Fan 590 Case Study 1

  • 1.

    Rao SB (1997) Metal cutting machine tool design—a review. J Manuf Sci Eng 119:713–716CrossRefGoogle Scholar

  • 2.

    Hale LC (1999) Principles and techniques for designing precision machines. PhD thesis. Massachusetts Institute of TechnologyGoogle Scholar

  • 3.

    Schellekens P, Rosielle N, Vermeulen H, Vermeulen M, Wetzels S, Pril W (1998) Design for precision: current status and trends. CIRP Ann Manuf Technol 47(2):557–586CrossRefGoogle Scholar

  • 4.

    Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools—a review: part I: geometric, cutting-force induced and fixture-dependent errors. Int J Mach Tools Manuf 40(9):1235–1256CrossRefGoogle Scholar

  • 5.

    Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools—a review: part II: thermal errors. Int J Mach Tools Manuf 40(9):1257–1284CrossRefGoogle Scholar

  • 6.

    Ni J (1997) A perspective review of CNC machine accuracy enhancement through real-time error compensation. Chin Mechanic Eng 8(1):29–33Google Scholar

  • 7.

    Chen JS (1995) Computer-aided accuracy enhancement for multi-axis CNC machine tool. Int J Mach Tools Manuf 35(4):593–605CrossRefGoogle Scholar

  • 8.

    Wang SM, Liu YL, Kang Y (2002) An efficient error compensation system for CNC multi-axis machines. Int J Mach Tools Manuf 42(11):1235–1245CrossRefGoogle Scholar

  • 9.

    Tsutsumi M, Saito A (2003) Identification and compensation of systematic deviations particular to 5-axis machining centers. Int J Mach Tools Manuf 43(8):771–780CrossRefGoogle Scholar

  • 10.

    Tsutsumi M, Saito A (2004) Identification of angular and positional deviations inherent to 5-axis machining centers with a tilting-rotary table by simultaneous four-axis control movements. Int J Mach Tools Manuf 44(12–13):1333–1342CrossRefGoogle Scholar

  • 11.

    Raksiri C, Parnichkun M (2004) Geometric and force errors compensation in a 3-axis CNC milling machine. Int J Mach Tools Manuf 44(12–13):1283–1291CrossRefGoogle Scholar

  • 12.

    Zhu WD, Wang ZG, Yamazaki K (2010) Machine tool component error extraction and error compensation by incorporating statistical analysis. Int J Mach Tools Manuf 50(9):798–806CrossRefGoogle Scholar

  • 13.

    Jung JH, Choi JP, Lee SJ (2006) Machining accuracy enhancement by compensating for volumetric errors of a machine tool and on-machine measurement. J Mater Process Technol 174(1–3):56–66CrossRefGoogle Scholar

  • 14.

    Zhang Q, Zhao HL, Tang H, Sheng BH (1999) Application of error compensation technique for NC machine tools: thermal error compensation. Manufact Technol Mach Tool 3:26–28Google Scholar

  • 15.

    Liang RJ, Ye WH, Zhang HY, Yang QF (2012) The thermal error optimization models for CNC machine tools. Int J Adv Manuf Technol 63(9–12):1167–1176Google Scholar

  • 16.

    Wu CW, Tang CH, Chang CF, Shiao YS (2012) Thermal error compensation method for machine center. Int J Adv Manuf Technol 59(5–8):681–689CrossRefGoogle Scholar

  • 17.

    Guo QJ, Yang JG, Wu H (2010) Application of ACO-BPN to thermal error modeling of NC machine tool. Int J Adv Manuf Technol 50(5–8):667–675CrossRefGoogle Scholar

  • 18.

    Han J, Wang LP, Wang HT, Cheng NB (2012) A new thermal error modeling method for CNC machine tools. Int J Adv Manuf Technol 62(1–4):205–212CrossRefGoogle Scholar

  • 19.

    Tan B, Mao XY, Liu HQ, Li B, He SP, Peng FY, Yin L (2014) A thermal error model for large machine tools that considers environmental thermal hysteresis effects. Int J Mach Tools Manuf 82–83:11–20CrossRefGoogle Scholar

  • 20.

    Li Y, Zhao WH, Wu WW, Lu BH, Chen YB (2014) Thermal error modeling of the spindle based on multiple variables for the precision machine tool. Int J Adv Manuf Technol 72(9–12):1415–1427CrossRefGoogle Scholar

  • 21.

