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고장력 주강 재질의 표면거칠기 향상을 위한 가공 최적화에 관한 연구

우성제1,2, 김효창1,2, 전원표3, 류성기1,#orcid

A Study on Optimizing Machining Conditions for Enhanced Surface Roughness in High Tensile Cast Steels

Seong Je Woo1,2, Hyo Chang Kim1,2, Jeon Won Pyo3, Sung Ki Lyu1,#orcid
Journal of the Korean Society for Precision Engineering 2026;43(7):709-716.
Published online: July 1, 2026

1경상국립대학교 대학원 융합기계공학과

2우창기계㈜

3한국폴리텍대학 순천캠퍼스

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#Corresponding Author / Email: sklyu@gnu.ac.kr, TEL: +82-55-772-1632, FAX: +82-55-772-1578
• Received: February 19, 2026   • Revised: March 11, 2026   • Accepted: March 16, 2026

Copyright © The Korean Society for Precision Engineering

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  • This study employed the Taguchi method to determine the optimal milling parameters for cast steel, aiming to minimize surface roughness. The analysis indicated that the feed rate was the most significant factor, with lower feed rates resulting in improved surface finish. In contrast, spindle speed and radial depth of cut had minimal impact, while axial depth of cut and tempering temperature emerged as crucial determinants. A linear regression model accounted for 87.46% of the variance in surface roughness. The predicted optimal conditions closely aligned with experimental results, yielding only a 2.93% error and achieving an average surface roughness of 3.033 μm. These findings provide a predictive framework for achieving the desired surface quality in milling processes.
  • KEYWORDS: Cover hub, CNC milling, Taguchi method, Surface roughness, Cast steel, ANOVA, Design of experiments
  • KEYWORDS: 커버 허브, 머시닝센터, 다구찌 방법, 표면거칠기, 주강, 분산분석, 실험계획법
주강(Cast Steel)은 우수한 강도, 내마모성 및 내구성을 지닌 재질로서 특수차량 및 중장비 부품 제조에 광범위하게 사용된다[1,2]. 그러나 높은 탄소 함량과 복잡한 미세조직으로 인해 가공성이 낮아 공구 마모가 빠르고 표면 품질 확보가 어려운 난삭재로 알려져 있다. 이러한 문제를 해결하기 위해 다구찌 방법(Taguchi Method)[3], 반응표면분석법(RSM) 등 통계적 실험 계획법(Design of Experiments)을 활용한 공정 최적화 연구가 다양한 난삭재에 적용되어 왔다[4-9].
그러나 기존 연구들은 주로 합금강[5,7] 및 스테인리스강[6], 티타늄 합금[10], 인코넬[11] 등의 고가(Costliness)의 합금 소재 가공 특성에 집중되어 있으며, 주강 소재에 대한 체계적인 가공 매개변수 최적화 연구는 상대적으로 제한적이다. 또한, 대부분의 연구는 단일 재료 조건에서 절삭 변수의 영향만을 분석하고 있어, 실제 양산공정에서 발생할 수 있는 열처리 편차에 따른 경도 변화가 가공성에 미치는 영향에 대한 정량적 분석은 부족한 실정이다. 이에 본 연구는 주강 소재를 대상으로, 경도(HB)를 주요 실험 인자로 포함한 가공 매개변수 최적화 실험을 수행함으로써, 열처리 편차가 존재하는 실제 양산 환경에 적용 가능한 절삭 조건을 도출하고자 한다. 최적화를 위해 다구찌 L27 직교배열을 통한 실험계획과 신호 대 잡음비(S/N Ratio) 분석, 분산분석(ANOVA)을 수행하여 각 인자의 기여도를 정량화하였으며, 회귀분석과 확인실험을 실시하여 예측 정확도를 검증하였다. 이러한 연구 결과를 통해 주강 부품의 양산 가공 시 표면 품질 개선과 공구 수명 연장을 위한 참고 기준을 제공하고자 한다.
