathered by IoT/IoH devices in real manufacturing sites, it does not hold all the answers to comprehensively understand the real sites since big data in general has issues on quality and variety. In-depth surveying such as retrospective interviewing has potential to complement the defect of big data, however, it inevitably requires intensive effort with high work load. In-depth surveying with subject screening based on big data would alleviate the load, and it would result in efficient surveying with both breadth and depth. This is consistent with the idea of “Pier Data” outlined in Fig. 6 in which “Big Data” are integrated with “Deep Data”. Demonstration of such a methodology in actual sites through further development of 6M data collection and visualization technology including IoH technologies is one of our future works.