    Huang YQ, Zhang J, Li X, Tian LJ (2014) Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle. Int J Adv Manuf Technol 71(9–12):1669–1675CrossRefGoogle Scholar

  • 22.

    Zhang Q, Wang GF, Liu YW, Zhao HL, Sheng BH (1999) Application of error compensation technique for NC machine tools: geometric error compensation. Manufact Technol Mach Tool 1:30–34Google Scholar

  • 23.

    Zhang Q, Zhang HG, Liu YW, Zhao HL, Sheng BH, Zhang TC (1999) Application of error compensation technique for NC machine tools: load error compensation. Manufact Technol Mach Tool 2:8–9Google Scholar

  • 24.

    Liu YW, Zhang Q, Zhao XS, Zhang ZF, Zhang YD (2002) Multi-body system based technique for compensating thermal errors in machining centers. Chin J Mech Eng 38(1):127–130CrossRefGoogle Scholar

  • 25.

    Fan JW, Guan JL, Wang WC, Luo Q, Zhang XL, Wang LY (2002) A universal modeling method for enhancement the volumetric accuracy of CNC machine tools. J Mater Process Technol 129(1–3):624–628CrossRefGoogle Scholar

  • 26.

    Smith S, Woody BA, Miller JA (2005) Improving the accuracy of large scale monolithic parts using fiducials. CIRP Ann Manuf Technol 54(1):483–486CrossRefGoogle Scholar

  • 27.

    Alberto CR, Leopoldo RH, Tatiana B, Ernst K (2007) Geometrical error analysis of a CNC micro-machine tool. Mechatronics 17(4–5):231–243Google Scholar

  • 28.

    Woody BA, Smith KS, Hocken RJ, Miller JA (2007) A technique for enhancing machine tool accuracy by transferring the metrology reference from the machine tool to the workpiece. J Manuf Sci Eng 129(3):636–643CrossRefGoogle Scholar

  • 29.

    Brecher C, Utsch P, Wenzel C (2009) Five-axes accuracy enhancement by compact and integral design. CIRP Ann Manuf Technol 58(1):355–358CrossRefGoogle Scholar

  • 30.

    Wang ZG, Cheng X, Nakamoto K, Kobayashi S, Yamazaki K (2010) Design and development of a precision machine tool using counter motion mechanisms. Int J Machine Tools Manufac 50(4):357–365CrossRefGoogle Scholar

  • 31.

    Brecher C, Utsch P, Klar R, Wenzel C (2010) Compact design for high precision machine tools. Int J Mach Tools Manuf 50(4):328–334CrossRefGoogle Scholar

  • 32.

    Chase KW, Greenwood WH, Loosli BG, Hauglund LF (1990) Least cost tolerance allocation for mechanical assemblies with automated process selection. Manufac Rev 3(1):49–59Google Scholar

  • 33.

    Chase KW (1999) Tolerance allocation methods for designers. ADCATS Rep 99(6):1–28Google Scholar

  • 34.

    Sivakumar K, Balamurugan C, Ramabalan S (2011) Concurrent multi-objective tolerance allocation of mechanical assemblies considering alternative manufacturing process selection. Int J Adv Manuf Technol 53(5–8):711–732CrossRefGoogle Scholar

  • 35.

    Andolfatto L, Thiébaut F, Lartigue C, Douilly M (2014) Quality-and cost-driven assembly technique selection and geometrical tolerance allocation for mechanical structure assembly. J Manuf Syst 33(1):103–115CrossRefGoogle Scholar

  • 36.

    Khodaygan S, Movahhedy MR (2014) Robust tolerance design of mechanical assemblies using a multi-objective optimization formulation. SAE Technical Paper No.2014-01-0378Google Scholar

  • 37.

    Montgomery DC (2009) Introduction to statistical quality control, 6th edn. Wiley, New York, pp 351–364Google Scholar

  • 38.

    International Standard ISO 230-2: 2006(E), Test code for machine tools—part 2: determination of accuracy and repeatability of positioning numerically controlled axesGoogle Scholar

  • 39.

    Ma H, Wang J (2009) Precision theory of instrument. Press of Beijing University of Aeronautics and Astronautics, Beijing, pp 376–379Google Scholar

  • 40.

    Jin T (2005) Precision theory and application. Press of University of Science and Technology of China, Hefei, pp 212–214Google Scholar

  • 41.

