Manufacturing Processes Interview Questions

Casting involves pouring molten metal into a mold where it solidifies into the desired shape, while forging uses compressive forces to deform solid metal into shape. Casting is suitable for complex geometries but may have porosity, whereas forging produces stronger parts with better grain structure due to the mechanical working of the material.

The main casting types are: Sand casting (large parts like engine blocks), Die casting (high-volume precision parts like automotive components), Investment casting (complex aerospace parts with fine details), and Centrifugal casting (pipes and cylindrical components). Selection depends on production volume, part complexity, dimensional accuracy, and material requirements.

CNC (Computer Numerical Control) machining uses programmed instructions to control machine tools automatically, eliminating manual operation. It is preferred because it offers higher precision (tolerances up to 0.001mm), repeatability, faster production rates, reduced human error, ability to create complex geometries, and consistent quality across large production batches.

MIG (Metal Inert Gas) welding uses a continuously fed wire electrode and is faster, easier to learn, and suitable for thicker materials and high-production environments. TIG (Tungsten Inert Gas) welding uses a non-consumable tungsten electrode with separate filler rod, offering superior control, cleaner welds, and is preferred for thin materials, precision work, and aesthetic welds on stainless steel and aluminum.

Forging is a manufacturing process where metal is shaped using localized compressive forces, typically delivered by a hammer or press. Main advantages include superior mechanical properties due to grain flow alignment, elimination of internal voids and porosity, excellent fatigue resistance, and the ability to produce parts stronger than cast or machined equivalents. It is widely used for critical components like crankshafts, connecting rods, and aerospace parts.

Hot working is performed above the recrystallization temperature of the metal, allowing easier deformation, no strain hardening, and refinement of grain structure. Cold working is done below recrystallization temperature, resulting in strain hardening, improved surface finish, tighter tolerances, but requires more force and may cause residual stresses. Hot working is used for primary shaping while cold working is for finishing operations.

Additive manufacturing builds parts layer-by-layer from digital models, unlike subtractive methods that remove material. Main technologies include FDM (Fused Deposition Modeling) for plastics, SLA (Stereolithography) for high-detail resin parts, SLS (Selective Laser Sintering) for functional prototypes, and DMLS/SLM for metal parts. It enables complex geometries impossible with traditional manufacturing and is ideal for prototyping and low-volume production.

In turning, the workpiece rotates while a stationary cutting tool removes material, primarily used for cylindrical parts on lathes. In milling, the cutting tool rotates while the workpiece is stationary or moves linearly, used for flat surfaces, slots, and complex contours. Turning is for axisymmetric parts like shafts and bushings, while milling handles prismatic parts, pockets, and 3D surfaces.

Spot welding (Resistance Spot Welding) joins metal sheets by applying pressure and passing high current through the contact point, generating heat through electrical resistance to fuse the materials. It is extensively used in automotive body assembly (a typical car has 3000-5000 spot welds), appliance manufacturing, and sheet metal fabrication due to its speed, automation capability, and suitability for thin gauge materials.

Pattern allowances are modifications made to pattern dimensions to compensate for changes during casting. Key allowances include: Shrinkage allowance (compensates for metal contraction during cooling), Draft allowance (taper for easy pattern removal), Machining allowance (extra material for finishing), Distortion allowance (for irregular shapes), and Shake allowance (for pattern withdrawal). These ensure the final casting meets design specifications.

Quality control ensures products meet specified standards through inspection, testing, and process monitoring. Key activities include incoming material inspection, in-process checks, final inspection, statistical process control (SPC), and root cause analysis for defects. QC prevents defective products from reaching customers, reduces waste and rework costs, maintains customer satisfaction, and ensures compliance with industry standards like ISO 9001.

Drilling creates new holes using a rotating drill bit, while boring enlarges existing holes to achieve precise dimensions and better surface finish. Drilling is a roughing operation with moderate accuracy, whereas boring is a finishing operation providing tight tolerances (up to 0.01mm) and concentricity. Boring is used when hole accuracy, size, or surface finish requirements exceed what drilling can achieve.

Common sheet metal processes include: Blanking (cutting flat shapes), Bending (creating angles), Deep drawing (forming cups/containers), Stamping (forming complex shapes), Punching (creating holes), and Roll forming (continuous profiles). These processes are widely used in automotive body panels, appliances, enclosures, and consumer products due to their high production rates and material efficiency.

