Remanufacturing Scheme Design for Used Parts Based on Incomplete Information Reconstruction

Remanufacturing is a process that returns used parts into “new or better than new” performance and conditions and includes disassembling, cleaning, repair, reassembly, and inspection [1]. However, in addition to differences in shape and size, used parts exhibit a high degree of uncertainty in terms of failure characteristics, quality conditions, and remaining life [2]. The sound design of the remanufacturing scheme is one of the key steps to improve remanufacturing efficiency and reduce production costs. It not only allows the reutilization of used parts but also helps to save money and resources and protect the environment [3‐5].

A remanufacturing scheme design uses advanced manufacturing techniques to repair used parts, so as to achieve low cost, high efficiency, high reuse rate, and performance recovery upgrade [6, 7]. A premise for the remanufacturing scheme is to understand the information on the failure characteristics of used parts. In general, the common failure modes of used parts include fracture, wear, and deformation, which account for more than 75% of the total failures of parts [8, 9]. Owing to the long-term service, wear, and loss of technical documentation, it is difficult to obtain the technical information of the parts, which results in differences in remanufacturing schemes and affects the quality of remanufacturing [10‐14]. At present, the design of remanufacturing schemes is still based on personnel experience and manuals, which leads to difficulties in remanufacturing efficiency, cost, and energy consumption, and there are a series of scientific problems that need to be solved [15‐18]. Therefore, a reasonable remanufacturing scheme can effectively improve the efficiency and reduce costs. Understanding the failure characteristics of used parts is of great significance to achieve high efficiency and strict quality requirements.

The purpose of this study is to generate rapidly and efficiently a remanufacturing scheme. Compared with previous studies, the novelty of this work lies in the following aspects: 1) A framework for a remanufacturing scheme design based on incomplete information reconstruction is developed. 2) By means of finite element analysis, the remaining life of the reconstructed model is predicted, and the requirements of the next life cycle are assessed. 3) By using an integral iteration method, the 3D scanned damage point cloud data is registered with the original point cloud data, and the missing point cloud model is constructed to achieve geometric information recovery. 4) A cladding path is generated for the recovered part model using the tool contact point path section line method.

This paper is organized in five sections. The first section introduces the remanufacturing design. The literature on remanufacturing scheme design is reviewed in Section 2. The third section explains in detail the method used. The fourth section takes a remanufactured turbine blade as an example to verify the effectiveness of the method. The fifth section summarizes and discusses the results of the case study to draw the conclusions of this study.

The development of a remanufacturing scheme is essential for the remanufacturing processes. It directly affects the remanufacturing production efficiency, cost, and energy consumption, and it is important to maximize the utilization of the residual value of used parts. Owing to the differences in load conditions and environmental media during product service life, used parts possess varying failure characteristics such as wear, fracture, and corrosion, which affect the remanufacturing processes. Therefore, to maximize the remanufacturing efficiency and cost-effectiveness, it is of paramount importance to develop a remanufacturing scheme that fully considers the unique failure characteristics of the used parts to be remanufactured.

The remanufacturing process planning and optimization and decision making over the remanufacturability of used parts have been studied in the literature. Li et al. [19] proposed a decision-making method for the remanufacturing process based on an improved fuzzy neural network, which comprehensively considered the uncertainty and ambiguity of the damage condition of used parts for remanufacturing and the quality requirements after remanufacturing. Jiang et al. [20] proposed a remanufacturing process planning method that combines rough sets and case-based reasoning and rapidly creates a process planning for used parts from the knowledge generated from existing parts remanufacturing. Zhang et al. [21] studied the process planning and scheduling problem of a typical remanufacturing system based on the difficulties and complexities brought about by various uncertainties in the remanufacturing processes, and they developed a multi-objective genetic algorithm based on simulation. Zheng et al. [22] proposed a hybrid method that combines coordinate measuring machine data acquisition, defect model regeneration, and repair process selection decisions for complex damage parts with various defects. Jiang et al. [23] developed a process planning decision method considering the ambiguity of remanufacturing performance standards and the inherent complexity associated with process planning decisions. A decision method combining quality function deployment and fuzzy linear regression was proposed to determine the most appropriate process plan. Kin et al. [24] analyzed the conditions and defects of the core components and determined the optimal repair process of these components. Chao et al. [25] established an effective method linking 3D point cloud reconstruction and laser metal deposition repair and proposed a method for remanufacturing damaged metal parts using the additive manufacturing technology. To improve the final quality of parts obtained by additive manufacturing, Um et al. [26] proposed a method of additive manufacturing representation based on a step neural network and a series of geometric reasoning methods to realize the automatic derivation of the repair section. Zhang et al. [27] proposed a modeling method for the repair of wear, edge crack, distortion, creep, and corrosion of aero-engine blades, and the effectiveness of the proposed method was verified by repair experiments. Wilson et al. [28] provided an attractive and cost-effective method for repair or remanufacturing of high-value engineering components through laser direct deposition techniques, in which turbine airfoil defects were successfully repaired using a new semi-automated geometric reconstruction algorithm and a laser direct deposition process.