2.1 시험편 열처리 조건의 영향 분석
커버허브는 차량 운행 중 반복 하중 및 충격에 노출되는 부품으로서, 요구되는 강도, 인성, 내마모성을 확보하기 위해 열처리 조건의 정밀한 관리가 필요하다. 양산 공정에서는 열처리 조건 편차로 인해 경도가 HB200 이하에서 HB220 이상까지 다양하게 나타나며, 이러한 경도 차이가 가공 특성에 미치는 영향을 사전에 검토 할 필요가 있다. 최적화 실험 전, 실제 양산 제품을 3가지 경도 범위(Case 1: HB200 이하, Case 2: HB200-210, Case 3: HB220 이상)로 분류하고, 각각의 미세조직 및 기계적 특성을 분석하여 경도를 주요 실험 인자로 선정하기 위한 근거를 마련하였다.
Fig. 1의 광학현미경 관찰 결과, 모든 시험편에서 Quenching 및 Tempering (Q&T) 처리에 의한 템퍼드 소르바이트(Tempered Sorbite) 조직이 주를 이루고 있음을 확인하였다. 그러나 상대적으로 높은 경도를 나타내는 Case 2와 Case 3에서는 침상 형태의 래스(Lath) 페라이트 조직의 흔적이 더욱 조밀하게 잔류하고 있으며, 이는 Case 1에 비해 조직적으로 치밀한 구성을 가짐을 시사한다. 이러한 미세조직적 특징은 재료의 강도와 내마모성 확보에는 유리하지만, 절삭 가공 시 전단 저항을 높이고 공구 마모를 촉진하여 가공성을 저하시키는 주요 원인이 된다[12,13].
Fig. 2는 각 Case의 기계적 특성을 비교한 결과이다. 경도가 증가함에 따라 인장강도는 639 (Case 1)에서 700 MPa (Case 3)로, 항복강도는 412에서 477 MPa로 선형적으로 증가하였다. 반면 신장율은 22% (Case 1)에서 16% (Case 3)로 감소하여 연성이 저하되는 경향을 보였다. 이러한 열처리 조건의 영향 분석을 바탕으로 경도가 가공 특성에 미치는 영향이 유의미할 것으로 판단하여 소재 경도를 주요 제어 인자로 선정하였다. 최적화 실험을 위해서는 경도 수준을 정밀하게 제어한 시험편이 필요하므로, 템퍼링 온도를 조절하여 실험용 시험편을 제작하였다.
2.2 시험편 제작
Table 1에 나타낸 바와 같이 가공 최적화 실험을 위해 커버 허브의 재질인 JIS SCMn2 합금 주강을 사용하였다. 탄소 함량이 0.25-0.35%의 중탄소 주강 재질로, Mn 첨가에 의해 담금질성(Hardenability)이 향상되어 열처리를 통한 강도 및 인성 제어가 용이하다. 열처리 조건의 영향 분석을 통해 경도가 미세조직 및 기계적 특성에 유의미한 영향을 미침을 확인하였으므로, 본 최적화 실험에서는 경도를 주요 제어 인자로 선정하였다. 이를 위해 담금질 온도를 860°C로 고정하고 템퍼링 온도를 변화시켜 Table 2와 같이 3가지 경도 수준(HB197, HB237, HB247)의 실험용 시험편을 제작하였다. 경도 수준은 열처리 조건의 영향을 분석한 양산 제품의 경도 범위(HB200 이하, HB200-210, HB220 이상)를 기준으로, 각 범위를 대표하는 값으로 설정하였다. HB197은 저경도 조건(Case 1), HB237은 중간 경도 조건(Case 2), HB247은 고경도 조건(Case 3)을 나타낸다. 시험편의 크기는 125 × 70 × 40 mm로 열처리 후 산화 스케일 제거 및 초기 표면 조건 확보를 위해 약 1.0 mm를 황삭 가공하였다.
2.3 실험방법
절삭가공은 DN Solutions Mynx 6500/50II 수직형 머시닝센터를 사용하여 수행하였다. 절삭 공구는 고경도 주강 가공에 적합한 한국야금(KORLOY Inc.)의 TiAlCrN 코팅기술이 적용된 PC5400 계열의 카바이드 인서트(XNKT080508PNSR-MM, Nose Radius 0.8 mm)를 사용하였다. 공구 커터는 직경 32 mm, 유효 길이 45 mm의 RM3PS4032HR-3S32 모델을 사용하였으며, Fig. 3과 같이 절삭 안정성 확보를 위해 3개의 인서트를 장착하였다. 또한, 각 블록에서는 Fig. 4에서와 같이 반경 방향 절입량(ae)을 공구 직경 대비 30% (9.6 mm), 50% (16 mm), 100% (32 mm)로 순차적으로 변화시키며 3회의 밀링 가공을 수행하였고, 모든 실험은 습식 가공 조건에서 수행하였으며, 수용성 절삭유를 절삭 가공시 연속 공급하였다. 각 절입량(ae) 조건에서 날당 이송량(fz), 주축 회전수(N), 축방향 절입량(ap) 등은 L27 직교배열(Table 3)에 따라 설정하였다.
3.1 매개변수 선정
밀링 가공에서 표면 거칠기에 영향을 미치는 주요 인자는 이송량, 절삭속도, 절삭깊이로 알려져 있다[3-9,14].
그러나 주강 소재의 경우 열처리 조건에 따른 경도 변화가 가공 특성에 미치는 영향은 상대적으로 연구가 제한적이다[15]. 이 실험을 위한 가공 매개변수는 다음과 같다.
- A: 날당 이송량(Feed per tooth, fz, mm/tooth)
- B: 주축회전수(Spindle speed, N, rpm)
- C: 축방향 절입량(Axial Depth of Cut, ap, mm)
- D: 경도(Hardness, HB)
