Discussion and Future Work

This paper has presented a framework for investigating tradeoffs under uncertainty using immersive computing technology. There are two difficult aspects of uncertainty that the ap- proach presented here addresses. The first is the difficulty in gathering data required to estimate the values of the parameters used in mathematical models of the disassembly process, in this case the time (and cost) of a large number of possible disassembly sequence steps, and the probability of damage caused while carrying out those steps. The second difficulty is that even after the data is gathered, unavoidable uncertainty remains, and the designer must determine its effect on the relative desirability of a very large number of possible design alternatives, in this case disassembly sequence steps. This paper presented a method for employing ICT to carry out a virtual experiment in order to simulate a large number of disassembly process steps, and from those simulations better estimate the cost and probability of damage associated with each possible step. Then, mathematical models (dynamic programming and multiattribute utility analysis) were employed to determine the disassembly sequence that resulted in the optimal combination of cost and probability of damage. The ICT demonstrated an effective method to gather data on human interaction with the product that can be used to improve the decision making process. In the proposed scenario, the user manipulates the virtual parts to estimate values for potential damage that might occur during disassembly. This data is subsequently used as input to the dynamic programming decision model used to determine the optimal disassembly process. Without the ability to manipulate real parts, the designer has to rely on past experience to anticipate the extent of damage during each part removal process. ICT provides a computer generated environment that supports user manipulation of virtual CAD models, thus allowing this data to be generated prior to manufacture of actual products.

Decisions about the design of the product that are affected by disassembly operations can be made prior to final product design. There are numerous opportunities for future work. The development of a more comprehensive model for estimating component damage (from haptic interaction) would increase data reliability. In addition, most products contain various types of fasteners such as screws, rivets, and snaps. Inclusion of fasteners would require interactive simulation of deformable surfaces of the virtual models and manipulation of tools to aid in disassembly. Interactive simulation of deformable surfaces and the use of tools are common features of virtual surgery applications and could readily be implemented in this work. Finally, it would be worthwhile to investigate how ICT can be employed to overcome the systematic biases that might be embedded in cognitive heuristics that designers use to estimate the costs and damage resulting from various product design and disassembly alternatives.

Acknowledgments This material is based upon work supported by the National Science Foundation under grant # CMMI-1100177 and CMMI-1068926. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Note: Leif Berg’s primary contribution to this paper was in the design and development of an interactive virtual reality application used to calculate and collect damage estimation data of a product during disassembly. The primary coauthor, Sara Behdad, developed the novel dynamic programming algorithm, leveraging data from the VR application as input parameters, to find the optimal disassembly sequence. All of the coauthors were involved in the design of the investigation and interpretation of the results.

Bibliography

Aleotti, J. and Caselli, S. (2011). Physics-based virtual reality for task learning and intelligent disassembly planning. Virtual reality, 15(1):41–54.

Behdad, S., Kwak, M., Kim, H., and Thurston, D. (2010). Simultaneous selective disassembly and end-of-life decision making for multiple products that share disassembly operations. Journal of Mechanical Design, 132(4):41002.

Behdad, S. and Thurston, D. (2012). Disassembly and reassembly sequence planning tradeoffs under uncertainty for product maintenance. Journal of Mechanical Design, 134(4):41011. Bellman, R. and Kalaba, R. (1965). Dynamic programming and modern control theory. Aca-

demic Press New York.

Borro, D., Hernantes, J., Garc´ıa-Alonso, A. M., and Matey, L. M. (2005). Collision Problem: Characteristics for a Taxonomy. In International Conference on Information Visualisation, pages 410–415.

Dewar, R. G., Carpenter, I. D., Ritchie, J. M., and Simmons, J. E. L. (1997). Assembly planning in a virtual environment. In Innovation in Technology Management-The Key to Global Leadership. PICMET’97: Portland International Conference on Management and Technology, pages 664–667. IEEE.

Dong, J. and Arndt, G. (2003). A review of current research on disassembly sequence generation and computer aided design for disassembly. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 217(3):299–312.

Faas, D. and Vance, J. M. (2010). Assessment of Pointshell Shrinking and Feature Size on Virtual Manual Assembly. In World Conference on Innovative Virtual Reality, pages 211– 218. American Society of Mechanical Engineers.

Giudice, F. and Kassem, M. (2009). End-of-life impact reduction through analysis and redis- tribution of disassembly depth: A case study in electronic device redesign. Computers & Industrial Engineering, 57(3):677–690.

Gonzalez-Torre, B. and Adenso-Diaz, B. (2004). Optimizing decision making at the end of life of a product. In Photonics Technologies for Robotics, Automation, and Manufacturing, pages 40–50. International Society for Optics and Photonics.

Hula, A., Jalali, K., Hamza, K., Skerlos, S. J., and Saitou, K. (2003). Multi-criteria decision- making for optimization of product disassembly under multiple situations. Environmental science & technology, 37(23):5303–13.

