Scrap Costs Research Articles

Optimal Multiple Wind Power Transmission Schemes Based on a Life Cycle Cost Analysis Model

Due to the high cost and complex challenges faced by offshore wind power transmission, economic research into offshore wind power transmission can provide a scientific basis for optimal decision-making on offshore wind power projects. Based on the analysis of the topology structure and characteristics of typical wind power transmission schemes, this paper compares the economic benefits of five different transmission schemes with a 3.6 GW sizeable onshore wind farm as the primary case. Research includes traditional high voltage alternating current (HVAC), voltage source converter high voltage direct current transmission (VSC-HVDC), a fractional frequency transmission system (FFTS), and two hybrid DC (MMC-LCC and DR-MMC) transmission scenarios. The entire life cycle cost analysis model (LCCA) is employed to thoroughly assess the cumulative impact of initial investment costs, operational expenses, and eventual scrap costs on top of the overall transmission scheme’s total cost. This comprehensive evaluation ensures a nuanced understanding of the financial implications across the project’s entire lifespan. In this example, HVAC has an economic advantage over VSC-HVDC in the transmission distance range of 78 km, and the financial range of a FFTS is 78–117 km. DR-MMC is better than the flexible DC delivery scheme in terms of transmission capacity, scalability, and offshore working platform construction costs in the DC delivery scheme. Therefore, the hybrid DC delivery scheme of offshore wind power composed of multi-type converters has excellent application prospects.

A comparative study on the effectiveness of pollutants control measures adopted in the steel industry to reduce workplace and environmental exposure: a case study

Our understanding of the environmental and occupational health implications of pollutants emitted in steel production is still lacking, despite the considerable amount of research devoted to this topic. Given the significance of steel recycling and the need to reduce greenhouse gas emissions, many steel factories are adopting electric arc furnace (EAF) technology. The use of a technological system designed for the capture of pollutants emitted through EAF steel production is highly ecological because of its utilization of iron scrap and low investment cost. Despite this, the main issue with the EAF is the environmental impact it poses, specifically the release of pollutants into the air, such as dust and organic substances, chlorinated dioxins and furans, dioxin-like polychlorinated biphenyls and brominated dioxins and furans. As a result, workers in this field have a considerable rate of morbidity. The main challenge for EAFs is to optimize the capture of powders produced during the techno-logical process, both from the EAF and the workplace. A state-of-the art solution for managing pollutants in modern steel manufacturing is highlighted in this paper, featuring a method used in Romania that employs the Best Available Techniques (BAT) reference document for iron and steel production to directly collect pollutants from the EAF. The system included a cylindrical fitting, a heat exchanger to cool the gases and a hood to collect contaminants. In comparison to other ventilation options, this equipment boasts lower investment and lower operational costs because of its effective and minimal air flow. Through the use of cutting-edge technology and progressive strategies, we can move closer toward our objective of a workplace free from injuries in the steel industry.

Exploring the Frontiers of Non-Destructive Techniques: Shearography, Ultrasound Laser and Thermography

Non-destructive testing (NDT) techniques serve as indispensable tools across diverse industries, facilitating material and component inspection without inflicting damage. This article underscores the paramount importance of NDT methods in enhancing product quality, safety, and cost-effectiveness. Leveraging advanced techniques such as shearography, ultrasonic laser, and thermography, manufacturers can proactively identify defects during the production process, thereby reducing scrap and rework costs while enhancing overall product integrity. Shearography excels in surface defect detection, while ultrasonic laser offers sensitivity without necessitating direct contact, and thermography enables versatile temperature-based inspection. The synergistic coexistence of these techniques within NDT protocols maximizes inspection efficacy, ensuring high-quality and safe outcomes across various industries. By embracing a comprehensive approach to NDT, industries can optimize operational efficiency, minimize risks, and uphold stringent quality standards. In conclusion, the integration of shearography, ultrasonic laser, and thermography in NDT practices represents a cornerstone in maximizing inspection effectiveness and maintaining superior quality end safety standards across industrial sectors.