Various fault characteristics lead to the need for customized solutions, and thus, a rapid and accurate acquisition of the fault characteristics can help improve the remanufacturing efficiency. Li et al. [29] targeting at the individuality of the failure condition and the complexity of the structure and shape of the worn mechanical parts in a remanufacturing system, proposed a reverse engineering-assisted remanufacturing integration method for the worn parts. Eyup et al. [30] used a coordinate measuring machine to capture the important scan lines on the surface of the damaged component and then construct the feature outline of the parts for remanufacturing. Zhang et al. [31] suggested a new approach that took into consideration the working conditions, structural characteristics, and loading distribution and analyzed the causes of fracture failure based on the finite element method. Hou et al. [32] investigated the repair of worn blades. The manual acquisition of the target surface is very time consuming, and thus, an innovative adaptive maintenance strategy was proposed, covering the pre-inspection, welding, and machining processes.

The previous works have provided effective solutions addressing the uncertainty and individuality of the failure conditions of used products. However, there are several steps in the restoration that are isolated. There are limited interactions between the restoration steps, resulting in an ad-hoc remanufacturing restoration. This paper proposes a remanufacturing scheme design method based on incomplete information reconstruction of second-hand parts. First, through finite element analysis, the remaining life of the reconstruction model can be predicted, and it can be determined whether the demand for the next life cycle is met. Second, by analyzing the incomplete and fuzzy component remanufacturing information, the missing point cloud model is constructed to recover the geometric information. Finally, the tool remanufacturing process path can be generated by the tool contact point path section line method.

Owing to the diversity in remanufacturing technologies and the complexity of the design of a repair solution, it is important to select a suitable remanufacturing process and design a repair solution. A design framework for the remanufacturing scheme design for used parts based on the reconstruction of defective information is established as shown in Figure 1.

The first step for remanufacturing used parts is to pre-test before remanufacturing to assess whether they meet the remanufacturing requirements. Geometric information reconstruction is performed on the used parts that meet the remanufacturing requirements. According to the principle of reverse engineering, the defect model of the part is extracted through 3D scanning, and the unit step integral iteration method is used to rapidly and efficiently register the defect point cloud data with the original data, and obtain the damage point cloud information through information reconstruction. Finally, the tool contact point path section line method is used to generate the repair path according to the identified missing point cloud.

Turbine blades are important transmission components in mechanical engineering. Due to the influence of service conditions, materials, and structures, it is easy to cause tooth surface fatigue damage, tooth surface wear, and fracture failure. Turbine blades have a long manufacturing cycle with high added value. As shown in Figure 14, the design steps of the blade remanufacturing scheme are described by taking a certain type of turbine blade as an example and the GH4133 alloy as its material.

As can be observed in Figures 15 and 16, the design life of the turbine blade is 1000 SI, and the fatigue life of the finite element calculation reconstruction model is 1300 SI, which can meet the remanufacturing requirements.

The original 3D scan model and the damage 3D model mesh of the used parts are shown in Figures 17 and 18. According to the defined size, the original 3D scan model has 1974 nodes with different coordinates.

The mesh size selected for the used component model is 0.91 mm, and 6650 nodes are obtained.

The coordinates of the original 3D model nodes are then exported to Excel, where the coordinates of the 1974 different points of the blade are obtained. Some of the data are listed in Table 1.

The obtained damage model nodes and their respective coordinates are listed in Table 2.

Comparing the original and corrupted coordinate sets, the three-dimensional model of the blade is divided into two two-dimensional planes by the unit integral iteration method, as shown in Figure 19.

The missing nodes are obtained by the algorithm to determine the original missing model of the turbine blade. Table 3 is an excerpt from the coordinates of the nodes of the damaged blade. In total, 394 nodes of the original model are missing.

According to the identified missing point cloud, an incomplete entity is generated. The coordinates and normal vector of the welding points are calculated by the tool contact point path hatching method, and the coordinates and attitude of the machine gun head at each point are obtained. It is necessary to ensure that, with the robot processing attitude, the laser beam is perpendicular to the processing surface. However, in the actual machining, the laser beam axis and the normal of the processing surface should have a certain angle to avoid laser beam reflection into the processing head, which would cause damage to the optical path. The tip of the gun should be prevented from melting after the end of cladding. If the angle is too large, it would cause accumulation and jitter, thus affecting the surface quality. Generally, the path is extended outward by a distance, and then the points are sequentially connected to obtain the processing trajectory, as shown in Figure 20.

The set of coordinates needed to repair the path is extracted from Microsoft Excel and is imported as a curve into SolidWorks through the XYZ point function to generate the final machining path, as presented in Figure 21.

In this study, iso-parametric tool-path planning [33] is used to plan the machining path. The tool trajectory map obtained by iso-parametric tool-path planning is used for the generation of machining, as shown in Figure 22.

The trajectory line length is an important parameter for evaluating the tool trajectory. Once the trajectory is generated, its length is easily calculated. The trajectory lengths of the iso-parametric tool-path planning and point contact methods are calculated separately, and the results are presented in Table 4.

It can be observed from Table 4 that the tool contact point path section line method is shorter, mainly because the path spacing is determined by the curvature of the processing surface when the other processing parameters are the same. The iso-parametric tool-path planning is the minimum distance between two adjacent trajectory lines, and thus, the overall trajectory line will appear dense and the number of trajectory lines will increase significantly. Therefore, the total length will increase significantly.