Jayaram, S., Connacher, H. I., and Lyons, K. W. (1997). Virtual assembly using virtual reality techniques. Computer-Aided Design, 29(8):575–584.

Jayaram, S., Jayaram, U., Kim, Y. J., DeChenne, C., Lyons, K. W., Palmer, C., and Mitsui, T. (2007). Industry case studies in the use of immersive virtual assembly. Virtual Reality, 11(4):217–228.

Jim´enez, P., Thomas, F., and Torras, C. (2001). 3D collision detection: a survey. Computers & Graphics, 25(2):269–285.

Kang, J.-G. and Xirouchakis, P. (2006). Disassembly sequencing for maintenance: a survey. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Man- ufacture, 220(10):1697–1716.

Kara, S., Pornprasitpol, P., and Kaebernick, H. (2005). A selective disassembly methodology for end-of-life products. Assembly Automation, 25(2):124–134.

Kim, C. E. and Vance, J. M. (2004). Collision detection and part interaction modeling to facil- itate immersive virtual assembly methods. Journal of Computing and Information Science in Engineering, 4(2):83–90.

Lambert, a. J. D. (2003). Disassembly sequencing: A survey. International Journal of Produc- tion Research, 41(16):3721–3759.

Lauterbach, C., Mo, Q., and Manocha, D. (2010). gProximity: Hierarchical GPU-based Oper- ations for Collision and Distance Queries. In Computer Graphics Forum, volume 29, pages 419–428. Wiley Online Library.

Lee, D., Xirouchakis, P., and Zust, R. (2002). Disassembly scheduling with capacity constraints. CIRP Annals-Manufacturing Technology, 51(1):387–390.

Li, J., Khoo, L., and Tor, S. (2003). Desktop virtual reality for maintenance training: an object oriented prototype system (V-REALISM). Computers in Industry, 52(2):109–125.

Lin, M. C. and Manocha, D. (1995). Fast interference detection between geometric models. The Visual Computer, 11(10):542–561.

Mattson, C. A. and Messac, A. (2005). Pareto frontier based concept selection under uncer- tainty, with visualization. Optimization and Engineering, 6(1):85–115.

McGovern, S. M. and Gupta, S. M. (2006). Ant colony optimization for disassembly sequencing with multiple objectives. The International Journal of Advanced Manufacturing Technology, 30(5-6):481–496.

Pan, J., Zhang, L., and Manocha, D. (2012). Collision-free and smooth trajectory computation in cluttered environments. International Journal of Robotics Research, 31(10):1155–1175. Pomares, J., Puente, S., Torres, F., Candelas, F., and Gil, P. (2004). Virtual disassembly of

products based on geometric models. Computers in Industry, 55(1):1–14.

Ritchie, J. M., Simmons, J., Dewar, R., and Carpenter, I. (1999). A methodology for eliciting expert knowledge in virtual engineering environments. In Management of Engineering and

Technology, 1999. Technology and Innovation Management. PICMET’99. Portland Interna- tional Conference on, volume 1, page 202. IEEE.

Sengupta, M. and Styblinski, M. A. (1997). Visualization of trade-offs in optimization of integrated circuits with multiple objectives. In IEEE International Symposium on Circuits and Systems, volume 3, pages 1640–1643. IEEE.

Seth, A., Su, H.-J., and Vance, J. M. (2006). SHARP: A System for Haptic Assembly and Realistic Prototyping. In Computers and Information in Engineering Conference, volume 2006, pages 905–912. ASME.

Seth, A., Vance, J. M., and Oliver, J. H. (2010a). Combining dynamic modeling with geomet- ric constraint management to support low clearance virtual manual assembly. Journal of Mechanical Design, 132(8):81002.

Seth, A., Vance, J. M., and Oliver, J. H. (2010b). Virtual reality for assembly methods proto- typing: a review. Virtual Reality, 15(1):5–20.

Tang, Y., Zhou, M., and Gao, M. (2006). Fuzzy-Petri-net-based disassembly planning consid- ering human factors. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 36(4):718–726.

Thurston, D. L. (1991). A formal method for subjective design evaluation with multiple at- tributes. Research in engineering design, 3(2):105–122.

Thurston, D. L. (2001). Real and Misconceived Limitations to Decision Based Design With Utility Analysis. Journal of Mechanical Design, 123(2):176.

Thurston, D. L., Lewis, K., Chen, W., and Schmidt, L. C. (2006). Multi-attribute utility analysis of conflicting preferences. In Decision Making in Engineering Design. ASME Press. Tian, Y. Q., Thurston, D. L., and Carnahan, J. V. (1994). Incorporating end-users attitudes towards uncertainty into an expert system. Journal of Mechanical Design, 116(2):493–500.

Zhang, L., Huang, X., Kim, Y. J., and Manocha, D. (2008). D-plan: Efficient collision-free path computation for part removal and disassembly. Computer-Aided Design and Applications, 5(6):774–786.