Technology portfolio assessment for near-zero emission iron and steel industry in China

China aims to peak CO2 emissions before 2030 and to achieve carbon neutrality before 2060; hence, industrial sectors in China are keen to figure out appropriate pathways to support the national target of carbon neutrality. The objective of this study is to explore near-zero emission pathways for the steel industry of China through a detailed technology assessment. The innovative technology development has been simulated using the AIM-China/steel model, developed by including material-based technologies and optimal cost analysis. Six scenarios have been given in terms of different levels of production output, emission reduction and carbon tax. Near-zero emission and carbon tax scenarios have shown that China's steel industry can achieve near-zero emission using electric furnaces and hydrogen-based direct reduction iron technologies with policy support. Based on these technologies, minimised production costs have been calculated, revealing that the steel produced by these technologies is cost-effective. Moreover, the feedstock cost can play a key role in these technology portfolios, especially the cost of scrap, iron ore and hydrogen. In addition, the feedstock supply can have strong regional effects and can subsequently impact the allocation of steelmaking in the future. Therefore, China can achieve near-zero emissions in the steel industry, and electric furnace and hydrogen-based direct reduction iron technologies are crucial to achieving them.

Development and implementation of in-line segmentation for continuous electrode production in lithium-ion battery cell manufacturing for traceability applications

One of the biggest challenges in battery cell production is reducing manufacturing costs. Production errors and manufacturing inaccuracies due to a complex process chain and cost-intensive material flows lead to high scrap rates and costs. In order to detect and eliminate production errors early in the manufacturing process, traceability approaches have been increasingly referred to in the literature. Here, process and product parameters are already assigned to the individual intermediate products and used to evaluate whether further processing is possible. When transferring traceability concepts to battery cell production, the challenge is to identify individual sections in the continuous processes of electrode production. These process steps have a decisive influence on the later electrochemical performance of the battery cells and have so far only been performed in batches to reduce possible defects. This paper presents two approaches to segmenting the produced electrodes in-line in the coating process to provide discrete sections for tracking and tracing applications. Production data are allocated to single electrodes instead of the entire electrode coil. Laser and ink marking systems are used to apply unique markings that are subsequently read out for product and process data assignment. Appropriate integration requirements must be met for both marking systems to apply readable markings. The requirements are derived for both marking systems from the respective mode of operation and compared with each other. It is shown that laser systems are more technically challenging and cost-intensive to integrate than ink systems but offer greater geometrical flexibility. Furthermore, it is shown how both marking technologies are integrated into the respective coating lines for lithium-ion battery electrodes within the pilot production lines at two research facilities. The traceability of product data to individual electrode sections is demonstrated using in-line and hand-measured electrode areal mass loading data.

Human-centric zero-defect manufacturing: State-of-the-art review, perspectives, and challenges

Zero defect manufacturing (ZDM) aims at eliminating defects throughout the value stream as well as the cost of rework and scrap. The ambitious goal of zero defects requires the extensive utilization of emerging technologies. Amidst the major drive for technological advancement, humans are often kept out of the loop because they are perceived as the root cause of error. The report from the European Commission on Industry 5.0 emphasizes that human-centric is a key pillar in building a more resilient industry and is vital to incorporate the human component into the manufacturing sector. However, we did not find any publications that explain what human-centric ZDM is, nor what the roles of humans are in advancing ZDM. As a contribution to bridging this gap, a systematic literature review is conducted using different databases. We collected 36 publications and categorised them into 3 different human roles which are managers, engineers, and operators. From our search, we found out that managers play a vital role in cultivating ZDM in the entire organization to prevent errors despite the fact they often do not have direct contact with the production line as operators. Operators can help advance ZDM through knowledge capturing with feedback functions to the engineer to better design a corrective action to prevent errors. Assistive technologies such as extended reality are efficient tools used by operators to eliminate human errors in production environments. Human-centric is now a goal in the future manufacturing sector, but it could face barriers such as high technological investments and resistance to changes in their work tasks. This paper can contribute to paving the roadmap of human-centric ZDM to bring defects to zero and reposition the manufacturing sector to become more resilient.

