Manufacturing processes have to become more sustainable. For grinding processes, this means that tool wear and performance need to be critically evaluated in their economic, environmental, and social impact. Tool wear affects several stakeholders. Different wear mechanisms on the grit and bond level lead to a change in tool profile and sharpness. For the user, wear changes tool costs, process stability, and maybe worker safety. Tool manufacturers need tool wear to sell replacements, whereas tool users might not like the higher waste and costs from tool wear but need tool self-sharpening.

Grinding is an important abrasive machining process at the end of many process chains. Understanding energy transformation in grinding is not only important to improve energy efficiency but also crucial for understanding the chip formation process itself. Grinding energy can be studied at the macroscopic or microscopic levels, wherein the entire grinding tool is considered or the phenomena at the single cutting edges are studied. This paper explores existing energy modeling approaches in grinding with particular emphasis on physical models. Models on energy transformation during the ductile grit–workpiece engagement for three regimes —being friction, plowing, and shearing —are explained. In addition to the critical depth of cut (DOC) when chip formation starts, a critical depth when plowing begins is introduced to divide between the different regimes. Selected models for each regime are combined to an integrated grinding energy model that allows researchers to investigate forces and energy during grit engagement.

Product improvement, usually through changes in design and functionality, is relying more and more on the continuous analysis of large amounts of data. Product data can come from many sources with varying effort in obtaining the data, e.g., condition monitoring and maintenance data. Intelligent products, also known as “product embedded information devices” (PEID), are already equipped with sensors and onboard computing capabilities and therefore able to generate valuable data such as the number of user interactions during the use phase. The internet of things (IoT) makes data transfer possible at any time to close the loop for the product lifecycle data and methods like machine learning promote new uses of those data. This paper proposes a methodology to capture the most relevant data on product use and human–product interaction automatically and utilize it as part of data-driven product improvement. Product engineers and designers will gain insights into the use phase and can derive design changes and quality improvements. The methodology guides the user through research on product use dimensions based on the principles of user-centered design (UCD). The findings are applied to define what usage elements, such as specific actions and context, need to be available from the use phase. During systems development, machine learning is suggested to fuse sensor data to efficiently capture the usage elements. After product deployment, use data are retrieved and analyzed to identify the improvement potential. This research is a first step on the long way to self-optimizing products.

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.