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IIoT-Enabled Production System for Composite Intensive Vehicle Manufacturing.


The stringent CAFE [1] standards set forth by the Obama's administration has pushed the automotive industry to radically revolutionize and design lightweight systems using advanced materials such as composites. Despite several outstanding benefits, advanced materials often come with additional costs. Minimizing physical infrastructure and improving efficiency will make the use of these materials affordable. This paper explorer a solution offered by IIoT, to minimize cost of the body structure. This solution is validated by implementing in a virtual plant simulation environment.

A baseline body shop was designed, this is similar to BMW i3's [2] body shop, which is the current state of art in mass production of composite intensive vehicle. In this paper the following hypotheses will be validated.

* IIoT enabling - Manufacturing cost reduction

** Improving the overall equipment efficiency

* IIoT enabling - Capital investment

** Increasing shop floor efficiency

In the following section this paper will discuss about the baseline vehicle structure used, production volumes, capacity planning, baseline factory layout simulation and proposed factory layout simulation.


A baseline composite intensive vehicle structure concept was developed, using this a baseline body shop is designed to manufacture this vehicle.

The Body structure of the baseline vehicle is split into two parts. An aluminum spaceframe (Figure 1) with the powertrain and a CFRP module (Figure 2) for passenger cell. The lower aluminum spaceframe is common for all three variants and the passenger cell are different for all three variants with few common parts. The three variants assumed for this baseline vehicle are sedan, wagon and fastback.

The passenger cell has 54 different composite parts over all three variants. In this example its assumed that these parts manufactured from carbon fiber reinforced epoxy using wet compression molding.

To develop the body-shop the peak production volume per year was assumed to be 108,000 units with eight production years as show in the Figure 3. These numbers are similar to mid segment passenger cars.

Line rates and cycle times are picked so that the peak monthly production could meet the target volume with an extended shift model. The agreed capacity with regular shift model is 14,600 units per month (Figure 4). Using this value, line rates and cycle-time for the body shop was determined.

The desired line-rate for the body shop is 38 vehicles per hour and the cycle time is 109 sec. With this data a baseline body shop is deigned. For the scope of this study, only the body shop for CFRP module is considered. A Bill of material for the CFRP module was generated from the cad models. Information such process times, precedence diagrams are generated. Adhesive bonding is the most common joining method for composite structure. The time for adhesive application is often limited by the speed of the robot and the assembly time is limited by the curing time of the adhesive. These data were used to develop a body shop layout. To meet the required line rates two parallel lines, have to be commissioned. Each line has eight station over ten takts. Conventional principles for line balancing were used to develop a most optimum line. This study was performed under the assumption that individual parts are manufactured in a press shop and are stored in buffer and supplied to the body shop in time.

Baseline Body-Shop Layout

After performing line balancing, the entire operation is divided into 8 stations and are performed in 10 takts. Station 6 is performed over 3 takts and this is where the body side outers are bonded to the under-pan for the structure (Figure 7). These body side outer rings are also unique to each vehicle variants. Each body side outer is a sub assembly of 3 large parts which are bonded on a sub assembly line as shown in the Figure 5.

All stations have an adhesive booth and a couple of multi axis assembly robots as shown in Figure 6. The orange boxes in Figure 6 illustrate the adhesive application booth.

The major part of the takt time is adhesive bonding operations. The operation for adhesive bonding [3] can be split as follows.

1. Surface preparation.

The cleaning head on the multi axis robot will clean the adhesive bond region. Operation time is the product of multi axis speed and length of the adhesive bead.

2. Adhesive application.

The robot manipulates the part under an adhesive application nozzle. Operation time is the product of multi axis speed and length of the adhesive bead.

3. Part assembly and adhesive curing.

Once the adhesive is applied the part is located to its final location and held in its final location for initial curing. At this phase the adhesive need not cure completely but just enough for the body structure to retain its geometry during the assembly operations. The operation time for this phase is the summation of time to locate the part in its fixture and adhesive curing time. For the case of this study we assumed that the curing time is 60 seconds.

In the Table 1 you can see the processing times, and parts assembled for each stations. To perform line balancing [4] for a composite intensive body is more challenging than a traditional body shop. This is mainly due the fact that individual adhesive operation can into be split into multiple stations. In a traditional steel body shop spot welds on the same part can be split into multiple stations in interest of cycle time and access to the weld region.

Performance Estimation for Baseline Body Shop

Assumptions Made for the Baseline Simulation

* Availability for all station = 98% of processing time

* Availability for all conveyers = 98% of processing time.

* Overall part quality = 100%

Results from Baseline Simulation

The following result were obtained from the simulation (Figure 8) as shown in Figure 9, and are explained below.

Cumulated Statistics of the Parts which the Drain Deleted

* Mean life for each body structure = 50.04 hrs.

* Throughput per hour = 39

* % time value added per part = 44.66%

* % time spent in transportation = 24.41%

* % time spent in in line buffers = 24.74%

This percentage time value added signifies the ratio of time when operations were performed on the parts vs nonproductive time such as transportation and storage.

The resource utilization chart (Figure 10) is a good illustration, to indicated bottle neck stations and helps in understanding the rate of utilization for each station. Due to the limitations on line balancing, the working time is not consistent over all stations.