The Heat Affected Zone (HAZ) is the area of base metal adjacent to the weld that experiences thermal cycles but does not melt. The HAZ undergoes microstructural changes due to heating and cooling, which can affect mechanical properties - potentially causing hardening, softening, or reduced corrosion resistance depending on the material. Controlling HAZ size and properties is critical for weld quality, achieved through proper heat input and welding parameters.

A Coordinate Measuring Machine (CMM) is a precision device that measures the geometry of physical objects by sensing discrete points on surfaces using a probe. CMMs can measure dimensions, form, and position of features with accuracy up to micrometers. They are used for first article inspection, in-process quality checks, reverse engineering, and comparing manufactured parts against CAD models. Modern CMMs can be manual, CNC-controlled, or use optical/laser scanning.

Common casting defects include: Porosity (caused by trapped gases - prevent with proper venting and degassing), Shrinkage cavities (due to inadequate feeding - use risers and chills), Hot tears (from restrained contraction - modify design and use proper allowances), Cold shuts (incomplete filling - increase pouring temperature and speed), and Misruns (premature solidification - optimize gating and fluidity). Prevention requires proper mold design, process parameter control, and material quality.

G-codes control machine movements: G00 (rapid positioning), G01 (linear interpolation), G02/G03 (circular interpolation CW/CCW), G17/18/19 (plane selection), G20/G21 (inch/metric units), G28 (home position), G40/41/42 (cutter compensation), G90/91 (absolute/incremental positioning). M-codes control machine functions: M00 (program stop), M03/M04 (spindle CW/CCW), M05 (spindle stop), M06 (tool change), M08/M09 (coolant on/off), M30 (program end). Understanding these is essential for CNC programming and troubleshooting.

Common welding defects include: Porosity (gas pockets), Lack of fusion (incomplete bonding), Undercut (groove at weld toe), Cracks (hot/cold cracking), Incomplete penetration, and Slag inclusions. Detection methods include: Visual inspection (surface defects), Dye penetrant testing (surface cracks), Magnetic particle testing (ferromagnetic materials), Ultrasonic testing (internal defects), and Radiographic testing (comprehensive internal inspection). Selection of NDT method depends on defect type, material, and criticality.

Critical die casting parameters include: Injection pressure (typically 10-175 MPa, affects filling and density), Injection speed (slow shot and fast shot phases), Die temperature (controls solidification and surface quality), Metal temperature (affects fluidity and cycle time), Cycle time (productivity vs quality balance), and Vacuum level (for high-integrity parts). Optimizing these parameters through DOE and simulation reduces porosity, improves surface finish, and minimizes cycle time while maintaining dimensional accuracy.

Cutting parameters depend on: Material properties (harder materials need lower speeds), Tool material (HSS, carbide, ceramic have different ranges), Operation type (roughing vs finishing), Machine rigidity, and Coolant availability. General guidelines: Cutting speed (Vc) affects tool life and surface finish, Feed rate (f) affects material removal rate and surface roughness, Depth of cut (ap) affects forces and stability. Use manufacturer recommendations as starting point, then optimize through trials. Higher speeds with lower feeds give better finish; lower speeds with higher feeds maximize MRR.

Key forging die design considerations include: Parting line location (affects grain flow and die cost), Draft angles (typically 3-7 degrees for easy ejection), Fillet radii (prevent stress concentrations and improve material flow), Flash design (controls excess material flow), Die material selection (H13 tool steel common for hot forging), Preform design (multi-stage forging), and Grain flow optimization (align with stress directions). Simulation tools like DEFORM help optimize die design before expensive tooling fabrication.

SPC uses statistical methods to monitor and control processes. Key elements include: Control charts (X-bar R charts for variables, p-charts for attributes), Process capability indices (Cp, Cpk - target Cpk > 1.33), Upper/Lower control limits (UCL/LCL at +/-3 sigma), and Rules for detecting out-of-control conditions (Western Electric rules). SPC helps identify process variations before they cause defects, distinguishes between common cause and special cause variation, and enables data-driven process improvement. It is fundamental to Six Sigma and lean manufacturing.

Main metal AM technologies: DMLS/SLM (Direct Metal Laser Sintering/Selective Laser Melting) - uses laser to fuse metal powder, excellent for complex geometries, used in aerospace and medical implants. EBM (Electron Beam Melting) - uses electron beam in vacuum, faster for titanium, common in orthopedic implants. Binder Jetting - binds powder with adhesive then sinters, more economical for larger parts. DED (Directed Energy Deposition) - deposits and melts wire/powder, used for repair and adding features. Selection depends on material, part size, accuracy requirements, and production volume.