- E: 반경방향 절입량(Radial Depth of Cut, ae, mm)
효율적인 실험 설계를 위해, Table 3과 같이 다구찌 방법의 L27(3^13) 직교배열을 적용하였다. 5인자 3수준 전면 요인 실험(3^5 = 243회)과 비교하여 실험 횟수를 89% 감소시키면서도 각 인자의 주효과를 독립적으로 평가할 수 있다. 각 매개변수의 수준값은 공구 제작사(KORLOY)의 권장 절삭 조건과 예비 가공 시험 결과를 기반으로 설정하였으며, 경도 범위는 양산 제품의 열처리 경도 분포를 반영하였다. 이러한 조건은 실제 양산 공정에서 적용 가능한 가공범위를 고려한 선정이다.
3.2 표면 거칠기 측정
이 연구의 목적은 SCMn2 주강의 밀링 가공에서 주요 공정 매개변수가 표면 거칠기에 미치는 영향을 정량화하고 최적 가공 조건을 도출하는 것이다.
커버허브의 밀봉면(Sealing Surface)은 누설과 관련해서 표면 품질의 신뢰성이 매우 중요하다. 산술평균거칠기(Ra)는 표면의 평균 특성을 나타내는 지표로서 국부적인 피크와 밸리의 최대 높이를 충분하게 반영하지 못한다. 반면, 최대 높이 거칠기(Rt, maximum peak-to-valley roughness height)는 표면의 최대 피크와 밸리 간 차이를 나타내므로, 절삭과정에서 발생 가능한 국부적 표면 결함과 밀봉 접촉면의 기능적 요구사항을 민감하게 평가할 수 있다. 따라서, 표면거칠기의 평가 지표로 Rt를 선정하였다. 측정은 Mitutoyo SJ-210 휴대용 표면 거칠기 측정기를 사용하였으며, ISO 21920-3:2021 표준에 따라 컷오프 길이(λc) 0.8 mm, 평가 길이(ln) 4.0 mm로 설정하였다. 각 실험 조건마다 가공 영역 중앙부에서 이송 방향에 수직하게 3회 반복 측정하여 평균값을 사용하였다.
3.3 데이터 분석 및 최적화
품질 특성으로 설정한 표면 거칠기는 값이 작을수록 바람직하므로, 데이터 분석을 위해 다구찌 방법의 망소특성(Smallerthe- better) 신호 대 잡음비를 품질 특성으로 적용하였다. 망소특성 S/N비는 식(1)과 같이 정의된다[11].
(1)
S/N=Power of SignalPower of Noise=-10log(MSD)
반복실험 측정 결과인 측정 데이터 값을 y1, y2, ...yn일 때 MSD (Mean Squared Deviation)는 식(2)와 같다.
(2)
MSD=1nΣi=1n(yi-0)2=1nΣi=1nyi2
여기서 n은 반복측정 횟수(n = 3), yi는 i 번째 표면거칠기 Rt 측정값이다. S/N비가 클수록 목표값 달성도가 높고 공정 변동에 대한 강건성(Robustness)이 우수함을 의미한다. 최적 가공 조건 도출을 위해 Minitab 18을 사용하여 S/N비 주효과 분석 및 분산분석을 수행하여 각 인자의 기여도를 정량화하고 통계적 유의성을 검증하였으며, 유의수준은 α = 0.05로 설정하였다.
4.1 표면거칠기 측정 결과 및 S/N비 분석
Table 4와 Fig. 5는 L27 직교배열에 따라 수행한 27회 실험의 표면거칠기(Rt) 측정 결과 및 식(1)에 따라 계산된 S/N비를 나타낸 것이다. Rt는 최소 3.13 μm (Exp. No. 1)에서 최대 10.48 μm(Exp. No. 19)의 범위로 측정되었으며, 평균값은 6.75 μm이었다. Fig. 5에서 날당 이송량이 0.1 mm/tooth인 조건에서 Rt가 전반적으로 낮게 나타났으며(평균 4.79 μm), 이는 Level 2 (6.56 μm) 및 Level 3 (8.9 μm) 대비 각각 27%, 46% 낮은 값이다.
최적 가공조건 도출을 위해 Minitab의 S/N비 반응표를 Table 5 및 Fig. 6과 같이 분석하였다. Delta 값 기준 인자별 영향도는 날당 이송량(A, Δ = 5.56 dB), 축방향 절입량(C, Δ = 2.85 dB), 경도(D, Δ = 1.74 dB), 주축속도(B, Δ = 1.00 dB), 반경방향 절입량(E, Δ = 0.7 dB) 순이다. 각 인자의 최적 수준은 A1 (0.1 mm/tooth), B1 (800 rpm), C1 (2.0 mm), D1 (HB197), E2 (16 mm)로 확인되었다.
4.2 분산분석 결과
Table 6은 각 인자의 통계적 유의성을 평가한 분산분석 결과를 나타낸 것이다(α = 0.05). 날당 이송량(p < 0.001, F = 72.73)과 축방향 절입량(p < 0.001, F = 19.83)은 통계적으로 매우 유의한 인자로 확인되었으며, 경도 역시 유의하였다(p = 0.006, F = 7.09). 반면 절삭속도(p = 0.119)와 반경방향 절입량(p = 0.285)은 통계적으로 유의하지 않았다.
각 인자의 기여도는 날당 이송량 65.26%, 축방향 절입량 17.79%, 소재 경도 6.36%, 절삭속도 2.18%, 반경방향 절입량 1.22%로 나타났다. S/N비 기반 분산분석 모델의 결정계수(R2)는 0.9282로, 선정된 5개 인자가 S/N비 변동의 92.82%를 설명할 수 있음을 의미한다. 이는 실험을 위한 표면거칠기에 영향을 미치는 주요 인자들을 적절히 선정하였으며, 개발된 회귀 모델이 새로운 가공 조건에서의 표면거칠기를 높은 정확도로 예측할 수 있음을 나타낸다.

