Immersive Computing Technology to Investigate Tradeoffs Under Uncertainty in Disassembly Sequence Planning

The scientific and industrial communities have begun investigating the possibility of making product recovery economically viable. Disassembly sequence planning may be used to make end-of-life product take-back processes more cost effective. Much of the research involving disassembly sequence planning relies on mathematical optimization models. These models often require input data that is unavailable or can only be approximated with high uncertainty. In addition, there are few mathematical models that include consideration of the potential of product damage during disassembly operations. The emergence of Immersive Computing Technologies (ICT) enables designers to evaluate products without the need for physical prototypes. Utilizing unique 3D user interfaces, designers can investigate a multitude of potential disassembly operations without resorting to disassembly of actual products. The information obtained through immersive simulation can be used to determine the optimum disassembly sequence. The aim of this work is to apply a decision analytical approach in combination with immersive computing technology to optimize the disassembly sequence while considering trade-offs between two conflicting attributes: disassembly cost and damage estimation during disassembly operations. A wooden Burr puzzle is used as an example product test case. Immersive human computer interaction is used to determine input values for key variables in the mathematical model. The results demonstrate that the use of dynamic programming algorithms coupled with virtual disassembly simulation is an effective method for evaluating multiple attributes in disassembly sequence planning. This paper presents a decision analytical approach, combined with immersive computing techniques, to optimize the disassembly sequence. Future work will concentrate on creating better methods of estimating damage in virtual disassembly environments and using the immersive technology to further explore the feasible design space.

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Integrating Immersive Computing Technology With Mixed-Integer Nonlinear Programming for Disassembly Sequence Planning Under Uncertainty

Volume 3: 38th Design Automation Conference, Parts A and B ◽

10.1115/detc2012-70392 ◽

2012 ◽

Author(s):

Sara Behdad ◽

Leif P. Berg ◽

Deborah Thurston ◽

Judy Vance

Keyword(s):

Programming Model ◽

Analytical Models ◽

Mixed Integer ◽

Design Stage ◽

Planning Under Uncertainty ◽

Computing Technology ◽

Disassembly Sequence Planning ◽

Early Design Stage ◽

Sequence Planning ◽

Disassembly Sequence

Disassembly sequence planning at the early conceptual stage of design leads to enormous benefits including simplification of products, lower assembly and disassembly costs, and design modifications which result in increased potential profitability of end-of-life salvaging operations. However, in the early design stage, determining the best disassembly sequence is challenging. First, the required information is not readily available and very time-consuming to gather. In addition, the best solution is sometimes counterintuitive, even to those with experience and expertise in disassembly procedures. Integrating analytical models with Immersive Computing Technology (ICT) can help designers overcome these issues. A two-stage procedure for doing so is introduced in this paper. In the first stage, a stochastic programming model together with the information obtained through immersive simulation is applied to determine the optimal disassembly sequence, while considering uncertain outcomes, such as time, cost and the probability of causing damage. In the second stage, ICT is applied as a tool to explore alternative disassembly sequence solutions in an intuitive way. The benefit of using this procedure is to determine the best disassembly sequence, not only by solving the analytic model, but also by capturing human expertise. The designer can apply the obtained results from these two stages to analyze and modify the product design. An example of a Burr puzzle is used to illustrate the application of the method.

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A multi-layered disassembly sequence planning method to support decision making in de-manufacturing

Sadhana ◽

10.1007/s12046-021-01622-3 ◽

2021 ◽

Vol 46 (2) ◽

Author(s):

Gulivindala Anil Kumar ◽

M V A Raju Bahubalendruni ◽

V S S Prasad ◽

K Sankaranarayanasamy

Keyword(s):

Decision Making ◽

Planning Method ◽

Disassembly Sequence Planning ◽

Sequence Planning ◽

Disassembly Sequence ◽

Support Decision Making

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ASSEMBLY/DISASSEMBLY SEQUENCE PLANNING

Assembly Automation ◽

10.1108/eb004372 ◽

1992 ◽

Vol 12 (3) ◽

pp. 31-38 ◽

Cited By ~ 21

Author(s):

K. Yokota ◽

D.R. Brough

Keyword(s):

Disassembly Sequence Planning ◽

Sequence Planning ◽

Disassembly Sequence

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Partial/Parallel Disassembly Sequence Planning for Complex Products

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4037608 ◽

2017 ◽

Vol 140 (1) ◽

Cited By ~ 14

Author(s):

Fei Tao ◽

Luning Bi ◽

Ying Zuo ◽

A. Y. C. Nee

Keyword(s):

Degrees Of Freedom ◽

Diversity Management ◽

Disassembly Sequence Planning ◽

Complex Products ◽

Sequence Planning ◽

Modular Product ◽

Disassembly Sequence ◽

Comparison Results ◽

Complex Constraints ◽

Disassembly Time

Disassembly is a very important step in recycling and maintenance, particularly for energy saving. However, disassembly sequence planning (DSP) is a challenging combinatorial optimization problem due to complex constraints of many products. This paper considers partial and parallel disassembly sequence planning for solving the degrees-of-freedom in modular product design, considering disassembly time, cost, and energy consumption. An automatic self-decomposed disassembly precedence matrix (DPM) is designed to generate partial/parallel disassembly sequence for reducing complexity and improving efficiency. A Tabu search-based hyper heuristic algorithm with exponentially decreasing diversity management strategy is proposed. Compared with the low-level heuristics, the proposed algorithm is more efficient in terms of exploration ability and improving energy benefits (EBs). The comparison results of three different disassembly strategies prove that the partial/parallel disassembly has a great advantage in reducing disassembly time, and improving EBs and disassembly profit (DP).

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