    Litivin FL, Fuentes A (2004) Gears geometry and applied theory, 2nd edn. Cambridge University Press, Cambridge, pp 633–644CrossRefGoogle Scholar

  • 42.

    Gonzalez-Perez I, Fuentes A, Hayasaka K (2006) Analytical determination of basic machine-tool settings for generation of spiral bevel gears from blank data. J Mech Des 132(10):101002-1–11Google Scholar

  • 43.

    Zeng T (1989) Design and machining of spiral bevel gear. Harbin Institute of Technology Press, Harbin, pp 90–91Google Scholar

  • 44.

    Fan Q (2006) Computerized modeling and simulation of spiral bevel and hypoid gears manufactured by gleason face hobbing process. J Mech Des 128(6):1315–1327CrossRefGoogle Scholar

  • 45.

    Fan Q (2007) Enhanced algorithms of contact simulation for hypoid gear drives produced by face-milling and face-hobbing processes. J Mech Des 129(1):31–37CrossRefGoogle Scholar

  • 46.

    China National Standard GB 11365-89, Accuracy of bevel and hypoid gearsGoogle Scholar

  • Every year it seems we get one team that comes out of seemingly nowhere to exceed expectations and serve as a battleground for heated debates about sustainability and legitimacy. This season, that role has been filled by the Columbus Blue Jackets who are suddenly first in the NHL in wins, points percentage and goal differential on the back of their 13-game winning streak.

    This, just a few months after being a popular pick as a cellar-dweller in pre-season projections.

    As is the case with any team that goes on a run as good as the Blue Jackets’ streak, the formula is likely some combination of legitimate true talent and good fortune from the hockey gods. The trick is finding out what the split is, or more specifically, whether there’s an over-reliance on the latter.

    Put another way: if the luck dries up, is there enough to fall back on? This is the crux of why we prioritize things like shot metrics and territorial play in our evaluations, because it’s a far more reliable predictor of future success than simply whether the puck happened to go into the net or not.

    There’s no question that Columbus has been a beneficiary of some good fortune early on. Only the Minnesota Wild are currently sporting a higher combined shooting and save percentage than their 103.24 figure. They’re in a virtual dead heat with the Chicago Blackhawks as the two teams most heavily outperforming their expected goal rates based on their shot metrics.

    It’s easy to let one-sided beatdowns of good teams (such as their 10-0 win over Montreal) skew your perception of the team and what they’re capable of at their best, but if their skaters stop scoring on 11.3% of the shots they take or their goalies stop saving 93.2% of the shots they face (which history says they likely will), it’ll be tougher sledding moving forward.

    All of that said, what they’ve accomplished to date has been awfully impressive and at the very least has forced us to recalibrate those pre-season expectations based on this new information. Even if the Blue Jackets are not actually the best team in the league like the current standings would suggest, that doesn’t mean we should automatically expect them to come crashing all the way back down. It’s unfortunately not the sexiest conclusion, but the reality is there’s a wide range of likely outcomes for Columbus that land somewhere between the two extremes.

    Let’s get to the stuff they’re legitimately doing well. It’s fair to suggest Columbus has been creating some of its own luck lately, with the five-on-five shot profile on an upward trajectory as the season has gone along. They’re now all the way up to tenth in unblocked shot attempts, seventh in shots on goal and fifth in scoring chances. If that keeps up, it bodes well for their chances of staying afloat once the shooting percentage dips like we’d expect.

    All of that pales in comparison to their power play, though, which has unquestionably been the team’s bread and butter this season. Not only is their 27.1 per cent conversion rate pacing the league this season, it’s actually the best rate we’ve seen in the past two decades — the 2012-13 Capitals, 2008-09 Red Wings, 2014-15 Capitals and 2009-10 Capitals are the only teams that went over 25 per cent in that span. The unconscionably high 20+ percent clip they’re shooting at as a team here as well surely won’t last all season either, but they’ve afforded themselves enough wiggle room to regress and still remain supremely productive.

    It’s next to impossible to watch their top power play unit work and not immediately become infatuated with it, because the way it’s been assembled is a hockey nerd’s dream. All of the puzzle pieces fit together perfectly, with each individual skill you’d ideally like to see being covered and displayed somewhere on the ice.

    Zach Werenski has been a revelation in his first spin around the league, showing patience and instincts while tightroping the blueline well beyond his years. He possesses all of the tools you’d like to see from a player manning the point on the power play: poise so as not to panic under pressure, vision to swing the puck wherever it needs to go, and a quick shot that finds its way through traffic more often than not.