4.2.1 주요인자간 교호작용 분석

다구찌 방법은 일반적으로 교호작용 효과가 주효과(Main Effect)에 비해 작다는 가정을 기반으로 하는 주효과 중심 분석에 적합한 실험으로 모든 인자간 교호작용을 독립적으로 평가하는 데에는 제한이 있다.
그러나 교호작용을 고려하지 않을 경우 최적 조건 해석이 왜곡될 수 있으므로, 유의한 인자로 확인된 날당 이송량(fz), 축방향 절입량(ap), 경도(HB) 간 2-요인 교호작용(Two-way Interaction)을 추가 분석하였다. Fig. 7은 주요 인자 간 교호작용(Interaction Plot)을 그래프로 나타낸 것이다.
Fig. 7(a)의 fz × ap 교호작용에서는, 세 곡선이 거의 평행하게 나타나 교호작용 효과가 크지 않음을 확인할 수 있다. 반면 Fig. 7(b) fz × HB 교호작용과 Fig. 7(c) ap ×HB 교호작용에서는 일부 비선형 패턴이 나타났다.
Table 7의 분산분석 결과에서도 fz × ap 교호작용은 F-value = 1.85, p-value = 0.164로 통계적으로 유의하지 않았으며, 이는 Fig. 7(a)의 경향과 일치한다. 반면 fz × HB 교호작용은 F-value = 5.41, p-value = 0.005로 유의하게 나타났으며, ap × HB 교호작용 또한 F-value = 26.45, p-value = 0.000으로 높은 유의성을 보였다. 이러한 결과는 일부 인자 조합에서 교호작용이 존재할 가능성을 시사한다. 다만 이 연구의 실험 설계 특성상 모든 교호작용을 독립적으로 평가하는 데에는 한계가 있으므로, 향후 연구에서는 전면요인설계 또는 반응표면분석법을 적용하여 교 호작용 효과를 보다 정량적으로 분석할 필요가 있다.
4.3 회귀분석 및 예측모델
임의의 가공 조건에서 표면거칠기를 예측하기 위해 선형 회귀분석을 수행하였다. 도출된 예측 방정식은 식(3)과 같다.
(3)
Rt=-5.99+41.06(fz)-0.000479(N)+0.898(ap)-0.01699(Hardness)+0.00845(ae)
여기서 Rt는 표면거칠기(μm), fz는 날당 이송량(mm/tooth), N은 주축 회전수(rpm), ap는 축방향의 절입량(mm), Hardness는 경도(HB), ae는 반경방향의 절입량(mm)이다. 선형회귀모델로 얻은 예측값과 실제 실험 결과를 Fig. 8에 비교하여 나타내었다. 표면거칠기 Rt의 선형회귀모델에서 도출된 R2은 87.46%로, 모델이 데이터의 변동성의 약 87%를 설명하는 것으로 나타났다. 이는 비교적 양호한 적합도를 의미하지만 일부 변동성은 공구마모나 기계진동 등 모델에 포함되지 않은 공정 변수의 영향과 관련될 수 있다. 본 모델의 적용 범위는 Table 3에서 제시한 실험 매개변수 범위로 제한되며, 범위를 벗어난 조건에서 외삽은 예측 오차가 증가할 수 있다. 또한 본 모델은 SCMn2 주강 습식 가공조건에서 도출된 결과이므로 다른 소재나 공구 조건에서는 추가 검증이 필요하다.
4.4 확인실험 및 고찰
Table 8 및 Fig. 9는 다구찌 방법으로 도출한 최적 조건의 유효성을 검증하기 위해 검증 실험을 수행한 결과이다. S/N비 분석 결과에 따라 도출된 최적 가공 조건(A1B1C1D1E2)으로, 통계적 신뢰도 확보를 위해 5회 반복 가공을 수행하여 표면거칠기를 측정한 결과, 평균 Rt는 3.033 μm으로 나타났다.
회귀 모델 방정식 3을 이용하여 최적 조건에서의 표면거칠기를 예측한 결과는 3.122 μm이며, 실측값과 예측값 간 오차는 2.93%에 불과하였다. 이는 회귀 모델의 높은 예측 정확도를 입증하는 결과이다.
최적 조건에서 달성한 Rt 3.033 μm은 초기 L27 실험의 최소값(3.13 μm) 대비 3% 개선, 전체 평균(6.75 μm) 대비 55.1% 감소한 값이다. 이는 다구찌 방법과 회귀분석으로 도출한 최적 가공 조건이 주강 소재의 표면 품질을 향상시킬 수 있음을 실험적으로 검증하였다.
날당 이송량의 기여도(65.26%)는 기하학적 표면거칠기 모델 Rt ≒ fz2/(8×rε) [10]과 정성적으로 일치한다. 이는 날당 이송량이 2배 증가 했을때 평균 표면거칠기(Rt)가 1.86배 증가하여, 이송량의 증가에 따라 표면거칠기가 더 거칠어지는 것을 실험적으로 알 수 있었다. 이러한 영향은 Mohamed [14]이 보고한 탄소강 밀링 연구(61.7%)보다 약 5.8% 높게 나타났는데, 이는 주강의 불균일한 미세조직과 상대적으로 낮은 가공성으로 인해 이송 조건 변화에 더욱 민감하게 반응하기 때문으로 해석된다.
축방향 절입량의 유의한 영향(기여도 17.79%)은 절삭력 증가와 절삭공정의 동적 불안정성 증가에 기인한다. 절입량이 증가하면 절삭 단면적과 공구 및 공작물 접촉 길이가 증가하여 절삭력이 상승하고 공구 처짐 및 미세 진동 발생 가능성이 커진다. 특히 ap × HB 교호작용은 주강 소재의 미세조직 불균일성과 경도편차로 인해 절삭 저항 변동이 발생할 수 있다. 이러한 절삭 저항 변동은 절입량이 증가할수록 절삭공정의 동적 불안정성을 증대시켜 표면에 불규칙한 공구 흔적을 형성하며, 결과적으로 표면거칠기 영향을 준 것으로 검토된다.
경도의 영향(기여도 6.36%)은 열처리 조건의 영향 분석과 일치하였다. 소재 경도 증가에 따른 절삭 저항 상승과 구성인선(BUE) 형성 증가에 기인하며, 수준3(HB247)에서 평균 Rt가 수준1(HB197) 대비 15% 증가하였다. 이는 Fig. 1에서 관찰된 바와 같이 고경도 조건에서 나타나는 침상 형태의 래스 페라이트 조직이 가공성을 저하시킨 결과로 판단된다. 따라서 주요 실험 인자를 경도로 선정한 것은 적절하였으며, 양산 공정에서 열처리 조건 관리는 경도 HB200 이하로 유지하는 것이 표면 품질 확보에 유리함을 확인하였다. 반면 절삭속도(기여도 2.18%)와 반경방향 절입량(기여도 1.22%)은 통계적으로 유의하지 않았다(p > 0.05). 이는 주축속도 범위(800-1200 rpm)가 주강 가공 권장 범위 내에 있어 변동 영향이 제한적이었기 때문인 것으로 사료된다.
