Quantification of the characteristics that influence the monitoring of crimping operations of electric terminals for use in the automotive industry

Transcription

1 Quantification of the characteristics that influence the monitoring of crimping operations of electric terminals for use in the automotive industry Vasco A. van Zeller Instituto Superior Técnico, Lisboa, Portugal April 2014 Abstract What factors influence the correct or incorrect monitoring of crimped terminals? The present work, based on a series of tests performed by DELPHI, proposes a method of defining good and bad monitoring and looks into the factors that define the crimp, taking them as inputs for a formula that is designed to relate the characteristics of the crimp with the expected accuracy for the monitoring system in place. Keywords: Crimping, monitoring, CFA, CFM, missing strands 1 Introduction In the auto industry, the electronic component is increasingly relevant [1]. However, during the fabrication of crimped terminals, several errors can occur. One such common error is when, during the stripping of the wire, one of the several strands that compose the wire is accidentally cut and removed along with the insulation. This results in less material being crimped that what is expected and therefore a general loss of quality and durability of the finished product [2]. One increasingly common method of controlling (if not avoiding) these production errors is to install monitoring systems better known as Crimp Force Monitoring (CFM) or Crimp Force Analysis (CFA) [3]. While there are several implementations of this concept, the basic idea is to perform a crimp that is considered error free and representative of the batch in question, generate a plot of crimp force vs displacement 1 for this crimp and compare the plot of each subsequent crimp with the one obtained in the reference crimp [4]. Deviations in the curve are calculated and 1 Can be angular or linear displacement, as a result of the crank mechanism in used the crimping machine

2 measured against a previously determined threshold. The monitoring systems upon which the present work is based takes the forceangle plot of the reference crimp and calculates the area contained between a horizontal line that crosses the 80% peak force mark and the force-displacement plot. Because the performance of the monitoring systems used by DELPHI is not totally known, the company undertook a massive project to exhaustively test a large number of wire-terminal combinations, inducing errors in the crimp in a controlled manner (1 missing strands, then 2, then 3 and so on) to see how the monitoring system would react and whether these defects would be detected. Each study is composed of 100 perfect crimps 15 defective crimps for each defect (15 for 1 missing strand, 15 for 2 missing strands, 15 for 3 missing strands etc. until 100% detection is observed). Figure 1 Crimping curves showing the reference area contained, between the crimp line and the 80% peak force mark At this point a new unit RU is created such that A reference = 1000 RU. This unit will be used as a measuring unit for deviations from the reference crimp. The setup phase is finished and the system is ready to begin crimping the final product. Every successive crimp will be tested against the reference one. Deviations from the reference curved are accounted based on the ad-hoc unit RU: a crimp is rejected if the total deviation in relation to the reference curve is greater than 70 RU. 2 DELPHI monitoring studies Despite being a very powerful tool, CFA systems still display false positives and, more worrying, still fail to detect missing strands in the crimp, especially when the number of strands potentially missing is very small in comparison to the total number of strands. It is the purpose of the present work to determine some of the factors that contribute to these failures in monitoring. For each crimp, not just the pass/fail monitoring results are registered but also the total deviation values (measured in RU units). All this information is noted on a relevant file. Thus, at the end of a given monitoring study, several lists of values have been compiled. From each of those lists, the average μ and standard deviation σ are then computed. Using these values for μ and σ, normal curves can be extrapolated. f 0,04 0,04 0,03 0,03 0,02 0,02 0,01 0,01 0, RU Figure 2 Normal curves obtained from a monitoring study Looking at the placement and shape of the normal distribution curves, one can tell what kind of monitoring is being performed

