Machining of High Strength Steels With Emphasis on Surface Integrity by air force machinability data center_2 docx

Figure 146. A turning and boring surface texture test piece.  Chapter  Figure 147. Machinability testing utilising an ‘accelerated testing procedure’ – a combination of the rapid facing and degraded tool tests . Machinability and Surface Integrity  on a moderately short timescale. Normally in many previous testing programs, an uncoated cemented car- bide P20, or P10 grade would have been used, since these grades withstand both higher speeds and have better tool wear resistance to that of previously utilised cutting tool materials. However in this case, an P25 grade was chosen, which is a degradation from the optimum P20 grade, but it should still perform satis- factorily. Furthermore, the cutting speed was raised by >2.5 times the optimum of 200 m min –1 , with all fac- ing operations being conducted at a ‘constant surface speed’ 12 of 550 m min –1 . Typical tool-life curves produce by the AWT tech- nique are illustrated in Fig. 148, showing the expected three stages of ank wear. is ank wear being a func- tion of: the initial edge breakdown, steady-state wear – as the insert’s ank progressively degenerates and - nally, catastrophic insert edge breakdown – as the edge completely fails. Detailed metallurgical analysis can be made as to the reasons why some P/M compacts per- formed better than others, by reference to the litera- ture on the metallurgical interactions between the tool and the compact – this subject being outside the scope of the present discussion. e facing-o secondary machining operation meant that aer 10 facing passes, a pre-programmed ‘optional stop’ can then be applied, to allow both tool ank wear and compact surface tex- ture to be established. e faced-o surface texture re- sults can then be superimposed onto the same graph – for a direct comparison of ank wear and for that of the machined surface texture parameter. Without go- ing into too much detail of the specic aspects of the processing and metallurgical interactions present here on the composite graph, some compacts abraded the cutting insert more than others, while the ‘faced’ sur- face texture, generally seemed to get worse, then im- prove and nally worsen again. However, this is a complex problem which goes to the ‘heart’ of the vi- 12 ‘Constant surface speed’ , this can be achieved by employing the appropriate ‘canned-cycle’ G-code accessed from the CNC controller, which allows the testpiece’s rotational speed to in- crease as the faced diameter decreases*. * Normally there is a restriction on the rotational speed limit – created by the maximum available speed for this machine tool, which would normally be reached well before the cutting insert has coincided with that of components centre line, but because in this instance, the compacted testpiece is hollow, the rotational restriction does not present a problem. sual aspect of machined surfaces – wherein the real situation is that surface texture continuously degen- erates, and it is only the burnishing (i.e.‘ironing’) of the surface that ‘masks’ the temporary improvement in machined surface – more on this topic will be made in the surface integrity section. What is apparent from using the AWT technique is that on a very short tim- escale, considerable data can be generated and applied research assessments can be conducted both speedily and eciently. is topic of exploiting the minimum machining time and data-gathering activities to gain the maximum information, will be the strategic mes- sage for the following dialogue. Machinability Strategies: Minimising Machining Time, Maximising Data-Gathering Prior to commencing any form of machinability tri- als, parameters for cutting data need to be ascertained in order to minimise any likelihood of repetition of results, while reducing the amount of testpieces to be machined to the minimum. Data obtained from such trials must be valid and to ensure that the cutting pa- rameters selected are both realistic and signicant a disciplined experimental strategy based upon the ‘De- sign of Experiments’ (DoE) approach is necessary – see Fig. 149. Here, a ow-chart highlights the step-by- step approach for a well-proven industrial technique, to maximise the labour-intensive and costly exercise of obtaining a satisfactory conclusion to an unbiased and ranked series of machinability results. ere are a range of techniques that can be utilised to assess whether the cutting data inputs, namely: feeds, speeds, D OC ’s, etc., will result in the correct inputs to obtain an extended tool life, or an improvement in the ma- chined surface texture from the testing program. One such method is termed the ‘Latin square’ – which as- sesses the signicance of the test data and its interac-  Chapter  Figure 148. Graphical results obtained from the accelerated machinability test, illustrating how ank wear and surface texture degrades, with the number of facing-o passes . Machinability and Surface Integrity  tions. For a practical machinability trial employing a ‘Latin square’ , it uses a two-way ANOVA 13 table, with a limited amount of ‘degrees of freedom’ , typically: fee - drate, cutting speed, D OC , plus surface nish – these parameters can be changed/modied to suit the ‘pro- gramme of machining’ in hand. By using a very lim- ited group of cutting trials, a two-way ANOVA table can be constructed and their respective ‘F-ratio’ for each interaction can be determined. is calculated ‘F- ratio’ should be greater than the 5% ‘condence limit’ of the statistical distribution to be signicant. If the F- ratio falls below –5% (i.e. for the calculated F-ratio), then the interactions are not signicant, which ne- cessitates increasing the ‘factor strength’ (e.g. increas- ing the: cutting speed, feedrate, etc.), to generate data which is >5% condence limit – as shown by the ‘feed- back loop’ in Fig. 149, or alternatively, using a dierent factor. By such means, ANOVA tests for signicance of machining data, ensures that the processing parameters utilised for the prospective machinability trial are both valid and the correct ones to use in the proposed ma- chining programme. 