This study presents a coupled thermo-mechanical simulation and data-driven optimization framework for cryogenic minimum quantity lubrication (cryogenic-MQL) machining of laser-cladded mining components. The residual-stress field and plastic history after cladding and cooling are transferred to the cutting model. Paired simulations isolate initial residual-stress effects while keeping geometry and plastic history unchanged. The maximum absolute changes are 1.18
In-situ measurement of cutting tool wear is essential for understanding wear mechanisms and for improving machining reliability and process efficiency. However, most established measurement methods require periodic interruption of machining operations and tool removal, which prevents continuous observation of wear progression and may introduce measurement inconsistencies. This work presents a robot-assisted optical approach for the in-situ measurement of tool wear development directly on a CNC (Computer Numerical Control) machine. The system combines a chromatic confocal point sensor with a six-axis industrial robot and two precision linear stages. A tool coordinate system derived from the unworn tool geometry is introduced to compensate for robot positioning uncertainties and to ensure repeatable measurements of identical tool regions. The width of flank wear land is determined automatically from line-scan profiles using a segmentation-based algorithm. For flank wear land width determination, a standard deviation between 2.2 μm and 12.5 μm are obtained, which is mostly below the target uncertainty threshold of 10 μm (3
Industry 4.0 revolutionizes manufacturing by integrating Cyber-Physical Systems (CPS), the Industrial Internet of Things (IoT), and Artificial Intelligence (AI) to enable Smart Manufacturing by focusing on reducing downtime, enhancing efficiency, and so forth. On the one hand, this requires a critical focus on scheduling jobs to optimize resource utilization; on the other hand, it requires a suitable maintenance strategy, including the optimization of spare parts inventory management. Traditional scheduling models often overlook Preventive Maintenance (PM) activities and rely on periodic or reactive maintenance, leading to avoidable consequences. In many cases, these functionalities are treated in isolation, i.e., lacking a unified framework for a flow shop production environment. As part of the current work, an attempt is made to integrate job and PM scheduling, and subsequently, a novel optimization approach is proposed to jointly schedule jobs and PMs to enhance machine utilization while ensuring machine reliability. A mathematical formulation and a Genetic Algorithm (GA) approach are presented to address integrated job and PM scheduling under numerous constraints. Additionally, the GA approach is benchmarked against other meta-heuristic approaches, such as Differential Evolution (DE), Tabu Search (TS), and Simulated Annealing (SA). The joint scheduling of jobs and PM is validated in an automotive electronics manufacturing industry. The proposed models were tested and validated against available flow-shop benchmark data, as industrial data was not permitted to be published. The data was appended to include PM data, job priorities, and so on. The computational experiments demonstrate the effectiveness of these meta-heuristic approaches in balancing production efficiency with adherence to maintenance requirements by solving large-scale instances. The results highlight the benefits of integrated job and PM scheduling, which help develop resilient, data-driven smart manufacturing systems. The proposed approach is the first step in the integrated scheduling of jobs and PM. Maintenance activities inherently require spare parts, which, in turn, drive substantial inventory levels and associated costs. Subsequently, the next steps would be to predict PM plans based on historical data and thereby optimize the spare parts inventory level. This would integrate the job scheduling, PM scheduling, and spare parts inventory optimization.
Production equipment is often replaced or disposed of before its end of life, leading to an increase in waste and resource demand and increased cost for the procurement of new machinery. Transitioning to a circular economy is a possible solution to reduce the environmental impact by reusing and reselling production equipment. The adoption of reuse practices, however, requires a common information model documenting environmental data, terms, and descriptions related to the production equipment to assess the environmental impact compared to newly built machines. Existing ontologies, such as the Manufacturing Resource Capability Ontology (MaRCO), provide a structured representation of technical parameters of production equipment, but lack the integration of environmental information. To close this gap, this paper presents a selection of key performance indicators relevant to the environmental assessment of reused and new production equipment, along with an extension of an existing production equipment ontology to include these indicators. The extension is implemented and demonstrated in a use case study. The resulting ontology provides a baseline to reinforce the circularity of production equipment by enabling the assessment of environmental benefits.