A WPS is a documented procedure specifying how welding should be performed for a specific application. It includes: Joint design and dimensions, Base metal specifications, Filler metal requirements, Preheat and interpass temperatures, Welding parameters (current, voltage, travel speed), Position and progression, Shielding gas composition, and Post-weld heat treatment. WPS ensures consistent weld quality, is required by codes (AWS, ASME), must be qualified through Procedure Qualification Records (PQR), and is essential for traceability and quality assurance in critical applications.

Tool path optimization strategies include: High-speed machining (HSM) with constant chip load for reduced cycle time, Trochoidal milling for slot cutting with reduced tool wear, Adaptive clearing for consistent engagement angles, Climb milling vs conventional milling selection, Optimized entry/exit strategies to reduce shock loading, Minimizing air cutting and rapid movements, and Using appropriate step-over and step-down values. Modern CAM software offers automatic optimization, but understanding fundamentals helps in parameter selection and troubleshooting. Balance between cycle time, surface finish, and tool life is key.

Investment casting (lost wax process) involves: Creating wax patterns, Assembling patterns on a tree, Building ceramic shell through repeated dipping and stuccoing, Dewaxing (autoclave or flash fire), Firing shell to strengthen, Pouring molten metal, Breaking shell and finishing. Advantages: Excellent surface finish (Ra 1.6-3.2 micrometers), Tight tolerances (+/-0.1%), Complex geometries and thin walls, Wide material range, Near-net-shape reducing machining. Used for turbine blades, medical implants, jewelry, and aerospace components where precision and material properties justify higher cost.

Common deep drawing defects include: Wrinkling (insufficient blank holder force - increase BHF or use draw beads), Tearing (excessive stress - reduce BHF, increase die radius, use lubricant), Earing (anisotropy - optimize blank shape), Orange peel (large grain size - use finer grain material), and Scratches (poor lubrication or die surface - improve both). Process parameters to control: Blank holder force, Die/punch radii (typically 5-10x sheet thickness), Drawing ratio (limit ~2.0 for first draw), Lubrication, and Clearance (1.1-1.2x sheet thickness).

GD&T specifies allowable geometric variations using standardized symbols (per ASME Y14.5 or ISO GPS). Manufacturing impacts include: Process selection (tighter tolerances may require grinding vs milling), Fixture design (datum references define part holding), Inspection methods (CMM programming based on GD&T callouts), Cost implications (tighter tolerances increase cost), and Statistical tolerance analysis for assemblies. Key concepts: Datums establish reference frames, Feature control frames specify requirements, MMC/LMC bonus tolerances enable functional designs. Manufacturing engineers must interpret GD&T to select appropriate processes and develop inspection plans.

Friction Stir Welding (FSW) is a solid-state joining process where a rotating tool with a pin and shoulder plunges into the joint and traverses along, generating frictional heat to plasticize and join materials without melting. Advantages: Excellent joint properties (often stronger than parent material), No filler metal required, Low distortion and residual stresses, Suitable for difficult-to-weld aluminum alloys (2xxx, 7xxx series), Environmentally friendly (no fumes/spatter), and Good for long welds. Widely used in aerospace (aircraft fuselages), automotive (aluminum structures), and shipbuilding.

DfAM considerations include: Orientation optimization (affects surface finish, support needs, build time), Support structure minimization (design self-supporting angles >45 degrees), Wall thickness constraints (minimum depends on process, typically 0.4-1mm), Feature size limits (holes, gaps, fine details), Lattice structures and topology optimization for lightweighting, Part consolidation opportunities, Material-specific considerations (anisotropy, porosity), and Post-processing requirements (support removal, heat treatment, surface finishing). Effective DfAM enables parts impossible with traditional manufacturing while avoiding common AM pitfalls.

Tool wear mechanisms include: Abrasive wear (hard particles in workpiece scratch tool surface), Adhesive wear (material transfers and breaks away, forming BUE), Diffusion wear (chemical diffusion at high temperatures in carbide tools), Oxidation wear (chemical reaction at cutting edge), Fatigue wear (repeated stress cycles causing micro-cracks), and Chipping/fracture (mechanical overload). Crater wear occurs on rake face, flank wear on clearance face. Monitoring wear through surface finish, cutting forces, and power consumption helps predict tool life and optimize replacement intervals using Taylor's tool life equation.

