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In spite of the success of the stream of variation (SoV) approach to modeling variation propagation in multistation machining processes (MMPs), the absence of machininginduced variations could be an important factor that limits its application in accurate variation prediction. Such machining-induced variations are caused by geometricthermal effects, cutting-tool wear, etc. In this paper, a generic framework for machininginduced variation representation based on differential motion vectors is presented. Based on this representation framework, machining-induced variations can be explicitly incorporated in the SoV model. An experimentation is designed and implemented to estimate the model coefficients related to spindle thermal-induced variations and cutting-tool wear-induced variations. The proposed model is compared with the conventional SoV model resulting in an average improvement on quality prediction of 67%. This result verifies the advantage of the proposed extended SoV model. The application of the new model can significantly extend the capability of SoV-model-based methodologies in solving more complex quality improvement problems for MMPs, such as process diagnosis and process tolerance allocation, etc.

In product design and quality improvement fields, the development of reliable 3D machining variation models for multi-station machining processes is a key issue to estimate the resulting geometrical and dimensional quality of manufactured parts, generate robust process plans, eliminate downstream manufacturing problems and reduce ramp-up times. In the literature, two main 3D machining variation models have been studied: the Stream of Variation (SoV) model, oriented to product quality improvement (fault diagnosis, process planning evaluation and selection, etc.), and the Model of the Manufactured Part (MoMP), oriented to product and manufacturing design activities (manufacturing and product tolerance analysis and synthesis). This paper reviews the fundamentals of each model and describes step by step how to derive them using a simple case study. The paper analyzes both models and compares their main characteristics and applications. A discussion about the drawbacks and limitations of each model and some potential research lines in this field are also presented.

As reported by many professional bodies responsible for accrediting engineering programs, today's engineering graduates present important limitations in the practice of engineering because current engineering curricula is still too focused on fundamental engineering science without providing sufficient integration to industrial practice. To overcome these limitations, active learning approaches have been applied in the literature with positive results in engagement, motivation and student's performance. In this paper, we propose a project based learning approach with real manufacturing activities in a 4-year mechanical course to improve the learning process. The goal of the project is to plan the manufacturing process of a real part and conduct at shop-floor levels all the activities required. The experience was evaluated considering project/exam grades, questionnaires and manufacturing quality. The results showed an increase in student's satisfaction, improvement in the exam performance, and a clearly increase in student's enrolment in the manufacturing master degree.

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.