A Model to Improve the Quality Products

The topic of this paper is to present a solution who can improve product quality following the idea: “Unlike people who have verbal skills, machines use "sign language" to communicate what hurts or what has invaded their system’. Recognizing the "signs" or symptoms that the machine conveys is a required skill for those who work with machines and are responsible for their care and feeding. The acoustic behavior of technical products is predominantly defined in the design stage, although the acoustic characteristics of machine structures can be analyze and give a solution for the actual products and create a new generation of products. The paper describes the steps in technological process for a product and the solution who will reduce the costs with the non-quality of product and improve the management quality.improvement quality, iceberg quality costs, virtual simulation, model product design, low design product

A Provocation for Quality Product

The new problems on global market bring a wind of changes in our lives, as well in technology, in environment protection, the future provoke us to find immediately solution and identify new sources for industry. The solution to this problem is the improvement of quality management of enterprises and also of products which must be designed taking into consideration the protection of environment. From this point of view we have to change the total quality management.Also the target of this research is to identify new solutions and recycle the possibilities of the materials.The intense search for solutions, the needs for a system of approach, the use of knowledge or models, can be used as a measure to reduce variations between different countries and develop a new system inside the universities which implemented a new eco age and which are preparing the new generation to redesign the mantra of this new eco-age and its green products.A solution to this challenge, and an explanation of applying a sustainable strategy under the principles of quality and continuous improvement in research work done in our universities will be presented in this article, as well as some solutions which guarantee the efficiency as a result to the new challenges in our market place and technological processquality, technological process

A Model to Improve the Quality Products

The topic of this paper is to present a solution who can improve product qualityfollowing the idea: “Unlike people who have verbal skills, machines use "sign language"to communicate what hurts or what has invaded their system’. Recognizing the "signs"or symptoms that the machine conveys is a required skill for those who work withmachines and are responsible for their care and feeding. The acoustic behavior of technical products is predominantly defined in the design stage, although the acoustic characteristics of machine structures can be analyze and give a solution for the actual products and create a new generation of products. The paper describes the steps intechnological process for a product and the solution who will reduce the costs with the non-quality of product and improve the management quality

How Wastes Influence Quality Management

Companies are often surprised to learn that only a fraction of their activities actually add value for their customers. A primary cause of waste is information deficits – employees simply lack the knowledge they need to do their jobs efficiently and effectively. This leads employees to waste valuable time and motion searching, waiting, retrieving, reworking or just plain future action. Companies are able to respond to changing customer desires with high variety, high quality, low cost, and with very fast throughput times. Eliminating waste along entire value streams, instead of at isolated points, creates processes that need less human effort, less space, less capital, and less time to make products and services at far less costs and with much fewer defects, compared with traditional business systems. Companies are able to respond to changing customer desires with high variety, high quality, low cost, and with very fast throughput times

The effects of cutting speed and feed rate on BUE-BUL formation, cutting forces and surface roughness when machining AA6351 (T6) alloy

WOS: 000258426700003In this paper, the effects of machining parameters such as cutting speed and feed rate on BUE, BUL, main cutting force and surface roughness were experimentally investigated. Optimal and critical cutting parameters were determined. It was found that the cutting speed must be selected above 400-500 m/min in order to prevent BUE and BUL formation when machining of AA6351 (T6) alloy with uncoated carbide inserts. The results of this study show that the most important parameter affecting main cutting force and surface roughness is feed rate. As a result of this study, optimum cutting force and optimum feed rate were found in order to minimize surface roughness of the work piece. (C) 2008 Journal of Mechanical Engineering. All rights reserved

Investigation of the machinability of SiC reinforced MMC materials produced by molten metal stirring and conventional casting technique in die-sinking electrical discharge machine