Effective gating system design includes: Pouring basin (controls metal entry, prevents dross), Sprue (vertical channel, tapered to prevent aspiration), Sprue base well (reduces turbulence), Runners (horizontal channels distributing metal), Ingates (connect runners to mold cavity), Risers (feed shrinkage, blind or open), and Chills (control solidification direction). Design principles: Fill mold quickly but avoid turbulence, achieve directional solidification toward risers, filter slag/dross, minimize metal velocity at gates (<0.5 m/s). Simulation software (MAGMA, ProCAST) validates designs before production.

Industry 4.0 technologies transforming manufacturing include: IoT sensors for real-time machine monitoring, Digital twins for process simulation and optimization, Predictive maintenance using ML algorithms, AI-based quality inspection (computer vision), Autonomous mobile robots (AMR) for material handling, Cloud-based MES for production tracking, Augmented reality for operator guidance and training, and Blockchain for supply chain traceability. Benefits include reduced downtime, improved OEE, better quality, and faster time-to-market. Implementation requires infrastructure investment, workforce upskilling, and cybersecurity measures.

Key laser cutting parameters include: Laser power (determines cutting capacity, typically 1-6kW for CO2/fiber lasers), Cutting speed (affects edge quality and productivity), Focus position (surface, above, or below for different effects), Assist gas type and pressure (oxygen for carbon steel, nitrogen for stainless/aluminum), Nozzle standoff distance, and Beam quality (BPP, M-squared). For thick materials: lower speed, higher power. For thin materials: high speed prevents heat damage. Fiber lasers are more efficient for thin sheets and reflective metals; CO2 lasers better for thick non-metals.

Springback occurs when elastically strained material partially recovers after forming, causing the bend angle to open up. Factors affecting springback: Material yield strength (higher strength = more springback), Bend radius to thickness ratio (larger R/t = more springback), Elastic modulus, and Sheet thickness. Compensation methods: Overbending (bend past target angle), Bottoming/coining (plastic deformation at bend), Stretch bending (apply tension during bending), Optimized tooling design, and Simulation-based prediction. Springback can be 2-10 degrees depending on material; AHSS and aluminum alloys show significant springback requiring careful compensation.

Casting simulation (using software like MAGMA, ProCAST, or FLOW-3D) involves: Thermal analysis (predicting solidification sequence, hot spots, shrinkage porosity locations), Flow analysis (mold filling, air entrapment, turbulence), Stress analysis (residual stress, hot tearing risk), and Microstructure prediction (grain size, phases). Optimization process: Validate baseline model against actual castings, run DOE on parameters (pouring temperature, gating design, chill placement), evaluate Niyama criterion for shrinkage prediction, optimize feeding paths for directional solidification. Simulation reduces trial iterations, die costs, and scrap rates by 40-60% when properly implemented.

5-axis challenges include: Collision avoidance (tool holder, spindle, fixtures - use simulation and machine modeling), Kinematic singularities (axis alignment causing erratic motion - avoid through tool path planning), Post-processor accuracy (machine-specific kinematics - validate with dry runs), Surface quality at steep areas (tool engagement variation - optimize lead/lag angles), Work coordinate systems (multiple setups, complex transformations - use RTCP/TCPC), and Thermal effects (long cycles - compensate or manage). Solutions: Advanced CAM software with machine simulation, proper tooling selection (tapered tools, lollipop cutters), optimized cutting strategies (swarf, multi-axis contouring), and skilled programmer training.

Weld distortion prediction and control involves: Analytical methods (shrinkage force approach, inherent strain method), FEA simulation (thermo-mechanical analysis in SYSWELD, ABAQUS), and Empirical correlations. Control strategies: Design phase (balanced welds, neutral axis placement, weld sizing), Sequence optimization (backstep, skip welding, symmetric sequences), Fixturing and restraint (careful - may cause residual stress), Pre-setting and pre-cambering, Thermal management (preheat, interpass control), Low heat input processes (laser, electron beam), and Post-weld correction (mechanical straightening, stress relief). For critical structures like ship hulls or aerospace frames, combine simulation-based prediction with proven mitigation strategies.