13 ‘Analysis of variance’ (ANOVA), or as it should be more ap- propriately termed the ‘analysis of variation about the means’ , consists of portioning the total variation present in a data set into ‘components’. Each ‘component’ is attributed to an iden- tiable cause, or source of variation; in addition, one ‘com- ponent’ represents the variation due to uncontrolled factors and random errors associated with the response measure- ments.Specically, if the data set consists of ‘n’ measurements ‘y 1 .…,y n ’ and their mean is denoted by: ‘y ’ , the total varia- tion about the mean is embodied in the ‘sum of squared de- viations’ , as following diagram depicts, for the ‘partitioning scheme’ for ANOVA: Total Sum of Squares about the mean: n � i= (y  − ¯ y)  ↓ ↓ ↓ ↓ ↓ Sum of squares – due to Source1 Sum of squares – due to Source2 Sum of squares – due to Source3 Sum of squares – due to Source4 Error, or residual Sum of Squares e technique of analysis of variance decomposes this total ‘sum of squares’ into the parts shown above, for a case in which four identiable sources of variation are present – in addition to the ‘error component’. e number of identiable causes of variation and the formulae for the ‘component sums of squares’ are intrinsically connected to the specic experi- mental design utilised, in the data collection and to the statis- tical model deemed appropriate for this analysis. Rather than spending considerable time, eort and indeed exorbitant expense, on a large and com- plex machining testing programme, which more oen than not, produces numerous machined components that are almost indistinguishable from each other. It might be more prudent, to conduct a ‘condensed’ series of trials, based upon a rigorous statistically-designed methodology. erefore, experiments based on the so- called ‘orthogonal arrays’ can be benecially engaged in this regard. Many applied researchers and engineers have utilised a range of factorial-designed experi- ments, typied by the ‘Taguchi-approach’. e main problem with these ‘arrays’ is that in many situations the large number of ‘interactions’ (i.e. fac- tors) have been shown to interfere with the overall re- sults – introducing ‘secondary eects’ , which will not have been anticipated for, when the original strategic programme was devised 14 . Such spurious data, could seriously aect future machining recommendations and inuence the outcome in a negative manner. e ‘interaction problem’ can have these aects consider- ably reduced by incorporating a more ‘truncated-ap- proach’ to the experimental design strategy for the machinability trials, rather than using a ‘full’ Taguchi orthogonal array (Fig. 150). For example, if all of the experiments are conducted in for example one of ‘stan- dard’ the Taguchi L 8 (2 7 ) orthogonal array, depicted in Fig. 150, then the ‘total outcomes’ (i.e. components machined), would be: 2 7 = 128 × 8 = 1,024 individual components machined. Here, in the Taguchi orthogo- nal array seven factors have been employed and with the vast amount of components produced from such a long-running and very costly machining programme, many of the pertinent details will be lost on those en- gineers/researchers attempting to de-code the vast as- sortment of machinability data collated. However, it is possible to utilise a much simpler-approach to the overall massive data-collection and analysis problem, yet still providing statistical signicance, this can be achieved by adopting a ‘Fractional factorial-designed experiment’. Here, instead of the virtually ‘mindless task’ of producing 1,024 almost identical components, 14 ‘Orthogonal array factors’ – when utilising a ‘full’ Taguchi- designed orthogonal array for a complete picture of all of the interactions, then it has been shown (Shainin, 1985 – see refer- ences), that if many factors are employed (i.e. normally >5), this results in unwanted ‘secondary eects’ which cannot be accounted for, leading to spurious results from any machin- ability trials.  Chapter  by using a ‘Fractional factorial-designed experiment’ with an identical matrix to that given in Fig. 150, only 8 components are produced! is testing regime is both signicantly quicker and much less costly to perform, obtaining a ‘snap-shot’ of the overall ma- chinability problem, but because considerably less tes- tpieces are produced, the ‘interaction-problem’ and its ‘secondary eects’ are not an issue, even when seven factors are utilised. Obviously, this machinability data has to be collated and investigated in a disciplined and controlled fashion. One tried-and-tested method of establishing an unbiased and ranked interpretation of these results, is to use the much misunderstood and maligned technique of ‘Value Analysis’ 15 (VA). is VA when used to show trends in competitive functions 15 ‘Value Engineering and Analysis’ (VE/VA), with VE being principally concerned with an overall improvement of design- based details on engineering components, while a more lim- ited form of this technique is termed VA – being particularly relevant for detailed interpretation of recorded data from ex- perimentation. Here, in this case, from the wide-ranging and oen seemingly unrelated output of machinability trials. Figure 149. Flow chart indicating the desigh philosophy for unbiased and ranked machinability trials. Machinability and Surface Integrity  Figure 150. A fractional factorial-designed experiment, based upon a Taguchi L 8 (2 7 ) – orthogonal array.  