4.5 연구 제한점 및 향후 연구 방향
다구찌 L27 직교배열은 주효과 중심 분석에 적합한 실험 설계로, 인자 간 교호작용을 독립적으로 평가하는 데에는 한계가 있다. 이에 따라 주요 인자 간 교호작용을 추가적으로 분석하였으나, 보다 정밀한 교호작용 분석을 위해서는 전면 요인 설계 또는 반응표면분석법을 활용한 추가 연구가 필요하다.
또한, 본 연구는 단일 절삭유 조건(Wet Cutting Condition)에서 수행되었으므로, 절삭유의 종류, 공급 방식 및 유량 변화가 가공 특성에 미치는 영향에 대한 추가적인 검토가 필요하다. 아울러 실제 가공 공정에서는 공구 수명과 가공 비용을 함께 고려해야 하므로, 향후 연구에서는 이를 반영한 다목적 최적화(Multi-objective Optimization) 접근이 요구된다.
본 연구에서는 SCMn2 주강의 밀링 가공에서 표면거칠기에 영향을 미치는 주요 가공 인자를 분석하고 최적 가공조건을 도출하기 위해 다구찌 방법을 적용하였다. 양산 제품의 열처리 조건에 따른 경도 변화가 미세조직 및 기계적 특성에 영향을 미침을 확인하고, 이를 주요 실험 인자로 설정하여 L27 직교배열 기반의 최적화 실험을 수행하였다. 주요 결과는 다음과 같다.
1.날당 이송량은 표면거칠기에 가장 큰 영향을 미치는 주요인자로 확인되었으며(기여도 65.26%, p < 0.001), 낮은 이송량(0.1 mm/tooth) 조건이 표면 품질 개선에 가장 효과적이었다.
2. 축방향 절입량과 소재 경도(템퍼링 온도)는 표면거칠기에 통계적으로 유의한 영향을 미치는 인자로 확인되었다(기여도 각각 17.79%, 6.36%, p < 0.01). 낮은 절입량(2.0 mm)과 낮은 경도(HB197) 조건에서 우수한 표면 품질을 얻을 수 있었다.
3. 주축회전속도와 반경방향 절입량은 본 연구의 실험 범위 내에서 표면거칠기에 대한 통계적 영향이 유의하지 않았다(p > 0.05).
4. 선형 회귀 모델은 표면거칠기 데이터 변동성의 87.46%를 설명하는 것으로 나타났으며(R2 = 0.8746), 실험 범위 내에서 가공조건에 따른 표면거칠기 경향을 합리적으로 예측할 수 있었다.
5.최적 가공 조건(fz = 0.1 mm/tooth, N = 800 rpm, ap = 2.0 mm, HB = 197, ae = 16 mm)에서 수행한 확인 실험 결과, 평균 표면 거칠기 Rt 3.033 μm을 얻었으며. 이는 회귀 모델 예측값(3.122 μm)과 2.93% 오차로 일치하였다.
이상의 결과로부터 SCMn2 주강의 밀링 가공에서는 날당 이송량, 축방향 절입량 및 소재 경도가 표면거칠기를 지배하는 주요 인자로 확인되었다. 본 연구에서 도출한 최적 가공 조건과 예측 모델은 특수차량 커버허브와 같은 주강 부품의 정밀 가공에서 표면 품질 향상과 공정 조건 설정을 위한 참고 기준으로 활용될 수 있다. 특히 본 연구의 실험 범위에서는 열처리 조건을 관리하여 경도를 HB200 이하로 유지하고, 날당 이송량을 0.1 mm/tooth 수준으로 설정하는 것이 표면 품질 확보에 유리한 것으로 나타났다.
Fig. 1
Microstructures of SCMn2B cast steel at different hardness levels (500×). (a) Case1 (≤HB200), (b) Case2 (HB200–210), (c) Case3 (≥HB220)
JKSPE-026-00012f1.jpg
Fig. 2
Mechanical properties of cover hub specimen
JKSPE-026-00012f2.jpg
Fig. 3
Shape of cutting tool and insert tip used in the experiment. (a) Cutter and tool holder, (b) Machining process
JKSPE-026-00012f3.jpg
Fig. 4
Schematic drawing for experimental
JKSPE-026-00012f4.jpg
Fig. 5
Measured surface roughness (Rt) and calculated S/N ratio
JKSPE-026-00012f5.jpg
Fig. 6
Main effects of process parameters on the mean S/N ratio (Signal-to-noise: Smaller is better)
JKSPE-026-00012f6.jpg
Fig. 7
Interaction plots of major factors for surface roughness (Rt). (a) fz × ap interaction, (b) fz × HB interaction, (c) ap × HB interaction
JKSPE-026-00012f7.jpg
Fig. 8
Comparison between the linear regression model and experimental results
JKSPE-026-00012f8.jpg
Fig. 9
Comparison of the prediction and confirmation results on optimal machining conditions
JKSPE-026-00012f9.jpg
Table 1
Chemical composition of cast steel (SCMn2)
Table 1
C Si Mn P S Fe
0.25 0.30 1.00 0.040 0.040 Rem.
0.35 0.60 1.60 max. max.
Table 2
Heat treatment conditions for experimental specimens (based on preliminary analysis in section 2.1)
Table 2
Quenching Tempering Hardness (HB)
860°C, 2 hr 660°C, 4 hr 197
860°C, 2 hr 640°C, 4 hr 237
860°C, 2 hr 620°C, 4 hr 247
Table 3
Parameters and levels for cutting process
Table 3
Symbols Factors Level
