Katmanlı Üretim Tasarım Optimizasyonu için Üretim Farkındalığına Yönelik Kıyaslama

Öz

Bu çalışma, mevcut algoritma değerlendirme çerçevelerindeki önemli sınırlamaları ele alan, katmanlı imalat (AM) tasarım optimizasyonu için kapsamlı ve imalat odaklı bir kıyaslama sunmaktadır. Geleneksel kıyaslamalar (benchmarks) genellikle çıkıntı sınırlamaları, özellik çözünürlüğü, destek yapıları ve yapı oryantasyon etkileri gibi AM'ye özgü kısıtlamaları göz ardı eder. Bunu ele almak için, gerçekçi üretilebilirlik kısıtlamaları rehberliğinde, yedi tasarım değişkeni ve beş çelişkili hedef (malzeme kullanımı, yapı süresi, yapısal verimlilik, destek hacmi ve yüzey kalitesi) içeren, FDM süreçleri için tasarlanmış çok amaçlı bir kıyaslama öneriyoruz. Python ile geliştirilen kıyaslama, 30 bağımsız çalışmada beş optimizasyon algoritması ile test edildi. Hibrit algoritma, diferansiyel evrim ve parçacık sürüsü, kabul edilebilir fizibilite (96%) ve düşük değişkenlik ile tutarlı bir şekilde en yüksek hiper hacim puanlarını (0,230, 0,237, 0,237) elde etti. Buna karşılık, simulated annealing yüksek değişkenlik (CV = 36,9%) ve düşük ortalama performans (1,335) sergiledi. Duyarlılık analizi, dolgu yoğunluğunun, yapım süresi üzerinde r = 0,91 korelasyon katsayısı ile en etkili parametre olduğunu ortaya koydu. Bu sonuçlar, benchmark'ın algoritma performansını etkili bir şekilde ayırt etme yeteneğini ve üretim odaklı ortamlarda araştırma ve tasarım optimizasyonu için değerini göstermektedir.

Anahtar Kelimeler

• Katmanlı Üretim
• Çok Amaçlı Optimizasyon
• AM Karşılaştırması
• Üretim Kısıtlamaları

Manufacturing-Aware Benchmark for Additive Manufacturing Design Optimization

Öz

Additive manufacturing (AM) allows for complex, lightweight designs but introduces process-specific constraints that traditional benchmarks often overlook. We introduce a manufacturing-aware benchmark designed for PLA/FDM that includes nine design variables covering geometry, process, and orientation; six competing objectives (material weight, build time, structural efficiency, support volume, surface quality, and total cost); and 20 physics-based manufacturability constraints grouped into eight categories. The framework combines feasible-set exploration using Latin Hypercube Sampling (LHS) (n=10,000), multi-method sensitivity analysis, sample-based Pareto front extraction, and standardized algorithm evaluation under equal budgets for differential evolution (DE), particle swarm optimization (PSO), Bayesian optimization (BO), simulated annealing (SA), and a hybrid algorithm (HA). Statistical comparisons employ nonparametric tests and effect sizes, while multi-objective performance is analyzed using dominance relations and hypervolume. Sample-based feasibility is roughly evenly split (49.8% feasible), with violations mainly caused by print speed and thermal/support limits; print speed, support density, and build orientation are the main factors affecting feasibility and performance; build time and cost are nearly collinear; and improving surface quality generally requires slower speeds and thinner layers. Feasibility-based Pareto analysis shows significant reductions in weight and support volume while maintaining strength, revealing clear trade-offs and knee solutions. Boundary checks show 96.7% interior solutions, indicating the problem is not boundary-driven, and the few layer-height violations are minor and correctable. Algorithmically, scalarized results favor the hybrid approach (with PSO ≈ DE), while multi-objective indicators highlight complementary strengths in front coverage. Performance differences are highly significant (Kruskal–Wallis H = 179.23, p < 0,001; η² = 0.717; ω² = 0.711); the hybrid achieved the best scalarized outcome with tight uncertainty (95% CI [−4.9163, −4.7979], CV = 4.48%), and BO attained the highest mean hypervolume (0.847). The benchmark offers a reproducible, realistic testbed that bridges algorithmic evaluation with manufacturing feasibility and can be expanded with higher-fidelity models and materials.

Anahtar Kelimeler

• Additive Manufacturing
• Multi-Objective Optimization
• AM Benchmarking
• Manufacturing Constraints

Kaynakça

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