\n This paper presents the recent advancements and forthcoming challenges for abrasive machining with specific focus on the advancement of industrial applications. The most significant advancement of abrasive machining is in grinding applications of cubic boron nitride (CBN) abrasive. The advancement of CBN wheels, application of grinding models and simulation tools, development of high stiffness multi-axis grinding machines, and high-speed spindles have contributed to the growing industrial applications of grinding with plated and vitrified CBN wheels. Sustainability of abrasive machining also received more attention during the past two decades as global Fortune 500 corporations have included sustainability as a corporate goal. Abrasive machining will continue to be a critical process for manufacturing precision components in the decades to come. The advancement and adoption of additive manufacturing creates more unique challenges for abrasive machining of complex geometrical features which were impossible a few years ago. Furthermore, strategies for abrasive machining are needed to utilize the massive amount of process data available by connected factories. Therefore, it is expected that sustainability and data analytics for abrasive machining will become a more important focus for various manufacturers.

Recent Advancements in Machining With Abrasives

The helical groove, which has a complex shape in the radial section, strongly affects the performance of solid ceramic mills. The accuracy of shaping is strongly affected by the size of the grinding wheel and the kinematic parameters of its movement and rotation relative to the workpiece. Although it is possible to obtain the aforementioned properties using the existing methods, there does not exist a general approach the determination the acceptable ranges of the diameter of the grinding wheel and radius of curvature of its profiling section. In this paper we have analyzed the main features of the design of the profiling sections of grinding wheels, have formed a set of initial parameters that most efficiently implement the trajectory of the shaping movement, which in turn constitutes a new general method of shaping for the helical surfaces of ceramic cutters, have analyzed the character of rolling of a set of profiling sections on a helical surface, have derived new analytical relationships determining impacts of the grinding wheel design and parameters controlling the trajectory of the grinding wheel movement on the shape of the cutter profile in the axial section, and have demonstrated advantages of the developed general approach in designing a new ceramic mill. The new method of shaping allows to unambiguously realize movements of the grinding wheel on various machine layouts and to exclude the displacement of the shaping point by replacing rotation with displacement. Further development of this method will make it possible to replace five- coordinate machines with four-coordinate ones when manufacturing milling cutters with a helical surface that can significantly reduce the production costs.

High-precision method for determining the optimal trajectory of movement of a conical grinding wheel relative to the helical groove of solid ceramic mills

Groove is a key structure of high-performance integral cutting tools. It has to be manufactured by 5-axis grinding machine due to its complex spatial geometry and hard materials. The crucial manufacturing parameters (CMP) are grinding wheel positions and geometries. However, it is a challenging problem to solve the CMP for the designed groove. The traditional trial-and-error or analytical methods have defects such as time-consuming, limited-applying, and low accuracy. In this study, the problem is translated into a multiple output regression model of groove manufacture (MORGM) based on the big data technology and AI algorithms. The inputs are 34 groove geometry features, and the outputs are 5 CMP. Firstly, two groove machining big data sets with different range are established, each of which is includes 46,656 records. They are used as data resource for MORGM. Secondly, 7 AI algorithms, including linear regression, k nearest-neighbor regression, decision trees, random forest regression, support vector regression, and ANN algorithms, are discussed to build the model. Then, 28 experiments are carried out to test the big data set and algorithms. Finally, the best MORGM is built by ANN algorithm and the big data set with a larger range. The results show that CMP can be calculated accurately and conveniently by the built MORGM. 

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Big data-oriented wheel position and geometry calculation for cutting tool groove manufacturing based on AI algorithms

Abstract For tool grinding, an optimization potential in terms of productivity is available using graded grinding wheels. This is especially the case when grinding flutes in cemented carbide. Due to the properties of the cemented carbide, high radial wear occurs. Depending on the geometry of the flute, the radial wear varies along the engagement width of the grinding tool. The varying radial wear can be compensated by load adapting the grinding tool properties along its engagement width. The adaptation of the wear behaviour to non-uniform engagement conditions results in non-uniform radial wear occurring under uniform engagement conditions. Indeed, this is the case when dressing these grinding tools. Therefore, this publication presents a method that enables the dressing of graded grinding wheels. For this purpose, four differently graded grinding wheels and two non-graded grinding wheels are dressed with SiC-rolls. Different SiC-rolls, as well as dressing parameters, are investigated. The contour accuracy after dressing is analysed using contour profiles in polyurethane. In the presented paper, dressing parameters are proposed which allow reproducible dressing of all grinding wheels used. 

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Dressing of hybrid-bonded graded diamond grinding wheels

Abstract Grinding the flutes of cemented carbide end mill cutters results in high and uneven radial wear of the grinding wheel. This is a consequence of the varying geometrical contact conditions over the grinding wheel width. Decreased manufacturing accuracy regarding the tool target geometry is the consequence. To compensate for this, the dressing intervals must be shortened. High non-productive times and an additionally reduction of the grinding layer results from this. The higher non-productive times reduce the productivity of the grinding process and, in conjunction with the shorter lifetime of the grinding tool, thus increase the costs per work piece. It has already been shown that load-adapted grinding wheels can reduce uneven radial wear up to 50%. The adaptation of the wear behaviour to non-uniform engagement conditions causes non-uniform radial wear to occur again under uniform engagement conditions. Uniform engagement conditions occur during grinding wheel sharpening. Therefore, the present study investigates the influence of sharpening on the grinding tool topography. For this purpose, sharpening tests are carried out on four differently graded grinding wheels. For comparability, the tests are also carried out on two non-graded grinding wheels. In the present work, the surface parameters are evaluated with regard to their suitability for analysing the sharpening condition of graded grinding wheels. The grain protrusion derived from this is subsequently used for evaluation. For this, a dependency on the grain concentration is proven and the grain protrusions are shown over the grinding wheel widths. From this, sharpening parameters are determined that enable reproducible sharpening of graded grinding wheels. 

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A. Muthulingam

Papers

Increasing the productivity and quality of flute grinding processes through the use of layered grinding wheels