CHAPTER 6. DISASSEMBLY SEQUENCE EVALUATION: A USER STUDY LEVERAGING IMMERSIVE COMPUTING TECHNOLOGIES

A paper published in the ASME Journal of Computing and Information Science in Engi- neering (March 2015, Vol. 15, No. 1).

Leif P. Berg1, Sara Behdad2, Judy M. Vance1, Deborah Thurston2

1_{Iowa State University, Ames, Iowa}

2_{University of Illinois at Urbana-Champaign Urbana, Illinois, USA}

6.1 Abstract

As interest in product recovery, reuse and recycling rises, planning and evaluating disas- sembly sequences is becoming increasingly important. The manner in which a product can be taken apart strongly influences end-of-life (EOL) operations and costs. Early disassembly plan- ning can also inform non-EOL processes including repair and routine maintenance. Recently, research has concentrated on creating optimization algorithms which automatically generate disassembly sequences. These algorithms often require data that is unavailable or estimated with high uncertainty. Furthermore, industries often employ CAD modeling software to evalu- ate disassembly sequences during the design stage. The combination of these methods result in mathematically-generated solutions, however, the solutions may not account for attributes that are difficult to quantify (human interaction). To help designers better explore and understand disassembly sequence opportunities, the research presented in this paper combines the value of mathematical modeling with the benefits of immersive computing technologies (ICT) to aid in early design decision making.

For the purposes of this research, an ICT application was developed. The application displays both 3D geometry of a product and an interactive graph visualization of existing disassembly sequences. The user can naturally interact with the geometric models and explore sequences outlined in the graph visualization. The calculated optimal path can be highlighted allowing the user to quickly compare the optimal sequence against alternatives. The application has been implemented in a three wall immersive projection environment. A user study involving a hydraulic pump assembly was conducted. The results suggest this approach may be a viable method of evaluating disassembly sequences early in design.

6.2 Introduction

In the course of executing routine maintenance or repair, a product may need to be par- tially or fully disassembled. When products approach end-of-life (EOL) a variety of challenges arise. In many cases, components can be reused or recycled, so a product disassembly process is required. Additionally, some components may have inherent value such that the primary objective of disassembly is the extraction of such a component.

Disassembly sequences are defined as a set of subsequent disassembly operations for the separation of an assembly into its sub-assemblies (Lambert, 2001). Disassembly and assem- bly are strictly disparate processes. Assembly operations are not always reversible and the value added in the disassembly process is typically lower than the obtained value in assembly; therefore, there are situations when partial disassembly is preferred to complete disassembly, especially when disassembly is performed for maintenance or component recovery. In addition, in disassembly planning, significant uncertainty exists with regard to the quality of the parts. Further, compared to assembly planning, there tends to be more sequence alternatives when performing disassembly. Even a small assembly with only a few parts may have many different possible disassembly sequences.

Disassembly planning often involves multiple objectives and considerations including: dis- assembly time, cost, and potential for damage. For products that require disassembly, EOL disassembly may account for significant product take-back costs. Considering disassembly

processes early in the product design process provides opportunities to evaluate and explore multiple methods of disassembly leading to improved designs.

The generation and evaluation of disassembly sequences can be explored using optimiza- tion methods, CAD tools, or physical prototypes. Optimization methods seek to arrive at an optimum disassembly sequence based on input and the formulation and solution of the opti- mization problem. The solution is only as valid as the accuracy of the system modeling and the suitability of the optimization method with the particular use case. CAD software allows designers to examine geometric constraints that dictate disassembly paths but neglect to ac- count for the physical interaction of the disassembly operator. Physical prototypes can be used to produce experience-guided disassembly sequences, however, they are often not available in the early design phase.

This research explores the use of immersive computing technologies (ICT) in disassembly sequence planning. ICT supports user interaction with virtual design configurations in in- creasingly natural ways to achieve an immersive life-like design experience. The ICT approach differs from traditional mouse and keyboard techniques in that it supports testing of virtual design alternatives through natural and context-based human interactions. Visual feedback is presented to a designer through stereoscopic viewing, resulting in the perception of a three di- mensional workspace. Real-time position tracking coupled with haptic (force feedback) devices enable the designer to interact with the virtual products using natural human motions. Local- ized audio feedback increases the realism of the simulated environment. These technologies can be leveraged to simulate assembly and disassembly operations without the need for physical prototypes. Additionally, they support exploration of potential alternatives and evaluation of multiple cost-effective approaches.

Previous work has only begun to examine the coupling of traditional disassembly sequence planning methods with the real-time potential of ICT (Berg et al., 2012; Behdad et al., 2014a,b, 2013). In attempts to further investigate this area the authors have designed, implemented, and evaluated an ICT application in a large-scale projection screen-based immersive environment. The paper is outlined as follows: Section6.3will present related background research. The ICT application is described in section6.4. A user study is presented in section6.5, with results

Figure 6.1 Disassembly graph of a six piece wooden puzzle Berg et al. (2012)

and discussion in section6.6. Section 6.8presents conclusions and insights into future research opportunities.