    Alex Wennberg, who leads the league in power play assists, is rapidly blossoming into a premier playmaker. Much like we’ve seen with guys such as Nicklas Backstrom or Claude Giroux, Wennberg has a knack for picking apart the opposing penalty killing unit with his pinpoint passing from the half-wall.

    Nick Foligno has established himself as the net-front presence, causing havoc around the goal line, and Cam Atkinson has been patrolling ‘the Ovechkin spot’ on the left circle (from which he’s fifth in the league in power play goals, behind only Sidney Crosby, Wayne Simmonds, Shea Weber, and Leon Draisaitl).

    Then there’s Sam Gagner, who I’ve intentionally saved for last. He’s been making a living as the centerpiece of that unit, setting up shop somewhere between the two circles whenever the Blue Jackets are up a man:

    Gagner has been a tremendous find, rewarding Columbus for scooping him off the free agent scrap heap this summer. All it took was a one-year deal for slightly over the league minimum, essentially hockey’s equivalent of a lottery ticket. Gagner is currently tied for 15th in goals and 37th in points, easily providing the best bang for your buck of any player in the league on a non-entry level deal.

    For our purposes though, the most interesting wrinkle here is what the Blue Jackets have done to optimize his presence in the lineup and make all of it run smoothly:

    PLAYERTOTAL TIME ON ICEPOWER PLAY TIME ON ICE% TOI SHARE ON PP
    Brayden Schenn585.61125.2321.38
    Thomas Vanek341.4870.420.62
    Alex Ovechkin596.92122.7820.57
    Brandon Pirri426.7586.8420.35
    Matt Moulson438.9387.820.00
    Jason Spezza460.2291.119.79
    Phil Kessel627.61124.0519.77
    Evgeni Malkin642.66124.419.36
    Wayne Simmonds691.04133.5219.32
    Sidney Crosby556.88107.3419.28
    Claude Giroux703.97134.6619.13
    Sam Gagner419.2780.0619.10
    Johnny Gaudreau490.8490.8618.51
    Jakub Voracek712.27131.5718.47

    It’s informative to see Gagner (and someone like Brandon Pirri) on here sprinkled in between the much bigger offensive stars you’d expect to see getting a heavy dose of prime scoring opportunities.

    Credit the Blue Jackets for realizing that while Gagner is likely never going to become the type of all-around player people hoped he would be when he was a top prospect, he can still provide on-ice value. They’ve done what every team should be striving to do with their players: find a happy marriage between the skills the player does possess and the requirements of the role they’re asking him to play.

    Columbus has kept Gagner down as the 11th-most used forward on the team at even strength, limiting his defensive exposure by having him play against weaker competition for shorter stretches of time. However, when the team goes on the power play, he’s been called upon far more frequently, seeing his usage spike all of the way up to third.

    One way or another, the puck has been finding its way to Gagner between the circles, and he’s made the most of his opportunities. He’s tied for 10th in power play goals with Jamie Benn and Alex Ovechkin, and 21st in power play points with Joe Pavelski and Connor McDavid.

    It seems like a simple enough concept, but it’s so important that it bears repeating. Smart, well-run organizations are taking advantage of every possible edge they can to improve their team and get a leg up on the competition. That can manifest itself in a number of different ways and various magnitudes.

    For the Blue Jackets, that light bulb moment came when they realized they were essentially throwing away shifts whenever they were using the likes of Jared Boll and Gregory Campbell as part of their fourth line. Without much actual on-ice utility, their main selling point was that they looked the part of what we’ve been told those types of players should be (grinders or face-punchers).

    Which is why it’s somewhat ironic that a modern day fourth liner like Gagner is far more likely to actually swing the outcome or change the momentum of a game with a goal than a player like Boll ever would’ve been able to with a big hit or fight.

    Columbus is an easy target as a team because of how much losing they’ve done over the years and how many head-scratching things John Tortorella has said to stir the pot — but actions speak louder than words. Whether they know it or will publicly admit it, the Columbus Blue Jackets are now doing their part to contribute to the widespread changing of the guard when it comes to roster construction, player deployment, and general hockey convention.

    They’re doing it by optimizing their lineup and putting their players in a position to succeed. Gagner is living proof of that.

    0 thoughts on “Fan 590 Case Study 1”

      -->

    Leave a Comment

    Your email address will not be published. Required fields are marked *