Proposed Body Shop Layout

With composite body structures the number of parts are much less when compared to a steel structure with the similar footprint. With this there a clear advantage over the convention steel structures as the number of joint are relatively lesser. These joint often span over larger flange lengths and are adhesively bonded. The average time for each joint is significantly longer than the traditional steel body structures and cannot be split into multiple operations. To meet traditional cycle times, multiple parallel lines are used as seen in the baseline study. We also noticed that these stations are difficult to line balance as these operations are very long and splitting them is not possible.

Considering all units of the body shop are connected with IIoT [5], few concepts for material handling are implemented and their influence on performance is evaluated. Initially goal was to improved station utilization, but the nature of the assembly operations, not much improvements were possible.

In a traditional body shop, roller/belt covers are used for material flow. Cost of setting up an infrastructure for the roller conveyers is very high and offers very little flexibility for changes. We can totally imagine automated guided vehicles to replace the conveyers. These AGV [6] can smartly transport the parts in the production system. With IIoT material handling systems we can impart a smart logic in these material handling systems to improve productivity.

The logic that controls the AGV is described in the Figure 11. With the help of IIoT, the AGV can pick the right station.

In the proposed body shop layout, we have the same number of station but they are all on one assembly network. This vastly improve space utilization and minimizing redundant infrastructure. With the material flow flexibility this layout is more robust for equipment failure. Smart material sorting block have been added in the plant simulation to emulate the logic of the AVG as shown in the Figure 13

The pathway for AGV generally doesn't require special infrastructure. Contact less locating beacon are placed in the floor (Figure 14) to provide the AGVs special awareness and also the central information process unit can monitor all AGVs location.

Performance Estimation for Proposed Body Shop

Assumptions Made for the Proposed Layout Simulation

* Similar to the baseline simulation.

Results from Proposed Layout Simulations

The following result were obtained from the simulation as shown in Figure 15 and explained bellow.

* Mean life for each body structure = 34.27 hrs.

* Throughput per hour = 38

* % time value added per part = 64.92%

* % time spent in transportation = 15.33%

* % time spent in in line buffers = 9.1%

Since the operation allocations for stations are similar to the baseline, no significant change in the resource utilization is noticed. Since the only significant change is in the material flow system, we can easily see the impact on productivity by implementing a smart connected material flow system.


With application IIoT to the material handling system, we noticed that the smart material flow and interconnected grid layout has significant improved the productive time. For the baseline layout the mean life time for the 50.04 hrs. and the % time value added is 44.66%. This implies that 55.3% of the mean life time of the parts which translates to 27.6 hours of the part's life was spent in the body shop with no value added. Similarly, for the proposed layout the % time value added is 75.5% of its mean life time and this translates to 8.3 hours of non-value added time.

Thus by implementing smart material flow system and the interconnected grid layout, 19.3 hours of non-value added time was eliminated from the body shop. The stations are also much closer to each other and elimination of the second conveyer will definitely improve the manufacturing floor foot print efficiency per car.

In future work, we would like to study the impact of capital, operating cost and its impact on the logistics and supplier network. This principles of smart material flow might yield more potential in the final assembly shop, were the variants in final products are very high and the number of parts are much higher. Performing a study on the final assembly shop would also be of great interest.


[1.] Transportation O., "Corporate Average Fuel Economy for MY 2017-MY 2025 Passenger Cars and Light Trucks," no. August, pp. 1-1178, 2012.

[2.] "BMW i3 Teardown and Benchmarking Study"

[3.] Cole G. S. and Sherman a. M., "Light weight materials for automotive applications," Mater. Charact., vol. 35, no. 1, pp. 3-;9, 1995.

[4.] Boysen N., Fliedner M., and Scholl A., "Assembly line balancing: Which model to use when?," Int. J. Prod. Econ., vol. 111, no. 2, pp. 509-528, 2008.

[5.] Qu T., Lei S. P., Wang Z. Z., Nie D. X., Chen X., and Huang G. Q., "IoT-based real-time production logistics synchronization system under smart cloud manufacturing," Int. J. Adv. Manuf. Technol., vol. 84, no. 1-4, pp. 147-164, 2016.

[6.] Michalos G., Makris S., Papakostas N., Mourtzis D., and Chryssolouris G., "Automotive assembly technologies review: challenges and outlook for a flexible and adaptive approach," CIRP J. Manuf. Sci. Technol., vol. 2, no. 2, pp. 81-91, 2010.


Veera Aditya Yerra

Cubicle 11, 4 Research Drive

Greenville, SC 29607


The authors would like to acknowledge the financial support of Klockwerks-Proforma fellowship and Altair fellowships and United States Department of Energy (DE-FOA-0001201).


IIoT - Industrial internet of things

AGV - Automated guided vehicles.

Veera Aditya Yerra

CU-ICAR Clemson University

Srikanth Pilla

Clemson University

Table 1. Processing times for each stations.

Station   Takt no.      process   parts/sub-assemblies
no.                      time          assembled

1         Takt 1        129       Floor pan
2         Takt 2        182       floor cross member 1 and rear
                                  seat back
3         Takt 3        114       Trunk floor
4         Takt 4        119       Trunk floor reinforcement
5         Takt 5        177       Firewall and windshield
6         Takt 6,7 &8   301       Body side outers and roof
                                  cross members
7         Takt 9        157       roof panel
8         Takt 10       133       rear closeout panel
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Author:Yerra, Veera Aditya; Pilla, Srikanth
Publication:SAE International Journal of Engines
Date:Apr 1, 2017
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