Forecasting of production and scrap amounts using artificial neural networks

The increase in consumer needs and the scarcity of production resources cause the concept of ‘productivity’ to be essential for companies. Reducing costs is an essential factor for increasing competitiveness, and therefore, businesses are taking action to reduce scrap costs and increase efficiency. Since the increase in scrap will reduce productivity, it may cause production delays and thus customer dissatisfaction. In this study, the slitting line of one of the essential Japanese supplier companies operating in the automotive sector in Turkey is discussed. The proposed model aims to predict the amount of production and scrap that may occur to increase productivity in the slitting line by using an artificial neural network (ANN) and increasing the efficiency of the slitting line with the measures to be taken. In this context, different ANN designs were made for production and scrap. During the execution of the ANN models, the production and scrap amounts were forecasted at 99 and 85%, respectively. While measuring the successful performance of the ANN models, root mean squared error, mean absolute percent error and R 2 indicators were used; the forecasted values produced by the ANNs that were successful in terms of performance indicators were compared with the actual values, and the reliability of the study was increased.

A data analytics model for improving process control in flexible manufacturing cells

With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology based on data analytics that simplifies the monitoring process for the operator, allowing the practitioner to concentrate on the relevant issues, anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine’s settings are needed (tool replacement, repositioning the axis, etc.). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.

Kai and Zen: Effective Tools for Improving Productivity and Quality: A Case Study of an Indian MSME

Old manufacturing methods have become obsolete and are ineffective in the current era of highly competitive global markets. The customer expectations have seen a dramatic shift in tastes and preferences due to large product availability that keeps on improving with time. Nowadays companies are forced to adopt new manufacturing methods that facilitate growth, notably lean manufacturing tools, and related techniques have seen a positive acceptance worldwide. Furthermore, kaizen is one of the effective tools that are based on the principle of the right use of workers’ potential and capabilities. The present case study is done in an industry engaged in manufacturing child parts which are used in gearbox assembly, transmission, hydraulic lift assembly, etc. for original equipment manufacturers. Henceforth, these issues formed the basis for the current study. Furthermore, a road map was developed that was based on understanding the current manufacturing process, thorough analysis of records, followed by detailed employee interviews. Moreover, this study used a kaizen approach that involved people from the shop floor that lead to many positive suggestions and practical implementation. Finally, this study successfully improved overall product quality and met customer expectations without any major financial investments. The positive findings of this case study saw tangible gains in the form of Withdrawl Nut P252006 additional rework cost reduced from 5.82% to 0.41% and in-house rejection (scrap) cost reduced from 0.84% to 0.03%. Pre Loading Nut P124034 additional rework cost reduced from 5.97% to 0.34% and in-house rejection (scrap) cost from 0.89% to 0.02%.

Modular cost model for Tolerance allocation, Process selection and Inspection planning

The need for highly reliable and precise products has forced industries to study potential uncertainties during designing needed parts. The reliability and acceptance of the product rely on several factors and tolerancing activity plays an important role to assure that the manufactured product meets the requirements. The importance of tolerancing activity can be noticed once designers prefer tight tolerances to ensure product performance and in contrast manufacturers want loose tolerances to reduce manufacturing and assembly complexity and then cost, to decrease the non-conformance rate. Therefore, tolerance allocation and inspection-planning design can be formalized as an optimization problem which the objective function represents the cost impacted by several aspects of the quality management: cost of failure, cost of the inspection. This paper details a modular cost model which includes four components: the manufacturing cost, the inspection cost, the scrap cost (internal failure), and the cost of external failure. Moreover, to improve the efficiency of the cost model, it integrates several factors such as frequencies of the monitoring and inspection activities, probability of conformed product, probability of non-detection of non-conformity, and probability of non-detection of confirmed. The applications of this model are illustrated and demonstrated through an industrial case study.

An integrated resource allocation and tolerance allocation optimization: A statistical-based dimensional tolerancing

Today's industrial world is facing rising demand for highly reliable and safe products. Complex industries, such as automobiles, medical, and aircraft, require a well-designed engineering plan which has a comprehensive understanding of the various certainties and uncertainties that occur in reality. Consequently, the need for reliable and precise parts has impacted the tolerancing activity. Key functions of complex products can often be realized by high precision part use. Thus, producers are confronted with high-quality requirements, cost pressure, and a rising number of demands. The introduction of new technologies and the need to meet the requirements have broadened the scope of tolerancing. In this paper, a statistical tolerance allocation model is developed to study the economic impact of allocated tolerances on an assembled product. The problem is aimed at optimizing the allocated tolerances to each part of the product while minimizing manufacturing costs. A modular cost model is proposed to determine the manufacturing costs related to each activity and part. The manufacturing costs include processing cost, inspection cost, scrap cost, assembly cost, and warranty cost. Furthermore, a genetic algorithm is adapted to study the applicability of the model developed on an exemplary assembled product.

