Issue 50

N. A. Fountas et alii, Frattura ed Integrità Strutturale, 50 (2019) 584-594; DOI: 10.3221/IGF-ESIS.50.49 585 I NTRODUCTION ue to their physical and mechanical properties copper and zinc alloys (brass) possess excellent machinability; high electric as well as thermal conductivity, significant resistance to corrosion and noticeable antibacterial properties. Consequently they are used in several industrial branches such as electronics, sanitary and automotive. Lead (Pb) is a key element added to Brass alloys to facilitate chip breakage, extend tool life and allow for wider applicable ranges of machining parameters [1]. On the other hand, it must be noted that in recent years the use of lead in brass alloys is restricted more and more, due to the legislation on protecting health and environment, since lead is a hazardous heavy metal. Alter- natively, numerous studies revealed that adding bismuth, selenium, indium, as well as graphite as substitution for lead improves the machinability of lead-free brasses. However, the manufacturing process of these materials is very complex and cost-intensive. Apart from that, low-leaded binary brass alloys, meeting the future requirements on maximum lead contents of 0.1%-0.25%, have been developed recently. In general, machinability of low-leaded brasses is significantly worse compared to leaded free-brass whilst given their chemical composition and microstructure, different machinability problems arise [2]. For the silicon alloyed materials, i.e. CuZn21Si3P (CW724R), tool wear is to be higher due to a silicon- rich and hard κ-phase in the microstructure. For other low-leaded brasses, such as CuZn42 (CW510L) and CuZn38As (CW511L), the main problems exist due to the formation of long chips and higher thermal as well as mechanical tool load [3]. Additionally, high adhesion tendency may result in chipping of the cutting edge and reduced work piece quality. Kato et al. [4] enhanced the chip evacuation in micro-drilling of CuZn21Si3P by optimizing tool geometry. Klocke et al. [2] and Nobel et al. [3] published approaches to enable high-performance cutting of low-leaded brass alloys. In Ref. [5] Nobel et al. investigates also the chip formation, the flow and its breakage in free orthogonal cutting. Metal cutting operations are widespread in manufacturing industry and the prediction and/or the control of the resulted machinability always is of great importance. One basic machinability parameter is the surface roughness, as it is closely associated with the quality, reliability and func- tional performance of components [6,7]. Another one is the cutting force system developed; it is needed for the estimation of power requirements and for the design of machine tool elements, tool-holders and fixtures, adequately rigid and free from vibration. Moreover, the cost of machining is strongly dependent on the rate of material removal, and this cost may be reduced by increasing the cutting speed and/or the feed rate; however, there are restrictions to the speed and feed values above which the life of the tool is shortened excessively. Axis-symmetrical components are primarily manufactured using turning operations. Due to the various factors influencing surface integrity in turning, non-competitive production times as well as poor surface finish may be experienced which in turn degrade functional behavior of turned components [8]. Therefore, as it occurs in any other metal cutting process and any other engineering material, finding the optimal process parameters is of paramount importance [8,9]. Arc-chain surface patterns are quite often in turning, yet; significant deviations may be observed owing to irregular chip formation phenomena such as discontinuous chip, built-up edge, low feeds, chat- tering and intense tool flank wear. Such occurrences are experienced especially when it comes to productivity or material selection limitations [6]. Manufacturing operations should be performed such that all aspects of design requirements for high performance, aesthetics and functionality are met. Obviously such requirements are directly related to dimensioning and tolerancing as well as surface texture indicators for ensuring longer lifespan for manufactured components and tribological functioning [10]. Despite the fact that arithmetic surface roughness average (Ra) and the maximum height of the profile (Rt) are not able to provide full information about the shape of profile they are two crucial indicators for judging surface finish. Characteristics like incli- nation and curvature of the surface roughness asperities, “emptiness” or “fullness” of the profile, distribution of the profile material at various heights are registered in the profile shape. The essential tribological aspects (e.g. friction, wear, state of lubrication) are highly dependent on profile shape [7,11-13]. Cutting forces occur when extreme conditions en- countered at the tool-workpiece contact [7,14,15]. This interaction can be related to tool wear and failure. As a result tool wear and cutting forces are related to each other. Surface roughness as well as cutting force monitoring is essential for evaluating the performance of machining processes, expand tool life and improve productivity. Nowadays, due to the de- velopment of computer technology, finite element and soft computing/artificial intelligence techniques are being used ex- tensively for modeling and optimization of machining processes. Soft computing/ artificial intelligence methods include neural network (NN), fuzzy set theory, genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) and particle swarm optimization (PSO); see Refs. [7,14]. The present study investigates the influence of cutting parameters on machinability indicators such as the main cutting force Fc and surface roughness parameters Ra and Rt when longitudi- nally turning CuZn39Pb3 brass alloy. Full quadratic regression models were developed to express the correlation of the machining conditions with the imparted machinability characteristics. Further on, an advanced artificial grey wolf optima- zation algorithm [16] was implemented to optimize successfully the aforementioned responses. D