# Evaluation of Disassembling Process Inference Based on Positional Relations Matrix
## 1 Introduction  
Disassembling is an important process for the maintenance, reparation, and disposal of mechanical as well as structural systems. An improper disassembling operation may cause severe damage to the system, or may result in injuries to operators or customers, as described in [1]. Assembling and disassembling are reversal operations; however, disassembling a machine is more difficult than assembling in general [2]. In addition, more often than not, disassembling processes are not prepared in advance. We need to organize a disassembling process based on the obtained arrangement information of the parts that make up the system to be disassembled.  
A number of studies dealing with disassembling process generation have been reported to date. [PERSON] et al. [3] studied the development of a computer-aided disassembly planning system. [PERSON] et al. [4] proposed an information model for disassembly and optimal disassembly sequence generation. The approach is based on a linear programming optimization model and has dynamic capabilities by means of state-dependent information. [PERSON] et al. [5] dealt with a method used to find an optimal disassembly sequence beforehand in the product design phase, to increase the reusability. [PERSON] and [PERSON] [6] introduced new optimization parameters that evaluate disassembly aptitude in order to take intoaccount the difficulty and feasibility of the disassembly operation; the genetic algorithm was employed to optimize the process sequence.  
In recent studies, various types of computer-aided vision and machine learning technologies have also been applied for supporting assembly/disassembly operations [7; 8; 9; 10]. [PERSON] et al. [11] offered an approach to the design for disassembly (DfD) to reduce the time and cost of disassembling a product and proposed a system that visualizes the generated disassembly sequence in augmented reality (AR) to support inexperienced operators in disassembly tasks; they used a gearbox as the target of their case study. [PERSON] et al. [12] developed a method to visually map the disassembly of a product, showing different routes towards target components; the map was based on analyses of vacuum cleaners. Research on disassembly sequence generation for sustainability has attracted considerable attention as well [13; 14]. To facilitate the long-term use and recyclability of vehicles, Toyota has worked to design parts to be more easily removed from their vehicles [15]; the easy-to-dismantle structures are practically applied to their modules. They have developed a matrix-based design method for circular economy.  
In order to deal with the various disassembling problems in general form, we are studying an approach based on positional relation information among the parts [16]. On the basis of our approach [17] based on the positional relations matrix that contains positional relation information among the parts, it is considered to be possible to formulate and solve various disassembling problems. The approach is considered to be efficient and applicable to various cases in principle; however, the computational cost and the optimality of the generated disassembling processes have not yet been confirmed.  
In the current study, we evaluate the applicability of the disassembling process inference based on the positional relations matrix. We have developed a disassembling-problem generation system that generates block pattern-based disassembling problems [18], which adopts a random walk process [19] for the placement of blocks. On the basis of the generated disassembling problems, we evaluate the applicability, the efficiency, and the optimality of the disassembling process inference approach based on the positional relations matrix.