AI-powered Statistical Process Control, MSA, and Process Capability analysis. Upload data, ask in plain English, get charts and reports.
Process Analytics & Six Sigma in Python — integrated workflows for capability analysis, root cause analysis, and statistical process control.
25 hands-on WSQ Certified Lean Six Sigma Green Belt labs across the full DMAIC roadmap — project charter, VOC/CTQ, Kano, SIPOC, value stream mapping, sampling and sample size, MSA/Gage R&R, DPMO and process capability (Cp/Cpk), Pareto and run charts, hypothesis testing, correlation and regression, FMEA, DOE and SPC control plans. 4-day course.
Process behavior charts (control charts / SPC) for Python — XmR, Xbar-S, histograms, capability, Taguchi loss. Implements Bishop's Variance Analysis System with chart math validated against published reference results.
8 hands-on Certified Lean Six Sigma Black Belt labs covering DMAIC, project charter, VOC, SIPOC, MSA, capability, hypothesis testing, regression, DOE, FMEA, SPC, control plans, benefits realization, and exam review.
COMSOL 6.4 model of an APCVD poly-Si reactor on a 300 mm wafer | inlet redesign cuts non-uniformity 180% to 3.3% | 14-run CCD, Gaussian-process surrogate, Monte Carlo Cpk, 3D asymmetric exhaust
WSQ courseware for Statistical Process Control (SPC) in Manufacturing (TGS-2026064862): 86-slide deck, Lesson Plan, Learner Guide, 12 hands-on activities and Excel worksheets covering control charts, Cp/Cpk capability and out-of-control root cause analysis. 1-day course by Tertiary Infotech Academy.
Physics-based cosmetic finish process-capability simulator for anodizing and PVD thin-film coatings
Simulated cover glass manufacturing quality analytics project for MQE portfolio
Talaşlı imalat ve CNC işleme merkezleri için hepsi-bir-arada mühendislik istasyonu: SPC (Cp, Cpk), ISO 286 Tolerans & Geçme, G-Kod Simülatörü & Strok Denetimi, Kesme Gücü, Yüzey Pürüzlülüğü (Ra), Parça Maliyeti ve Kurumsal PDF Raporlama.
Thermal oxidation of silicon in Python: Deal–Grove kinetics with Massoud thin-oxide corrections, furnace recipe integration, Monte Carlo process capability, and oxidation-enhanced diffusion.
Python-based SPC and process capability analysis for simulated manufacturing quality data.
Automated Process Capability Analysis (SPC) tool for massive production datasets. Supports Normal and Non-Normal distributions, Box-Cox/Johnson transformations, and automated subgroup reporting. Built as a high-performance alternative to manual statistical software.
Python toolkit for Cp/Cpk, PFMEA, OEE, Gauge R&R, SPC, process data, and manufacturing improvement.
A statistical tool modeled to measure process capability and estimate production yield
Automated characterization test rig demonstrating design-of-experiments: sweeps a device under test across its envelope, models the instrument, computes Cpk/yield, and shows a failure corner that one-factor-at-a-time testing misses but full-factorial DOE catches. Live shmoo explorer included.
DQIP — Digital Quality Intelligence Platform for cross-domain, standards-aware quality analytics, Six Sigma capability, risk intelligence, corrective-action guidance, and executive reporting.
Reproducible industrial quality-inspection system using OpenCV for defect detection, dimensional measurement, false accept/reject analysis, SPC, and Cp/Cpk evaluation.
Real-time manufacturing quality prioritization system combining SPC, predictive analytics and Power BI for industrial decision support.
Practical statistical and quality engineering tools for process capability analysis, manufacturing data, and engineering decision-making.
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