Precision forging process design involves: Material selection and billet design (accounting for forging ratio, grain flow requirements), Preform sequence design (blocker, finisher progression using simulation), Die design (optimized flash design, parting line, draft angles, die life considerations), Process parameter optimization (temperature windows, strain rate control, press tonnage), Lubrication strategy (graphite-based for hot forging, proper application method), Die preheating (isothermal or near-net-shape forging), Quality requirements (grain size ASTM 5-8, mechanical properties, ultrasonic inspection), and Tool steel selection (H13 for hot, D2 for cold). Use FEM simulation (DEFORM, FORGE) to validate metal flow, filling, die stress, and microstructure evolution before tool manufacturing.

Metal AM qualification involves: Machine qualification (IQ/OQ - installation and operational verification, beam characterization), Material qualification (powder certification, recycling procedures, chemistry control), Process parameter development (DOE for power, speed, hatch, layer thickness to optimize density >99.5%), Specimen testing (tensile, fatigue, creep at various orientations - account for anisotropy), NDE protocol development (CT scanning, surface inspection), Dimensional capability studies (accuracy, repeatability), Microstructure validation (grain structure, phase distribution), Heat treatment optimization, and Documentation for regulatory compliance (AS9100, NADCAP for aerospace). Build statistical database through coupon testing alongside production parts. Process monitoring (melt pool, thermal imaging) provides in-situ quality data.

Structural HPDC optimization for components like shock towers requires: Alloy selection (high-integrity alloys like Silafont-36, Aural-2 with low Fe, modified Si), Vacuum/semi-solid processing (reduce porosity to <1% for T7 heat treatment capability), Die thermal management (conformal cooling channels, spot cooling), Gate and runner optimization (controlled fill, minimize air entrapment - target fill time 20-50ms), Real-time process monitoring (cavity pressure, temperature), Shot profile optimization (slow shot, fast shot velocities), Squeeze pin placement for thick sections, and Heat treatment protocol (T7 for ductility). Achieve yield strength >120 MPa, elongation >10% required for crash-relevant parts. Validate with CT scanning, mechanical testing, and crash simulation correlation.

Chatter analysis and prevention involves: Stability lobe diagram generation (tap testing for tool-holder-spindle FRF, calculate using analytical or time-domain methods), Spindle speed selection (operate in stable lobes at optimal chip load), Tool selection (shorter tools, high damping holders like hydraulic/shrink-fit), Process parameter optimization (axial/radial depth combinations for stability), Workpiece fixturing (maximize rigidity, dampen resonances), Variable helix/pitch tools (disrupt regenerative effect), Active damping systems (spindle-integrated or tool-based), and Real-time monitoring (accelerometers, microphones) with adaptive control. For thin-walled aerospace parts, use material removal simulation to predict dynamic response changes during machining and adjust parameters accordingly.

Dissimilar welding challenges include: Difference in melting points (controls dilution and joint geometry), Thermal expansion mismatch (causes residual stress and cracking), Intermetallic compound formation (brittle phases at interface - Fe-Al, Ti-Al systems), Galvanic corrosion potential, and Carbon migration in ferritic-austenitic joints. Solutions: Filler metal selection (nickel-based for many combinations, butter layers), Process selection (low heat input - laser, electron beam, FSW), Joint design optimization (transition pieces, interlayers), Controlled dilution through weld parameters, Post-weld heat treatment (stress relief, diffusion control), and Coating/cladding alternatives. Examples: Steel to aluminum requires mechanical joining or explosive welding; stainless to carbon steel uses ENiCrFe-3 filler.

Progressive die design involves: Part analysis (formability using FLD, blank development, strip layout optimization - target >80% material utilization), Station sequence planning (pilot holes, blanking, forming, piercing, trimming in optimal order), Strip design (carrier design, pitch determination, progression), Die construction (die set selection, insert design, guided stripping, quick-change features), Simulation validation (AutoForm, PAM-STAMP for forming, springback, process robustness), Tool steel selection (D2, DC53 for wear resistance; A2 for toughness), Sensor integration (tonnage monitors, misfeed detection), and Design for manufacturability (accessibility for maintenance, insert replacement). Target production rates of 20-60 SPM with die life >500,000 strokes. Include FMEA for potential failure modes.