In this study, MMC materials were produced by stir casting method using AA7075 alloy matrix and silicon carbide (SiC) as reinforcement elements in order to investigate the effect of die-sinking electric discharge machining (EDM) process parameters for various weight percentage reinforcement (10% 14% and 18%) used MMC. EDM with three different pulse-on time (Time on) (25, 50 &75 mu s) and discharge current value (2, 4 & 6 Amperes) and at constant dwell time (20 mu s) and depth of cut (0.5 mm). After machining process, the effects of process parameters on processing time, average surface roughness, hole diameter and the weight loss were investigated. As a result of the study, highest values, the lowest values of processing time, hole diameter and average surface roughness were obtained for various weight percentage reinforcement MMC respectively. Ideal MMC, discharge current value and time-on duration for average surface roughness, hole diameter, processing time and material wear loss according to signal to noise ratio were determined. According to ANOVA results, R-2 values for the average surface roughness, hole diameter, processing time and material wear loss were calculated as 89.55%, 98.46%, 89.85% and 24.06% respectively.Karabuk University Rectorate and Scientific Research Projects (BAP) Management CoordinatorsKarabuk University [KBU-BAP-15/1-DR-027]I would like to thank Karabuk University Rectorate and Scientific Research Projects (BAP) Management Coordinators for their financial support within the scope of KBU-BAP-15/1-DR-027.WOS:0005794486000022-s2.0-8508671223

Hindawi Publishing Corporation Modelling and Simulation in Engineering Volume

Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm), various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm), and different feedrates (100, 130, 160, 190, 220 mm/min) on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN). Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG) model with 9 neurons in hidden layer has produced absolute fraction of variance (R 2 ) values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning

Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning

Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm), various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm), and different feedrates (100, 130, 160, 190, 220 mm/min) on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN). Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG) model with 9 neurons in hidden layer has produced absolute fraction of variance (R2) values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning

Analysis of Surface Roughness and Flank Wear Using the Taguchi Method in Milling of NiTi Shape Memory Alloy with Uncoated Tools

The aim of this study was to optimize machining parameters to obtain the smallest average surface roughness (Ra) and flank wear (Vb) values as a result of the surface milling of a nickel-titanium (NiTi) shape memory alloy (SMA) with uncoated cutting tools with different nose radius (rε) under dry cutting conditions. Tungsten carbide cutting tools with different rε (0.4 mm and 0.8 mm) were used in milling operations. The milling process was performed as lateral/surface cutting at three different cutting speeds (Vc) (20, 35 and 50 m/min), feed rates (fz) (0.03, 0.07 and 0.14 mm/tooth) and a constant axial cutting depth (0.7 mm). The effects of machining parameters in milling experiments were investigated based on the Taguchi L18 (21 × 32) orthogonal sequence, and the data obtained were analyzed using the Minitab 17 software. To determine the effects of processing parameters on Ra and Vb, analysis of variance (ANOVA) was used. The analysis results reveal that the dominant factor affecting the Ra is the cutting tool rε, while the main factor affecting Vb is the fz. Since the predicted values and measured values are very close to each other, it can be said that optimization is correct according to the validation test results

Taguchi optimization of surface roughness in the turning of Hastelloy C22 super alloy using cryogenically treated ceramic inserts

Cryogenic treatment has been used in recent years to improve the performance of cutting tools. This study evaluated the machinability of a nickel-molybdenum-based super alloy using cryogenically treated (-80 celcius and -145 celcius) ceramic inserts under dry turning conditions. Three cutting speeds (350, 400, and 450 m/min), three feed rates (0.1, 0.2, and 0.3 mm/rev), and a 1-mm fixed cutting depth were used in the turning tests. Experiments were conducted using the Taguchi orthogonal array L(27)design. The factors affecting the surface roughness (Ra) were determined via analysis of variance. The effect of cryogenic treatment type (shallow and deep), cutting speed, and feed rate on surface roughness was investigated. Results of the analysis determined that the feed rate was the major parameter that affected surface roughness and that the deep cryogenic treatment was more effective. The regression analysis confirmed that the experimental results and the predicted values were within the 95% confidence interval. The most effective parameter affecting the surface roughness was feed rate at a contribution of 57.9%. The contribution of the cutting tool type to the surface roughness was 28.5%. The results obtained showed that the surface roughness can be optimized for turning the Hastelloy c22 super alloy with the Taguchi method.WOS:0005610912000062-s2.0-8508965850