Optimization Model of Engineering Specifications Based on Grey Quality Gain-Loss Function

In view of the fact that the target values of some quality characteristics are grey, the grey quality gain-loss function model was applied in the analysis of the quality characteristics. At the same time, based on the analysis of engineering specifications and process capability, an optimization model of engineering specifications was proposed to minimize the expected total loss of each product and maximize the expected compensation with inspection costs, scrap costs and grey quality gain-loss into consideration. The optimal engineering specification can be obtained by using the optimization model. Through the example analysis and its application in dam concrete construction, the practicability of the model is verified, which provides an important reference for the research of the new theory of dam concrete construction quality control.

Renewable energy supply chain management with flexibility and automation in a production system

Due to technological development, there has been a rapid increase in energy demand, but the amount of available energy is not infinite. There is a daily increase in the price per unit of energy. At present, renewable energy is the option as a substitute for traditional energy. Based on this premise, this study introduces an energy supply chain management, where renewable energy can be converted from waste. The significant benefit of this system is that the utilization of waste can mitigate environmental pollution and provide a primary renewable energy source. This way, almost all traditional energy utilized places may have the benefit of using the renewable energy with less cost as waste is the raw material. This study introduces the collection of all waste from a city and ways in which the city can generate renewable energy. The waste supplier and energy manufacturer are the two players involved in this energy supply chain. The supplier collects all waste and transfers it to the manufacturer. Based on equal an unequal power of both players, there are two cases: one case for unequal power with their own profit and the other equal power with their joint profit. The Kuhn–Tucker methodology is utilized because of inequality constraint and that the Stackelberg game theoretic approach maximizes the profit of the energy supply chain because the potentiality of the energy supply chain players may not be identical. The comparative study also provides the supply chain players with equal and unequal power. Numerical experiments prove the effectiveness of this energy supply chain. The numerical investigation finds that the profit associated with the coordination policy case is greater than that from the unequal power case. Though the equal and unequal power of supply chain players are considered, 43.05% more profit is obtained for the cooperation policy. Flexibility of production rate is found strongly dependent on the development cost for both game policy and coordination policy. The sensitivity analysis is conducted for the key parameters of the model. Fifty percentage with positive and negative direction of key parameters are verified to check the effectiveness on the profit. It gives energy selling price and scrap cost of manufacturer are the best and least parameters among all parameters.

In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing

Abstract The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated in the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

Qualitative and quantitative assessment of waste generation in a refrigerator-manufacturing plant based on a waste tree and mass balance.

In this study, wastes originating at each production station during refrigerator manufacturing were identified and classified based on a waste tree. A mass balance study revealed a total waste production factor of 0.046 kg/kg of a product of which 75.3%, 23.9%, and 0.8% were non-hazardous wastes (NHWs), packaging wastes (PWs), and hazardous wastes (HWs), respectively. Wastes produced during refrigerator manufacturing were grouped under 35 different waste codes. Waste codes that contributed more than 5% by weight were 15 02 02 (contaminated absorbent material), 15 01 10 (contaminated packaging), 16 02 13 (electronic cards), 07 02 14 (polyol) and 08 05 01 (isocyanates), 19 08 13 (treatment sludge), 16 02 15 (capacitors), and 13 01 13 (hydraulic oil) for HWs, 12 01 01 (ferrous metal), and 16 02 16 (components) for NHWs, and, finally, 15 01 03 (wooden), 15 01 01 (paper&cardboard), and 15 01 02 (plastic) for PWs over 5 years. Scrap costs were used as a surrogate to determine production stages that generated high amounts of metal and plastic wastes. Logarithmically, increasing and decreasing trends were observed for PWs and NHWs over the study period, respectively. HW amounts did not exhibit a statistically significant trend. Twenty-eight BATs (best available techniques) were identified that could be applied in refrigerator manufacturing for waste minimization and management. Among those, 8 of them were proposed for further improvement for waste management in the facility.