1 2 3
A Feed per tooth [mm/tooth] 0.1 0.15 0.2
B Spindle speed [rpm] 800 1000 1200
C Depth of cut [ap, mm] 2.0 3.0 4.0
D Hardness [HB] 197 237 247
E Radial depth of cut [ae, %] 30 50 100
Table 4
Experimental results and calculated S/N ratios for L27 orthogonal array of the Taguchi method
Table 4
Exp. No. Parameters Rt [μm] S/N ratio [dB]
A B C D E
1 1 1 1 1 1 3.13 −9.92
2 1 1 1 1 2 3.28 −10.32
3 1 1 1 1 3 3.58 −11.07
4 1 2 2 2 1 5.43 −14.70
5 1 2 2 2 2 4.66 −13.37
6 1 2 2 2 3 5.38 −14.62
7 1 3 3 3 1 5.72 −15.15
8 1 3 3 3 2 6.68 −16.50
9 1 3 3 3 3 5.27 −14.44
10 2 1 2 3 1 6.47 −16.21
11 2 1 2 3 2 7.26 −17.21
12 2 1 2 3 3 7.53 −17.54
13 2 2 3 1 1 7.02 −16.92
14 2 2 3 1 2 7.16 −17.10
15 2 2 3 1 3 7.51 −17.51
16 2 3 1 2 1 6.04 −15.62
17 2 3 1 2 2 4.58 −13.22
18 2 3 1 2 3 5.49 −14.78
19 3 1 3 2 1 10.48 −20.40
20 3 1 3 2 2 9.44 −19.50
21 3 1 3 2 3 9.17 −19.24
22 3 2 1 3 1 7.26 −17.22
23 3 2 1 3 2 8.71 −18.80
24 3 2 1 3 3 10.23 −20.19
25 3 3 2 1 1 7.73 −17.77
26 3 3 2 1 2 7.11 −17.04
27 3 3 2 1 3 9.97 −19.98
Table 5
S/N response table for surface roughness (Rt)
Table 5
Level fz N ap HB ae
1 −13.34 −15.71 −14.57 −15.29 −15.99
2 −16.24 −16.72 −16.49 −16.16 −15.90
3 −18.91 −16.06 −17.42 −17.03 −16.60
Delta 5.56 1.00 2.85 1.74 0.70
Rank 1 4 2 3 5
Table 6
Analysis of variance for S/N response
Table 6
Source DF Adj SS Adj MS F-Value P-Value Contribution (%)
fz 2 139. 298 69.6491 72.73 0.000 65.26
N 2 4.661 2.3306 2.43 0.119 2.18
ap 2 37.978 18.9892 19.83 0.000 17.79
HB 2 13.576 6.7882 7.09 0.006 6.36
ae 2 2.607 1.3035 1.36 0.285 1.22
Error 16 15.323 0.9577 - - 7.18
Total 26 213.444 - - - 100
S=0.978609, R-sq=92.82%, R-sq(adj)=88.33%
Table 7
Results of ANOVA on interactions between major factors
Table 7
Interaction F-Value P-Value Description
fz × ap 1.85 0.164 Interaction weak
fz × HB 5.41 0.005 Significant
Ap × HB 26.45 0.000 Strong
Table 8
Prediction and confirmation test results for optimal machining conditions (A1B1C1D1E2) by Taguchi method
Table 8
Description Surface roughness [Rt] S/N ratio [dB]
Prediction 3.122 −9.889
Confirmation 1 3.051 −9.689
2 3.006 −9.560
3 3.085 −9.785
4 3.012 −9.577
5 3.009 −9.568
Avg’ 3.033 −9.636
Error [%] 2.93 -
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Seong Je Woo
JKSPE-026-00012i1.jpg
Ph.D. candidate in the Department of Convergence Mechanical Engineering, Gyeongsang National University. His research interest is Mechanical System Engineering.
Hyo Chang Kim
JKSPE-026-00012i2.jpg
Ph.D. candidate in the Department of Convergence Mechanical Engineering, Gyeongsang National University. His research interest is Precision Engineering.
Won Pyo Jeon
JKSPE-026-00012i3.jpg
Professor in the School of Industrial Facility, Suncheon Campus of Korea Polytechnics. His research interest is Integrative Mechanical Engineering.
Sung Ki Lyu
JKSPE-026-00012i4.jpg
Professor in the School of Mechanical Engineering, Gyeongsang National University. His research interest is Mechanical System Design.