3 in the test in question: detection failures will present strongly overlapping curves, whereas clean detections will present curves that appear distant and with no major overlap. 3 Quantifying monitoring While the plotting of normal curves for perfect and defective crimps are a powerful way of visualizing the performance of the monitoring system, this is still a qualitative approach (more overlap = bad, less overlap = good). Therefore, the quantification of overlap is proposed: X %G= P 1 ( x, σ 1, μ 1 )dx+ X P 2 (x,σ 2, μ 2 )dx Where X is the point where the two curves intersect. The proposed parameter %G represents the expected number of tests (out of 100), whose RU value can be observed both in perfect and defective crimps. It is therefore a quantification of the grey area where the monitoring system struggles. We can thus say that this value is inversely proportional to the probability of monitoring failures 2 and is contained between 0 and Selection and elimination of monitoring variables In order to judge the effect of the crimp variables on the monitoring, it was first necessary to thoroughly characterize the crimp process, listing variables pertaining to the wire, to the terminal, to the crimp parameters, as well relations between each other where these where deemed interesting. One example of such relations is dividing the thickness of the terminal wings with the inner radius of the crimp matrix, to try and judge the proportionality between the two. 4.1 Grouping of studies according to dimension For any given variable, values that are high for some gauges are low for other gauges 3. Furthermore, smaller gauge wires have a lower number of strands which means that even one strand removed during the monitoring tests can represent as much as 14% of material removed (1 strand in 7), whereas in the larger gauge wires, the percentage of removed material can be as little as 1.25% (1 strand in 80). Because of these two effects, it was decided that the best approach was to separate the monitoring studies according to their nominal cross section area. The following picture represents the division of the tests: others; 71 1,50; 24 2,00; 24 4,00; 30 0,75; 52 0,50; 117 0,35; 69 Figure 3 Number of tests per cross section area Once the above classes were determined, the process of selection and elimination of variables began. 0,50 0,35 0,75 4,00 2,00 1,50 2,50 1,00 3,00 5,00 2 Note that this detection failure can be good called bad or bad called good, depending on the system parameters. 3 Sometimes in unpredictable ways: it was observed that the average headroom of small terminals was as little as half of that of large terminals.

4 4.2 Elimination and classification of variables All the variables that could be reasonably measured and that were thought to be relevant are on the following table: Table 1 Crimping variables studied Variable Meaning #s Number of strands XSA / XSP PF #MS CWW R Øs CCH HR t CCW nominal area / nominal perimeter Peak Force Number of missing strands Core Wing Width Crimp Matrix Radius Diameter of strand Core crimp heigh Headroom terminal thickness Core crimp width Some of the variables above were also combined to form new ones: Table 2 Combined variables Variable Ratio %MS HR*PF R/t CWW / XSP Significado core crimp width / height Percentage of missing strands, difficulty of the test Proportion of force used to crimp the wire Flow of the terminal along the crimp matrix Terminal fitting Some of these variables were eliminated because they were not relevant, displayed no variation when grouped by area or simply because they would be more relevant when combined with another variable. Thus a final short list of 8 variables was tested in more depth. Two main approaches were used in order to classify the influence and determine if this influence was positive or negative in regards to the monitoring performance of the system: Best / Middle / Worst tables: for each class, the tests were subdivided in the top 15%, the bottom 15% and the rest. Within these subgroups, the average value of each variable was determined. When the average value of the variable in question is larger in the best subgroup than in the worst group, this suggests a positive influence variable. The opposite is true, mutatis mutandis for variables that display lower values for the best group. Contour plots: The test that is performed and its results are strongly dependent on the number of missing strands. As a result of this, the percentage of moving strands was treated as a separate, independent variable and looked at as the difficulty of the test and plotted simultaneously. For this, the software Minitab [5] was used to elaborate contour plots consisting of two variables at once: the missing strands on the yy axis and the relevant variable in study in the xx axis. At the end of both these analyses, six variables were determined to have an influence on the monitoring of crimped terminals, with two being of positive influence (i.e. the higher this variable, the better the expected outcome for the monitoring) and four being of negative influence.

5 Table 3 Influence of final variables Variable Observed influence t negative R/t positive HR positive PF*HR negative CCH negative CWW/XSP negative Ratio unkown / null where the first is for negative influence variables and the second is for positive influence variables. These transformations return values that are between 1 and 2 and that are dimensionless. These transformations were used for each variable (i.e. each series of values) within each area group. In other words, for positive influence variables, the highest value in of all the tests in the group took the value 2, and the lowest value took the value 1 and the opposite for negative influence variables. The present work proposes the following formula: An additional variable was analyzed (the crimp ratio) but revealed no particular influence either way. Where: = % 5 Proposed monitoring projection formula 5.1 Normalization of input parameters The variables selected in the previous point have not only very different orders of magnitude but also different units. Therefore, the first step in the creation of a formula that takes all these variables as an argument is to make them compatible with each other. In order to achieve this, the following transformations were designed: = = + 1, [1 2] + 1, [1 2] =,,,,, And is a vector containing the weights for each variable. As we can see in the formula above, the projected value depends simultaneously of the combined characteristics of the crimped terminal and the percentage of material removed from it during testing. The formula that was designed is based on the idea that no matter how bad the characteristics of the terminal, wire and/or crimp, with a sufficiently large number of missing strands, detection is effectively guaranteed. Conversely, even in optimum conditions it is not realistic to expect the system to detect the absence of 1 strand in 80. As a consequence of this, any formula for monitoring performance has to take into account the percentage of missing strands as a primary parameter and all of the other characteristics combined and weighted as a multiplying factor that will make the detection harder or easier. The best set of

