Guillem Quintana

Surface Roughness Generation and Material Removal Rate in Ball End Milling Operations

Surface roughness plays an important role in the performance of a finished part. Surface roughness generated in machining operations is influenced by an enormous set of factors such as cutting parameters, cutting tool characteristics, workpiece properties, or cutting phenomena. Cutting geometric characteristics, when ball end mill is used, clearly affect surface crests generated. In this paper, we study the influence of the geometric characteristics of ball end mill cut on the theoretical surface roughness obtained. The crests height on the surface (h) and roughness average parameter (Ra) are calculated as function of cutting tool radius (R) and radial depth of cut (Ae). Surface angularity is also considered. This work also analyzes cutting parameters implication on material removal rate (MRR) of ball end milling operations. The equations and especially the figures presented in this paper can be easily applied in workshops to improve quality and productivity of ball end milling operations. Finally, experimentation carried out permits to observe and quantify the deviations of the theoretical approach.

Modelling Power Consumption in Ball-End Milling Operations

Power consumption is a factor of increasing interest in manufacturing due to its obvious impact on production costs and the environment. The aim of this work is to analyze the influence of process parameters on power consumption in high-speed ball-end milling operations carried out on AISI H13 steel. A total of 300 experiments were carried out in a 3-axis vertical milling center, the Deckel-Maho 105 V linear. The power consumed by the spindle and by the X, Y, and Z machine tool axes was measured using four ammeters located in the respective power cables. The data collected was used to develop an artificial neural network (ANN) which was used to predict power consumption during operations. The results obtained from the ANN are very accurate. Power consumption predictions can help operators to determine the most effective cutting parameters for saving energy and money while bringing the milling process closer to the goal of environmentally sensitive manufacturing which has become a topic of general importance.

Prediction, monitoring and control of surface roughness in high-torque milling machine operations

The development and testing of an application that will predict, monitor and control surface roughness are described. It comprises three modules for off-line roughness prediction, surface roughness monitoring and surface roughness control, and is especially designed for high-torque, high-power milling operations, which are widely used nowadays in the manufacture of wind turbine components. The application is tested in a milling machine with a high working volume. Due to the highly complex phenomena that generate surface roughness and the large number of factors that interact during the cutting process, models to calculate the average surface roughness parameter (Ra) are based on artificial neural networks (ANN) as they are especially suitable for modelling complex relationships between inputs and outputs.

Machine Tool Spindles

This chapter deals with spindle technologies for machine tools. The machine tool spindle provides the relative motion between the cutting tool and the workpiece which is necessary to perform a material removal operation. In turning, it is the physical link between the machine tool structure and the workpiece, while in processes like milling, drilling or grinding, it links the structure and the cutting tool. Therefore, the characteristics of the spindle, such as power, speed, stiffness, bearings, drive methods or thermal properties, amongst others, have a huge impact on machine tool performance and the quality of the end product. Machining requirements differ greatly from one sector to another in terms of materials, cutting tools, processes and parameters. Nowadays, the spindle industry provides a large variety of configurations and options in order to meet the needs of different industries. Therefore, it is crucial that companies correctly identify their machining requirements and make well-informed decisions about which spindle to acquire. In this chapter, some of the main spindle characteristics that are the basis of a wellinformed decision regarding spindles are introduced and discussed.

A decision-making tool based on decision trees for roughness prediction in face milling

The selection of the right cutting tool in manufacturing process design is always an open question, especially when different tools are available on the market with similar characteristics, but marked differences in price, ranging from low-cost to high-performance cutting tools. The ultimate decision of the engineer will depend on previous experience with the life cycle of the tool and its performance, but without the support of a systematic knowledge base. This research presents a decision-making system based on soft-computing techniques. First, several experiments were carried out with four different cutting tools: two flat-milling low-cost tools without any surface treatment or coating and two high-performance, high-cost cutting tools (in both cases with four cutting edges, similar geometrical features and diameters). Three different measures of tool wear are considered in the context of real workshop conditions: on-line power consumption, cutting length and volume of cut material. Finally, decision trees have been selected as the most suitable technique for building a decision-making system for two reasons: these trees show higher accuracy for the prediction of roughness in terms of tool wear and tool type. They also provide useful visual feedback on the information that is extracted from the real data, which can be directly used by the process engineer.

Experimental Introduction to Surface Roughness Parameters Measurement

Surface roughness influences the performance of a finished part. In machining operations, the surface roughness generated is influenced by an enormous set of factors. In ball end milling operations, the geometric characteristics of the cut clearly affect the surface crests generated. This paper presents an experimental methodology that permits engineering students to identify and analyze the surface roughness. The methodology is applicable to training courses and surface texture generation as well.

Cutting Tool Selection through Tool Wear, Cost, Power Consumption and Surface Roughness Analyses

The current requirements for an efficient dimensional inspection of manufactured parts have lead to development of different in process and on-machine measurement (OMM) techniques. Touch trigger probes (TTP) are the most common technologies utilized, inspired on contact probes used on coordinate measuring machines (CMMs). The on-machine accuracy of TTPs depends upon precision of the tool-machine control as well as upon the procedure for TTP presetting. Taking this into account, a different OMM technique is considered in this work, which consists on a laser micrometer (LM) that is commonly used for in-process measurement of continuous products. The behaviour of TTP and LM is analysed and discussed in terms of repeatability and reproducibility. Results obtained by both techniques are compared each other by measuring a cylindrical workpiece and by checking the results with those obtained on a CMM.

Experimental Introduction to Forced and Self-Excited Vibrations in Milling Processes and Identification of Stability Lobes Diagrams

In milling operations, cutting edge impacts due to the interaction between the cutter and the workpiece excite vibrations. It is possible to distinguish between free, forced and self-excited vibrations. Chatter is a self-excited vibration that can occur in machining processes, and is considered to be a common limitation of productivity and quality. Stability lobes diagrams (SLDs) show the frontier between chatter-free milling operations, i.e. stable dominated by forced vibrations, and operations with chatter, i.e. unstable. These diagrams are usually obtained from impact hammer testing. However, this method requires trained personnel with advanced knowledge and it is not easily applied in engineering studies or operator training. This paper presents an experimental method that allows engineering students and operators-in-training to observe the chatter phenomenon and to distinguish between forced and chatter vibrations and identify process stability diagrams.