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A Study on Optimizing Machining Conditions for Enhanced Surface Roughness in High Tensile Cast Steels
J. Korean Soc. Precis. Eng.. 2026;43(7):709-716.   Published online July 1, 2026
DOI: https://doi.org/10.7736/JKSPE.026.00012
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J. Korean Soc. Precis. Eng.. 2026;43(7):709-716.   Published online July 1, 2026
DOI: https://doi.org/10.7736/JKSPE.026.00012
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A Study on Optimizing Machining Conditions for Enhanced Surface Roughness in High Tensile Cast Steels
Image Image Image Image Image Image Image Image Image
Fig. 1 Microstructures of SCMn2B cast steel at different hardness levels (500×). (a) Case1 (≤HB200), (b) Case2 (HB200–210), (c) Case3 (≥HB220)
Fig. 2 Mechanical properties of cover hub specimen
Fig. 3 Shape of cutting tool and insert tip used in the experiment. (a) Cutter and tool holder, (b) Machining process
Fig. 4 Schematic drawing for experimental
Fig. 5 Measured surface roughness (Rt) and calculated S/N ratio
Fig. 6 Main effects of process parameters on the mean S/N ratio (Signal-to-noise: Smaller is better)
Fig. 7 Interaction plots of major factors for surface roughness (Rt). (a) fz × ap interaction, (b) fz × HB interaction, (c) ap × HB interaction
Fig. 8 Comparison between the linear regression model and experimental results
Fig. 9 Comparison of the prediction and confirmation results on optimal machining conditions