6 weights for the variables was determined at a later time. 5.2 Determining weights The determination of the best weights to use with the formula was done empirically. For each variable there are 3 possible weights: 1, 2 and 3, representing low, medium and high influence. Since there are 6 variables and 3 possible weights for each variable, the total number of combinations is 3 6 =729. Assuming that the best combination of weights is the one that will best reflect the reality of the existing studies, a simple (but computationally demanding) solution is to test all of them using a computer and see which combination best makes the formula reflect the known results. To judge how well the formula was performing when compared to the known results, two parameters were devised: Bad Called Bad (BCB): the number of tests with very bad monitoring placed in the first (worst) spots, as sorted by the formula; Good Called Good (GCG): the number of tests with excellent monitoring placed in the last (best) spots, as sorted by the formula; With these two parameters and the list of possible combination, a computer analysis was made to test all combinations of weights and, for each of these, take note of the GCG and BCB values. In the end, the machine sorted these values by reverse order of BCB and GCG (i.e. best weight combination first). With this sorting, the first result in the list is the most appropriate one. 5.3 Validation of formula using existing results In practice, the validation of the formula was done in the previous step: the top values of GCG and BCB indicate what percentage of the studies the formula is expected to predict. The following table describes the observed results. Table 4 Formula performance Area (mm 2 ) GCG (%) BCB (%) 0,35 98,3 50,0 0,50 88,2 35,7 0,75 88,6 90,9 1,50 93,3 66,7 2,00 80,0 57,1 4,00 92,9 83,3 The table above shows that the application of the formula can be expected to predict between 50% and 83% of the cases of monitoring failure and also between 80% and 98% of the cases of clear monitoring success. These values can be used to calibrate expectations regarding the monitoring systems in place. 6 Application of the formula to untested wire-terminal combinations Even for untested combinations, the proposed formula can be used to predict the capability of the monitoring system to detect missing strands. For example, the chart below represents all the known results for the 0,50mm 2 class, where each point is an individual test. The three vertical lines represent the expected monitoring performance for a lack of 1, 2

7 and 3 strands respectively, but for a wireterminal combination composed of 16 strands and that had not been tested before. 1-%G 1 0,5 0-0,5 1,5 3,5 5,5 7,5 Figure 4 Chart for the 0,50mm2 section, showing projected monitoring lines for 1, 2 and 3 missing strands on a 16 strand wire Each vertical line was obtained introducing all the relevant variables in the formula, along with the corresponding weights. The PV value is now a function only of the number of missing strands, which can be introduced one by one. By plotting these PV values in the same chart as those obtained in the previous monitoring studies (i.e. the ones already made by DELPHI) as was done in the chart above, a rough projection of monitoring can be obtained. In the present example, it s hard to tell if 1 missing strand will be detected. But 2 and 3 missing strands fall within an area of the chart that we can confidently state that the system will detect this defect. So long as all the characteristics of the crimping operation are known as they usually are this process can be done for any new combination avoiding potentially tens of man-hours of qualified work. 7 Conclusions PV Crimp monitors don t always detect crimping defects. While the quantity of missing material relative to the total material in the wire is very important, this factor alone is not sufficient to explain monitoring failures. After an analysis of the operation, 6 factors were identified as having an influence for which 6 corresponding weights were determined. A formula was created using these 6 factors and weights as well as the percentage of missing material. The formula was validated using the large body of studies performed by DELPHI. This formula can also be used to predict the monitoring outcome of future combinations, so long as basic characteristics of the terminal, wire and crimp are known. 8 Future work Should new studies be performed, the results should be introduced in the master file and the formula should be tested against new results. If a sufficiently number of studies is performed and added, new weights should be calculated using the method described in the present work. With more time and more computational power, the number of weights could be increased from 3 to 5. While this would dramatically increase the number of combinations, with newer computers and newer parallel processors with as much as 8 cores, this it s not an impossible task. 9 Bibliography [1] Effects of Design Variables on Compression Rate of Wire in Connector Crimping Process of Wiring Harness Using FEM. Gu, S. M., Choi, H. S. and Kim, Y. S. 2010, Transactions of Materials Processing. [2] Doyon, Pete. Crimping 101. Connector

8 Specifier. November [3] Boyd, Rob. The Misunderstood Crimp Force Monitor. Connector Specifier. January [4]. The Reality of Crimp Force Monitoring. CONNECTOR SPECIFIER. January [5] Minitab, Inc. Meet Minitab 15. s.l. : Minitab, Inc., 2007.