Chapter  and operations, can be successfully utilised from the comparisons of cutting uids, through to complex and dicult-to-machine aerospace machinability trials. If a more sophisticated technique is required, then it is also possible to utilise ‘Quality Function Deployment’ 16 (QFD), to obtain a complete picture of the outcomes from machining trials. QFD is oen used by indus- try as a means for its ‘Continuous-improvement pro- grammes’ 17 . Here for ‘simplicity’s-sake’ , the more basic and somewhat less complex VA tabulated data-colla- tion approach, will be briey reviewed. e application of VA to a series of collated and compiled massed-data is not new. In fact, it was widely-used during the 1960’s, but fell into disfavour, partly because its function and operation were oen not well-dened – this being exacerbated by poor im- plementation of its recommendations. However, VA techniques are useful, allowing one to interpret data trends both quickly and objectively – without undue bias – at a glance of a spreadsheet. Not only can signi- cant trends be readily seen, but the spreadsheet shown in Fig. 151 – shows a typical machinability data for P/ M compacts drilled by two diering drill-point geom- etries. By using the spreadsheet, not only can overall trends be readily seen, it also can depict sub-set trends as well, giving a complete picture (i.e. globally) of the important criteria in assessing machining data. As a simple ranking system is used, considerable objectiv- ity can be gained and with little undue inuence – bias, aecting the outcome from these tabulated results. In employing the ranking of the results, it is normal prac- tice to decrement down and if two values are ranked identically, then they are given the same rankings, fol- lowed by the next lower ranking, being two numbers lower, as following example shows: 16 ‘Quality Function Deployment’ (QFD), is a general term that means the: ‘Deployment of quality through deployment of qual- ity functions’ (Akao, 1988). It is oen known as the ‘House of Quality’ , because the tabulated graphical representation looks similar to that of a house – when all the interacting factors for subsequent analysis have been included on the chart. is QFD technique, is a wide-ranging philosophy for the com- plete analysis of both simple and intricate designs and can be successfully exploited for machinability trials. 17 ‘Continuous-improvement programmes’ , can be dened as an: ‘Operational philosophy that makes the best use of resources in order to increase product, or service quality and result in more eective satisfaction of customers’ (Swanson, 1995). For example, in Fig. 151 – for the values shown in column two (i.e. le-hand side: Jobber drill, rust Force 0.254 N): Compact type: 1 2 3 4 5 6 7 8 Ranking: 6 8 5 1 5 7 3 2 NB  Here, two 5’s were ranked, meaning that the next decremented value would rank as 3. Hence, in this case the Low compaction Compact type No. 2 this was best and Low compaction No. 4 worst – as jobber drilled. is ‘truncated approach’ the elementary and easily comprehended VA tabulation (Fig. 151) , enables non- specialists, together with knowlegdible experimenter, to recognize the inuence various machining param- eters have on the potential performance of the trials undertaken. By judicious use, the VA technique in conjunction with a strictly controlled and limited ma- chining strategy – based upon some form of ‘orthogo- nal array’ , in combination with the ‘strength’ (i.e >5% ‘F-ratio’) of parameters by ANOVA, this will enable a researcher to conduct a speedy, compact, realistic, yet meaningful machinability assessment. 7.2 Machined Roundness Roundness is a condition of a ‘surface of revolution’ , which can take the form of a: cylinder, cone, or sphere, where all the peripheral data points (i.e. measure- ments) intersect. In reality, the radius of say, a nomi- nally round workpiece tends to deviate – from the ‘true circle’ – around the periphery of the part, making these variations the theme to subjective interpretation of the measured results. In fact, in the past, the sim- plistic technique for the assessment of roundness was usually measuring three diameters on a workpiece, to determine the diametrical variations, then ‘averaging’ to give its overall dimensional size. Moreover, for vari- ations in a workpiece’s radius about an axis of rotation, this was oen found by positioning the part between a ‘bench-’ , or sine-centres’ – the latter equipment is em - ployed for turned tapered features, then rotating and monitoring it with dial gauges both at and along its length. In the past, this rather supercial metrologi- cal workpiece assessment was supposed to inform the inspector as to its potential in-service performance. If some radial variations occurred, this geometrical Machinability and Surface Integrity  Figure 151. Value analysis – tabulation of the performance of two drilling points and a typical range of drilling data, when machining powder metal- lurgy compacts .  Chapter  . technique in conjunction with a strictly controlled and limited ma- chining strategy – based upon some form of ‘orthogo- nal array’ , in combination with the strength (i.e >5% ‘F-ratio’) of parameters. accelerated machinability test, illustrating how ank wear and surface texture degrades, with the number of facing-o passes . Machinability and Surface Integrity  tions. For a practical machinability. data set into ‘components’. Each ‘component’ is attributed to an iden- tiable cause, or source of variation; in addition, one ‘com- ponent’ represents the variation due to uncontrolled factors