Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing

Abstract

Grinding is commonly used for machining parts made of hard or brittle materials with the intent of ensuring a better surface finish. The material removal ability of a grinding wheel depends on whether the wheel surface is populated with a sufficiently high number of randomly distributed active abrasive grains. This condition is ensured by performing dressing operations at regular time intervals. The effectiveness of a dressing operation is determined by measuring the surface topography of the wheel (regions and their distributions on the grinding wheel work surface where the active abrasive grains reside). In many cases, image processing methods are employed to determine the surface topography. However, such procedures must be able to remove the regions where the abrasive grains do not reside while keeping, at the same time, the regions where the abrasive grains reside. Thus, special kinds of image processing techniques are needed to distinguish the non-grain regions from the grain regions, which requires a heavy computing load and long duration. As an alternative, in the framework of the “Biologicalisation in Manufacturing” paradigm, this study employs a bio-inspiration-based computing method known as DNA-based computing (DBC). It is shown that DBC can eliminate non-grain regions while keeping grain regions with significantly lower computational effort and time. On a surface of size 706.5 μm in the circumferential direction and 530 μm in the width direction, there are about 7000 potential regions where grains might reside, as the image processing results exhibit. After performing DBC, this number is reduced to about 300 (representing a realistic estimate). Thus, the outcomes of this study can help develop an intelligent image processing system to optimize dressing operations and thereby, grinding operations.

Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

“…For this, the instrument first sets an xy-mesh (Figure 2c) and obtains the height information of the surface (Figure 2d). In addition to height information, the instruments can represent a mesh using a pixel [29]. As such, two types of information are produced.…”

“…The other is the images of the measured surface, as shown in Figure 3a-c. In particular, Figure 3a shows a raw image of an arbitrary surface [29]. Figure 3b shows a binary image [29].…”

Special Issue of the Manufacturing Engineering Society 2020 (SIMES-2020)

“…Regarding the specific contributions of the Special Issue in the journal Materials, 17 contributions about cutting-edge advances in different fields of the manufacturing engineering have been collected. In particular, these concern advances and innovations in manufacturing processes [6][7][8][9][10][11]; additive manufacturing and 3D printing [12,13]; sustainable and green manufacturing [14,15]; metrology and quality in manufacturing [16,17]; manufacturing of new materials [18]; manufacturing systems: machines, equipment and tooling [19]; robotics, mechatronics and manufacturing automation [20]; Industry 4.0 [21]; design, modeling and simulation in manufacturing engineering [22]. Figure 2 shows the main topics and their percentages in this journal.…”

“…Concretely, within the topic "Advances and innovations in manufacturing processes", the works are focused on manufacturing processes. Mainly, machining [6][7][8], forming [9], joining [10] and electroplating [11]. In machining, Kubo et al [6] use bio-inspired DNA-based computing for determining surface topography of a dressed grinding wheel; Wood et al [7] analyze the machinability of inconel718 alloy with a porous microstructure produced by laser melting powder bed fusion at higher energy densities; and Navarro-Mas et al [8] compare different parameters to evaluate the delamination produces in the edge trimming of basalt fiber reinforced plastics.…”

“…Mainly, machining [6][7][8], forming [9], joining [10] and electroplating [11]. In machining, Kubo et al [6] use bio-inspired DNA-based computing for determining surface topography of a dressed grinding wheel; Wood et al [7] analyze the machinability of inconel718 alloy with a porous microstructure produced by laser melting powder bed fusion at higher energy densities; and Navarro-Mas et al [8] compare different parameters to evaluate the delamination produces in the edge trimming of basalt fiber reinforced plastics. In forming, Merayo et al [9], in order to characterize the plastic behavior of aluminum alloys, predict the mechanical Among all of them, the topic "Advances and innovations in manufacturing processes" stands out for the number of contributions it has had in this Special Issue (representing 35% of all of them), followed by the topics "Additive manufacturing and 3D printing", "Sustainable and green manufacturing" and "Metrology and quality in manufacturing" (with a 12% each).…”