Comparative study of Sustainability Metrics for Face Milling AISI 1045 in different Machining Centers

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)The objective of this study is to compare a set of sustainability metrics between different manufacturing resources applied to high performances machining centers. The research compares distributed scenarios in order to find the optimal conditions that allow the minimum consumed power and the minimum roughness when performing face milling operations of AISI 1045 steel. The set of experiments for the surface machining was carried out considering different path strategies in three main directions for two dimensional movements of the tool. The selected experiments considered the main axis movement, the perpendicular axis movement and a 45 degrees movement. Besides, it was considered the feed rate speed and the cutting depth. The design of experiments was developed with the Taguchi method considering an orthogonal matrix of L27 design type, and three levels of experimental design, and the analysis of variance and noise signal were performed. The methodology to determine the lowest power consumed and the best surface quality allowed to establish the working condition in the most sustainable machining. The results show how the cutting parameters influence in each manufacturing resource

Cutting parameters optimisation in milling: expert machinist knowledge versus soft computing method

In traditional machining operations, cutting parameters are usually selected prior to machining according to machining handbooks and user’s experience. However, this method tends to be conservative and sub-optimal since part accuracy and non machining failures prevail over machining process efficiency. In this paper, a comparison between traditional cutting parameter optimisation by an expert machinist and an experimental optimisation procedure based on Soft Computing methods is conducted. The proposed methodology increases the machining performance in 6.1% and improves the understanding of the machining operation through the use of Adaptive Neuro-fuzzy Inference System

Derivation and application of the stream of variation model to the manufacture of ceramic floor tiles

One of the main problems in the manufacture of floor tiles is the dimensional variability of the ceramic product, which leads to the product having to be classified into different dimensional qualities with an increase in cost. In this paper we propose a novel way of modelling the dimensional variability of ceramic floor tiles by the adaptation of the Stream of Variation model. The proposed methodology and its potential applicability contributes to the integration of process knowledge in the ceramic tile industry and allow tile manufacturers have a new methodology for process improvement, variation reduction and dimensional control

Project-based experience through real manufacturing activities in mechanical engineering

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

Variation propagation modelling for multi-station machining processes with fixtures based on locating surfaces

Modelling the dimensional variation propagation in multi-station machining processes (MMPs) has been studied intensively in the past decade to understand and reduce the variation of product quality characteristics. Among others, the Stream-of-Variation (SoV) model has been successfully applied in a variety of applications, such as fault diagnosis, process planning and process-oriented tolerancing. However, the current SoV model is limited to the MMPs where only fixtures with punctual locators are applied. Other types of fixtures, such as those based on locating surfaces, have not been investigated. In this paper, the derivation of the SoV model is extended to model the effect of fixture- and datum-induced variations when fixtures with locating surfaces are applied. Due to the hyperstatic nature of these fixtures, different workholding configurations can be adopted. This will increase the dimension of the SoV model exponentially and thus may make the model-based part quality prediction extremely complex. This paper presents a method of reducing the complexity of the SoV model when fixtures based on locating surfaces are applied and evaluates the worst-case approach of the resulting part quality

Model-based observer proposal for surface roughness monitoring

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels

Sustainable machining of molds for tile industry by minimum quantity lubrication

Nowadays, to reduce water pollution, soil contamination, and human health hazards, the environmental legislation is forcing manufacturing companies to avoid the use of metalworking fluids. Thus, the adoption of the dry machining and minimum quantity lubrication (MQL) techniques is becoming essential. However, small and medium companies are having difficulties and are skeptical about the adoption of these new techniques. In this study, a methodology is proposed to implement an MQL system for sustainable machining with a step-by-step procedure that facilitates its industrial application. The methodology is divided into three steps: i) MQL configuration to verify its effect on surface roughness, considering the effective flow rates and nozzle position; ii) process modeling based on the Box–Behnken design of experiments (DoE) to model surface roughness, power consumption, and tool life; and iii) process optimization for minimizing cost and environmental impact in terms of water usage and kg of CO2 equivalent. The methodology is applied in the manufacturing process of a component of a mold for the tile industry. Different alternatives are analyzed and the best alternative in both economic and environmental aspects is the use of the MQL system with optimal cutting parameters and an early tool change strategy that ensures part quality without subsequent grinding operations

Process-oriented tolerancing using the extended stream of variation model

Current works on process-oriented tolerancing for multi-station manufacturing processes (MMPs) have been mainly focused on allocating fixture tolerances to ensure part quality specifications at a minimum manufacturing cost. Some works have also included fixture maintenance policies into the tolerance allocation problem since they are related to both manufacturing cost and final part qual- ity. However, there is a lack of incorporation of other factors that lead to increase of manufacturing cost and degrade of product quality, such as cutting-tool wear and machine-tool thermal state. The allocation of the admissible values of these process variables may be critical due to their impact on cutting-tool replacement and quality loss costs. In this paper, the process-oriented tolerancing is ex- panded based on the recently developed, extended stream of variation (SoV) model, which explicitly represents the influence of machining process variables in the variation propagation along MMPs. In addition, the probability distribution functions (pdf) for some machining process variables are ana- lyzed, and a procedure to derive part quality constraints according to GD&T specifications is also shown. With this modeling capability extension, a complete process-oriented tolerancing can be con- ducted, reaching a real minimum manufacturing cost. In order to demonstrate the advantage of the proposed methodology over a conventional method, a case study is analyzed in detail

Variation propagation of bench vises in multi-stage machining processes

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)Variation propagation has been successfully modeled by the Stream of Variation (SoV) approach in multistage machining processes. However, the SoV model basically supports 3-2-1 fixtures based on punctual locators and other workholding systems such as conventional vises are not considered yet. In this paper, the SoV model is expanded to include the fixture- and datum-induced variations on workholding devices such as bench vises. The model derivation is validated through assembly and machining simulations on Computer Aided Design software. The case study analyzed shows an average error of part quality prediction between the SoV model and the CAD simulations of 0.26%

Manufacturing variation models in multi-station machining systems

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 model, oriented to product quality improvement (fault diagnosis, process planning evaluation and selection, etc.), and the model of the manufactured part, 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