Comprehensive capability study involves: Measurement system analysis (Gage R&R - target <10% of tolerance), Data collection strategy (minimum 50 parts over time representing normal variation, subgroups for within/between variation), Normality assessment (histogram, normal probability plot, Anderson-Darling test), Control chart analysis (verify statistical control before capability calculation), Capability indices calculation (Cp, Cpk for short-term; Pp, Ppk for long-term), Non-normal data handling (transform or use non-normal methods if needed), Confidence intervals for indices, and Process improvement if Cpk <1.33. For Six Sigma target Cpk >1.67. Report includes histogram with spec limits, control charts, capability summary, and improvement recommendations. Common in automotive (PPAP), aerospace (FAI), and medical device (process validation) industries.

Topology optimization integration workflow: Define design space, loads, constraints, and objectives in FEA software (Altair OptiStruct, ANSYS, nTopology), Apply AM-specific constraints (overhang angles, minimum feature size, build direction), Run optimization (compliance minimization, stress constraints, modal targets), Interpret and reconstruct geometry (smoothing, feature recognition), Validate optimized design (FEA verification, safety factors), Apply DfAM refinements (self-supporting transitions, powder removal features), Generate build file and support strategy, Process simulation for distortion prediction, Post-processing planning (machining datum surfaces, heat treatment), and Testing (mechanical qualification, NDT). Examples: GE LEAP fuel nozzle (20 parts to 1, 25% lighter), Airbus partition (45% lighter than machined). Balance optimization freedom with manufacturing reality.

Strategies for difficult materials include: Tool material selection (ceramic, PCBN for Inconel; uncoated carbide, PCD for Ti), Tool geometry (positive rake, sharp edges, proper chip breakers), Cutting parameters (low speed for Ti to prevent ignition; higher speed for Inconel with ceramic), Cooling strategies (high-pressure coolant 70+ bar, cryogenic for Ti, minimum quantity lubrication alternatives), Machining approach (climb milling, constant chip load, avoid dwelling), Tool path optimization (trochoidal for slotting, arc entry/exit), Wear monitoring (rapid notch wear in Ti, depth-of-cut notch in Inconel), and Fixturing (rigid setup, damping for thin walls). Key challenges: Low thermal conductivity causes heat concentration, work hardening in Inconel, reactivity and galling in Ti. Consider hybrid processes (LAM - laser-assisted machining) for extreme cases.

Digital twin implementation involves: Physical asset modeling (3D CAD, kinematic models), Sensor integration (vibration, temperature, power, vision systems - edge computing for real-time data), Data infrastructure (time-series database, cloud platform like Azure IoT, AWS), Physics-based models (FEA, CFD, process models), Machine learning integration (anomaly detection, predictive models trained on historical data), Real-time synchronization (bidirectional data flow, latency management), Visualization and analytics (3D real-time visualization, dashboards), and Closed-loop control (parameter optimization, adaptive control). Applications: Predictive maintenance (remaining useful life prediction), Process optimization (real-time parameter adjustment), Quality prediction (correlate process data to outcomes), and Virtual commissioning. ROI typically 10-25% efficiency gain. Requires cross-functional team (OT, IT, data science, domain experts).

Robotic welding cell design involves: Process requirements analysis (weld types, cycle time, quality targets), Robot selection (payload for torch and wire feeder, reach, axes configuration), Welding equipment integration (power source communication, wire feed system, torch maintenance), Positioner design (2-axis, headstock-tailstock for optimal weld positions), Fixture design (locating accuracy, clamping, anti-spatter), Safety system design (light curtains, interlocks per ISO 10218), Offline programming and simulation (robot path, collision avoidance, cycle time), Weld parameter database (WPS for different joints/materials), Seam tracking implementation (through-arc or vision-based for variation), Quality monitoring integration (arc data, vision inspection), and Cell controller/HMI development. Target OEE >85%, weld quality Cpk >1.33. Include error recovery procedures and operator training.

Manufacturing cost optimization approach: DFM/DFA analysis during design (reduce part count, simplify assembly, standardize fasteners), Make vs buy analysis (core competency, volume, capital requirements), Process selection optimization (compare casting vs forging vs machining based on volume and geometry), Material cost optimization (specification review, alternative materials, near-net-shape), Tolerance optimization (cost-tolerance studies, reduce tight tolerances where non-critical), Cycle time reduction (lean principles, automation analysis, setup time reduction), Supply chain optimization (supplier consolidation, regional sourcing, logistics), Quality cost analysis (prevention vs appraisal vs failure costs), Capital efficiency (equipment utilization, flexibility for volume changes), and Total cost of ownership modeling. Target 20-40% cost reduction through systematic analysis. Use cross-functional team including design, manufacturing, supply chain, and quality.