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Material cost reduction using cutting optimization Pro5 and enhanced N-BEATS deep learning forecasting: a case study
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Manufacturing industry is constantly looking for ways to improve the efficiency of production processes so as to lower production costs, maintain product quality, and improve production planning. This study aims to provide an integrated optimization and forecasting framework that combines Cutting Optimization Pro5, linear programming, and an Enhanced N-BEATS deep learning model to minimize material waste and manufacturing cost and to predict the number of production items with a price-reduction strategy. The real industrial case study is based on production and pricing data from the Akad factory of the General Company for Electrical and Electronic Products in Iraq for the period from 2014 to 2025, concerning the manufacture of 80 L and 120 L electric water heaters. That is, Cutting Optimization Pro5 was used to specify optimal sheet sizes and cutting plans, minimizing waste and reducing production costs. At the same time, the Enhanced N-BEATS deep learning model was implemented to forecast future production quantities. The optimization results showed a significant reduction in wasted materials and production costs, allowing lower product prices. Thus, the forecasting results proved that the performance of the Enhanced N-BEATS model was much better than that of SARIMAX and Simple Linear Regression since the model showed the lowest prediction errors (pMAPE value for the 120 L product is equal to 0.040 and nMSE is equal to 0.016, while pMAPE for the 80 L product equals 0.091, and nMSE equals 0.007). The results show that combining cutting optimization and deep learning can create an efficient decision-support framework to improve production efficiency, lower production costs, and enhance production planning in industrial environments.

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Publication Date
Tue Dec 05 2017
Journal Name
International Journal Of Science And Research (ijsr)
Multi Response Optimization of Submerged Arc Welding Using Taguchi Fuzzy Logic Based on Utility Theory
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Publication Date
Sun Oct 01 2017
Journal Name
Journal Of Engineering
Optimization of Surface Roughness for Al-alloy in Electro-chemical Machining (ECM) using Taguchi Method
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Electro-chemical Machining is  significant  process to remove metal with using  anodic dissolution. Electro-chemical machining use to removed metal workpiece from (7025) aluminum alloy using Potassium chloride (KCl) solution .The tool used was made from copper. In this present the optimize processes input parameter use are( current, gap and electrolyte concentration) and surface roughness (Ra) as output .The experiments on electro-chemical machining with use current (30, 50, 70)A, gap (1.00, 1.25, 1.50) mm and electrolyte concentration (100, 200, 300) (g/L).  The method (ANOVA) was used to limited the large influence factors affected on surface roughness and found the current was the large influence f

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Publication Date
Mon Jan 01 2018
Journal Name
Biochemical And Cellular Archives
Optimization of enzymatic conversion of waste office paper using response surface methodology for bioethanol production
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Publication Date
Mon Feb 01 2021
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Evaluation of the stability enhancement of the conventional sliding mode controller using whale optimization algorithm
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The proposed work is an attempt to investigate the stability of the nonlinear system by using a whale optimization algorithm as of one of the meta-heuristic optimization methods, and this investigation was conducted on a single inverted pendulum as a study model. The evaluation measures which were used in this article values of gain and sliding surface of the conventional sliding mode controller to illustrate the extent of the system`s stability. Furthermore, control action, the relationship between error and its derivative, desired, and actual position in addition to sliding response graphically showed the feasibility of the proposed solution. The attained results illustrated considerable improvement in the settling time and minimizing the

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Publication Date
Thu Oct 01 2020
Journal Name
Alexandria Engineering Journal
Biodegradation of reactive dyes by some bacteria using response surface methodology as an optimization technique
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Water pollution as a result of contamination with dye-contaminating effluents is a severe issue for water reservoirs, which instigated the study of biodegradation of Reactive Red 195 and Reactive Blue dyes by E. coli and Bacillus sp. The effects of occupation time, solution pH, initial dyes concentrations, biomass loading, and temperature were investigated via batch-system experiments by using the Design of Experiment (DOE) for 2 levels and 5 factors response surface methodology (RSM). The operational conditions used for these factors were optimized using quadratic techniques by reducing the number of experiments. The results revealed that the two types of bacteria had a powerful effect on biodegradable dyes. The regression analysis reveale

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Publication Date
Sun Apr 29 2012
Journal Name
Journal Of Karbala University
Optimization Quantitative Determination of Cimetidine in Pharmaceutical Preparations via Bromothymol Blue Using Central Composite Design
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Publication Date
Wed Dec 25 2019
Journal Name
Journal Of Engineering
Corrosion Rate Optimization of Mild-Steel under Different Cooling Tower Working Parameters Using Taguchi Design
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This study investigates the implementation of Taguchi design in the estimation of minimum corrosion rate of mild-steel in cooling tower that uses saline solution of different concentration. The experiments were set on the basis of Taguchi’s L16 orthogonal array. The runs were carried out under different condition such as inlet concentration of saline solution, temperature, and flowrate. The Signal-to- Noise ratio and ANOVA analysis were used to define the impact of cooling tower working conditions on the corrosion rate. A regression had been modelled and optimized to identify the optimum level for the working parameters that had been founded to be 13%NaCl, 35ᴼC, and 1 l/min. Also a confirmation run to establish the p

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Publication Date
Mon Oct 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
Robot Path Planning in Unknown Environments with Multi-Objectives Using an Improved COOT Optimization Algorithm
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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Geological Journal
Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data
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Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

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Publication Date
Sat Apr 19 2025
Journal Name
Plos One
Early Detection of Autism Spectrum Disorder in Children Using Different Machine Learning Algorithms
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Abstract<p>Autism spectrum disorder(ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in individuals. Global health organization facing difficulties in establishing an effective ASD diagnostic system that facilitates precise analysis and early autism prediction. It is a scientific issue that necessitates resolution. This research presents an approach for the early prediction of children with ASD utilizing significant variables through machine learning (ML) methods. Three stages comprise the suggested technique. First, a 1250-case ASD dataset was identified and preprocessed. Five extremely effective traits with high Pearson c</p> ... Show More
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