Process planning and DFM §

• Decision criteria: geometry complexity, material, batch size, tolerance, surface finish, cost, and lead time.
• Rule of thumb: for a feature, choose the process that achieves the required tolerance and finish at the lowest cost per part for the given batch size.
• Hybrid processes: a part may use multiple processes. E.g., a turbine blade: Casting (gross shape) → Subtractive manufacturing (precision machining) → Surface finishing (coating).
• Batch-size threshold: below ~50 parts, additive or direct machining is often cheapest; above ~500, casting or forming amortizes tooling; above ~10 000, dedicated tooling (die-casting, stamping) dominates.

• CNC turning: 2.10 + 1.80 + 0.10 = $4.00 per part.
• Forging + machining: 0.90 + 1.20 + 4.50 = $6.60 per part.
• CNC turning wins at $N = 200$: the forging die ($900 total) is amortized over too few parts. The crossover is at $N^* = 900 / (4.00 - 2.10 - 0.10) \approx 500$ parts, above which forging becomes cheaper.

Step 1 — Global rule. Sequence operations so that every operation references a datum that already exists: roughing removes bulk material and stress first, thermal treatments that distort the part come next, and precision finishing comes last so that no later operation destroys the achieved accuracy.

Step 2 — Ordering rules.

• Roughing before finishing: remove bulk material with a roughing operation, then finish with a precision operation.
• Thermal operations early: heat treatment, casting, or forming are done before precision machining to avoid distortion.
• Primary datum first: machine the primary datum face before any other feature, so that all subsequent operations reference a stable, accurate surface.
• Fixturing: each operation requires a fixture that locates and clamps the part. The fixture design affects achievable tolerance and cycle time (see the Fixture and workholding design section below).
• In-process inspection: critical dimensions are measured after each operation to detect drift before the next operation.
• Deburr and clean between operations to prevent chip contamination of finished surfaces.

Step 3 — Worked example. For the aluminum bracket of the Quizz below, the sequence is: Op 10 rough mill the pocket (leaves 0.5 mm stock, removes ~90 % of the chip volume); Op 20 finish mill the pocket (0.2 mm stepover, reaches final dimensions while the part stays in the same fixture); Op 30 drill and ream the mounting holes (reaming last, because it references the finished pocket faces); Op 40 deburr and clean. Reversing Ops 20 and 40 would leave chips in the pocket during reaming and scratch the finished surfaces during deburring.

Step 4 — Authoritative source. The roughing/finishing and datum-first rules are the operation-planning chapters of Boothroyd, Dewhurst, Knight, Product Design for Manufacture and Assembly.

Example problem — a steel part needs four operations: (a) rough milling, (b) quench-and-temper heat treatment, (c) finish milling, (d) drilling and reaming of a Ø10 H7 hole. Order them and justify.

Worst-case (linear) stack-up:

Each individual tolerance $T_i$ is the full bilateral tolerance of feature $i$. The result is the maximum possible deviation. Conservative, simple, and independent of the distribution of errors.

Statistical (RSS) stack-up:

Assumes each $T_i$ is an independent, normally distributed error with 3σ spread. The system tolerance is the 3σ of the combined distribution. For equal-magnitude tolerances the RSS result is tighter than worst-case by a factor of $\sqrt{n}$: 50 % tighter at $n = 4$, 67 % tighter at $n = 9$.

A dimensional chain is the sequence of features whose errors accumulate into one system dimension (here a clearance gap $G$ between the last block and the wall):

Worked example. Four blocks, each ±0.1 mm, in the chain above. Worst-case: $T = 4 \times 0.1 = 0.4$ mm. RSS: $T = \sqrt{4 \times 0.1^2} = 0.2$ mm — 50 % tighter, because it is unlikely that all four errors align in the same direction.

Cost-based allocation:

The allocation problem is:

where $C_i(T_i)$ is the cost of achieving tolerance $T_i$ on feature $i$ (typically $C_i \propto T_i^{-k}$ with $k \approx 0.5$–$1.0$), and $f$ is the stack-up function (linear or RSS). The optimal solution equalizes the marginal cost per unit of tolerance across all features: $\partial C_i / \partial T_i = \lambda$ for all $i$.

Practical guidance:

• Use worst-case for safety-critical or functional fits (gears, bearings).
• Use RSS for non-critical dimensional chains with $n \geq 4$ independent errors.
• Use cost-based allocation when the system tolerance is loose enough to allow trade-offs.

Example problem — a chain of 5 features, each ±0.05 mm, must fit a system tolerance of ±0.15 mm. Does the chain pass with worst-case? With RSS?

Step 1 — Global rule. A rigid body in 3-D has 6 degrees of freedom (3 translations, 3 rotations). The fixture must remove all 6 with the minimum number of contact points, then clamp. The canonical scheme is 3-2-1:

Step 2 — The 3-2-1 locating principle.

• 3 points on the primary datum face (removes 3 DOF: 2 rotations, 1 translation).
• 2 points on the secondary datum face (removes 2 DOF: 1 rotation, 1 translation).
• 1 point on the tertiary datum face (removes 1 DOF: 1 translation).

The 3-2-1 scheme is the minimum constraint. Additional locators are used only for support (to prevent deflection under cutting forces), not for location.