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Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

A Study on Optimizing Machining Conditions for Enhanced Surface Roughness in High Tensile Cast Steels
C Si Mn P S Fe
0.25 0.30 1.00 0.040 0.040 Rem.
0.35 0.60 1.60 max. max.
Quenching Tempering Hardness (HB)
860°C, 2 hr 660°C, 4 hr 197
860°C, 2 hr 640°C, 4 hr 237
860°C, 2 hr 620°C, 4 hr 247
Symbols Factors Level
1 2 3
A Feed per tooth [mm/tooth] 0.1 0.15 0.2
B Spindle speed [rpm] 800 1000 1200
C Depth of cut [ap, mm] 2.0 3.0 4.0
D Hardness [HB] 197 237 247
E Radial depth of cut [ae, %] 30 50 100
Exp. No. Parameters Rt [μm] S/N ratio [dB]
A B C D E
1 1 1 1 1 1 3.13 −9.92
2 1 1 1 1 2 3.28 −10.32
3 1 1 1 1 3 3.58 −11.07
4 1 2 2 2 1 5.43 −14.70
5 1 2 2 2 2 4.66 −13.37
6 1 2 2 2 3 5.38 −14.62
7 1 3 3 3 1 5.72 −15.15
8 1 3 3 3 2 6.68 −16.50
9 1 3 3 3 3 5.27 −14.44
10 2 1 2 3 1 6.47 −16.21
11 2 1 2 3 2 7.26 −17.21
12 2 1 2 3 3 7.53 −17.54
13 2 2 3 1 1 7.02 −16.92
14 2 2 3 1 2 7.16 −17.10
15 2 2 3 1 3 7.51 −17.51
16 2 3 1 2 1 6.04 −15.62
17 2 3 1 2 2 4.58 −13.22
18 2 3 1 2 3 5.49 −14.78
19 3 1 3 2 1 10.48 −20.40
20 3 1 3 2 2 9.44 −19.50
21 3 1 3 2 3 9.17 −19.24
22 3 2 1 3 1 7.26 −17.22
23 3 2 1 3 2 8.71 −18.80
24 3 2 1 3 3 10.23 −20.19
25 3 3 2 1 1 7.73 −17.77
26 3 3 2 1 2 7.11 −17.04
27 3 3 2 1 3 9.97 −19.98
Level fz N ap HB ae
1 −13.34 −15.71 −14.57 −15.29 −15.99
2 −16.24 −16.72 −16.49 −16.16 −15.90
3 −18.91 −16.06 −17.42 −17.03 −16.60
Delta 5.56 1.00 2.85 1.74 0.70
Rank 1 4 2 3 5
Source DF Adj SS Adj MS F-Value P-Value Contribution (%)
fz 2 139. 298 69.6491 72.73 0.000 65.26
N 2 4.661 2.3306 2.43 0.119 2.18
ap 2 37.978 18.9892 19.83 0.000 17.79
HB 2 13.576 6.7882 7.09 0.006 6.36
ae 2 2.607 1.3035 1.36 0.285 1.22
Error 16 15.323 0.9577 - - 7.18
Total 26 213.444 - - - 100
S=0.978609, R-sq=92.82%, R-sq(adj)=88.33%
Interaction F-Value P-Value Description
fz × ap 1.85 0.164 Interaction weak
fz × HB 5.41 0.005 Significant
Ap × HB 26.45 0.000 Strong
Description Surface roughness [Rt] S/N ratio [dB]
Prediction 3.122 −9.889
Confirmation 1 3.051 −9.689
2 3.006 −9.560
3 3.085 −9.785
4 3.012 −9.577
5 3.009 −9.568
Avg’ 3.033 −9.636
Error [%] 2.93 -
Table 1 Chemical composition of cast steel (SCMn2)
Table 2 Heat treatment conditions for experimental specimens (based on preliminary analysis in section 2.1)
Table 3 Parameters and levels for cutting process
Table 4 Experimental results and calculated S/N ratios for L27 orthogonal array of the Taguchi method
Table 5 S/N response table for surface roughness (Rt)
Table 6 Analysis of variance for S/N response
Table 7 Results of ANOVA on interactions between major factors
Table 8 Prediction and confirmation test results for optimal machining conditions (A1B1C1D1E2) by Taguchi method

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

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