Step 3 — Clamp force. The clamp force must exceed the maximum cutting force component that tends to lift or rotate the part, with a safety factor of 2–3:

where $\mu$ is the coefficient of friction between the part and the fixture (typically 0.3–0.5 for steel-on-steel), and $SF$ is the safety factor. For a milling operation, $F_{\text{cut, lift}}$ is the vertical component of the cutting force, which can be estimated from the specific cutting force $K_c$ (N/mm²) times the uncut chip area.

Worked example. Milling lifts the part with $F_{\text{cut, lift}} = 300$ N; $\mu = 0.4$, $SF = 2$. Then $F_{\text{clamp}} \geq (300 / 0.4) \times 2 = 1500$ N — a standard vise rated at 5 kN is sufficient.

Step 4 — Trade-off: fixture complexity vs. achievable tolerance.

• A simple vise or soft jaws: setup in minutes, tolerance ~0.1 mm, low cost.
• A dedicated fixture with precision locators: setup in hours, tolerance ~0.01 mm, high cost (amortized over batch).
• A palletized fixture with in-process probing: setup in seconds (changeover), tolerance ~0.005 mm, very high cost, justified for high-mix, high-volume lines.

The fixture must be designed after the operation sequence is fixed, because the clamping face and access directions depend on which features are machined in which setup.

Example problem — a cutting operation tends to lift the part with 500 N; the fixture pads are dry steel-on-steel ($\mu = 0.3$); the shop uses a safety factor of 2.5. What minimum clamp force must the fixture provide?

Step 1 — Global flow. CAM takes the CAD model plus the process plan (which operations, which tools, which parameters) and generates the tool path: roughing passes that follow the stock boundary, finishing passes that follow the final geometry, then drilling cycles. The tool path determines cycle time, surface finish, and tool wear.

Step 2 — Verification and post-processing.

• Simulation: the CAM software simulates the tool path to detect collisions, overcuts, and undercuts before the part is machined.
• Post-processing: the tool path is translated into the specific machine’s control language (FANUC, Siemens, Heidenhain, etc.).

Step 3 — Cycle time estimation:

where $t_{\text{cut}}$ depends on feed rate, stepover, and depth of cut; $t_{\text{rapid}}$ on machine travel speed; $t_{\text{setup}}$ on fixture and tool-change time.

Worked example. A facing pass of 200 mm at a feed rate of 1200 mm/min: $t_{\text{cut}} = 200/1200 = 0.167$ min. Rapids of 300 mm at 15 000 mm/min: $t_{\text{rapid}} = 0.02$ min. One tool change of 6 s: $t_{\text{setup}} = 0.1$ min. Total $t_{\text{cycle}} \approx 0.29$ min ≈ 17 s per part.

Example problem — a pocket finishing pass has a total path length of 1200 mm at 900 mm/min, plus 500 mm of rapids at 12 000 mm/min, plus two tool changes of 5 s each. Compute the cycle time in minutes.

Step 1 — The standard. ISO 10303-224 (Application protocol: Mechanical product definition for process planning using machining features) defines the data model for machining process plans: operations, setups, tools, tolerances, and fixtures as structured entities.

Step 2 — Worked example. An aerospace supplier receives an AP 224 file from a customer. The planning software parses it and validates the plan automatically: every machining feature in the model has an assigned operation, every tolerance is within the shop’s demonstrated process capability, and every tool called out exists in the tool library. The check catches one hole with no assigned drilling operation before the plan reaches the shop floor — a defect that would otherwise surface as a scrapped part.

Step 3 — Practice.

• In practice, most shops still exchange process plans as PDFs, Excel sheets, or proprietary CAM files. STEP AP 224 adoption is growing in aerospace and automotive supply chains where multi-vendor data exchange is mandatory.
• The key benefit of a structured process plan: it can be validated (e.g., check that all features are covered, that tolerances are consistent, that tool access is feasible) before the plan reaches the shop floor.

1. Stock: 6061-T6 flat bar, 100 × 100 × 50 mm. Source from Materials processing.
2. Process selection:
   • Gross shape: the bar is already the right size. No Forming or Casting needed.
   • Pocket and holes: Subtractive manufacturing (CNC milling). The 5 mm pocket and 2 mm wall are within the capability of a 3-axis or 4-axis mill.
   • Surface finish: the as-machined finish (Ra 1.6 µm) is acceptable. No Surface finishing needed unless a cosmetic finish is required.
3. Operation sequence:
   • Op 10: Rough mill the pocket (stepover 1 mm, depth of cut 2 mm).
   • Op 20: Finish mill the pocket (stepover 0.2 mm, depth of cut 0.5 mm).
   • Op 30: Drill and ream the mounting holes (0.1 mm tolerance requires reaming).
   • Op 40: Deburr and clean.
4. Fixturing: a vise with soft jaws to clamp the bar. The pocket is machined in one setup to maintain the 0.1 mm tolerance.
5. Inspection: CMM (coordinate measuring machine) after Op 40 to verify all critical dimensions. Metrology and quality control.
6. Cost comparison (batch = 1000):

CNC milling wins at this batch size: the die-casting die cost ($12 000) is not amortized enough at 1000 parts, and SLM material + post-processing cost is too high for a simple bracket. The crossover for die-casting would be at ~5000–10 000 parts. 7. Cost summary: at 1000 parts, total cost ≈ $9 000. The cycle time is ~15 min/part; the total machine time is ~250 hours.