Inventory model for sustainable operations of a closed-loop supply chain: Role of a third-party refurbisher

This paper investigates the joint decision on inventory and refurbishing strategies in a closed-loop supply chain, consisting of a manufacturer responsible for the production and first-market sales and a third-party refurbisher responsible for the refurbishment and second market. Based on a game-theoretic model, we investigate the effects of supply chain structures on the production operations, sales price of a refurbished product, profits and environmental performance. Through numerical analyses, we reveal that the integrated system yields higher first-market sales and a larger production lot size, while the decentralized one shows higher second-market sales and a larger refurbishing lot. Interestingly, the integrated system sets the higher price for the refurbished item due to channel conflict issues. Efforts to increase the second-market sales can harm the overall supply chain profit by leading to the decrease in the higher-margin first-market sales. We also show that the difference in pricing decisions and inventory holding cost components allows a better operational performance of the third-party refurbisher's process in the decentralized system. Then, it leads to the better environmental performance in the decentralized system, enhancing the refurbishing rate and sales of refurbished items while reducing scrap costs. By investigating the overall performance of integrated and decentralized supply chains, we offer important implications to practicing supply chain managers by revealing how the third-party refurbisher can help the sustainable operations of a closed-loop supply chain. We also contribute to the body of knowledge by incorporating pricing and lot sizing issues into the context of closed-loop supply chain management.

Model of quality control station allocation with consider work in process, and defect probability of final product

Planning is needed in quality control for the target output produced is good. Quality control in the production line is carried out based on the inspection with acceptance of products that meet the requirements. This quality control method is carried out by allocating the quality control station flexibly to separate the rework products to be reworked and scrap to dispose of. The number of acceptable product proportions will depend on the ability of the production line to produce the product expressed in the probability of the product defect. The probability of a production line defect product will be affected to the probability at each work station, where the proportion of product defects that occur will also determine the allocation of the number of quality control stations. Research that discusses the allocation of quality control stations by considering the probability of the final product on the production line and the proportion of work in process does not yet exist, therefore researchers intend to do so. The development of this model is done by developing an objective function to minimize the total production cost. The total cost consists of processing costs, inspection costs, waiting costs, quality control station assignation costs, rework costs, and scrap costs. The aim is to find the configuration of the quality control station allocation in the production line at a minimum cost. From the results of the study obtained a quality control station allocation model that can minimize assembly costs and be flexible to changes in demand parameters, costs, defect probability of final product.

On the propagation of quality requirements for mechanical assemblies in industrial manufacturing

A frequent challenge encountered by manufacturers of mechanical assemblies consists of the definition of quality criteria for the assembly lines of the subcomponents which are mounted into the final product. The rollout of Industry 4.0 standards paves the way for the usage of data-driven, intelligent approaches towards this goal. In this work, we investigate such a scenario originating in the daily operations of thyssenkrupp Presta AG, where new vibroacoustic quality specifications must be derived for the assembly line producing ball nut assemblies, based on the feedback offered by the vibroacoustic quality test of the steering gear, the final mechanical assembly they are mounted into. We first present a Mixed Integer Linear Programming (MILP) formulation for the problem and show that small instances of the corresponding available dataset can be solved to optimality. Upon ascertainment of the unsuitability of the MILP approach for industrial daily operations due to its long computation time, we propose a heuristic solving approach based on genetic algorithms and measure the performance gap between them in terms of achieved solution quality and computation time. Finally, we additionally propose a greedy heuristic designed to outperform the genetic algorithm in terms of computation time while still featuring a comparable solution quality. The practical relevance of the results is guaranteed, since the best solution reached by the genetic algorithm reduces the scrap costs with respect to the method currently employed by the company by 49.91%.

Multi-Objective Optimization of Electric Arc Furnace Using the Non-Dominated Sorting Genetic Algorithm II

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of the EAF-based steel-making process based on a multi-objective optimisation using evolutionary computing and machine learning. Using a large amount of real-world historical data containing 6423 consecutive EAF heats collected from a melt shop in an established steel plant this work not only creates machine learning models for both EAF and ladle furnaces but also simultaneously minimises the total scrap cost and EAF energy consumption per ton of scrap. In the modelling process, several algorithms are tested, tuned, evaluated and compared before selecting Gradient Boosting as the best option to model the data analysed. A similar approach is followed for the selection of the multi-objective optimisation algorithm. For this task, six techniques are tested and compared based on the hypervolume performance indicator to just then select the Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the best option. Given this applied research focus on a real manufacturing process, real-world constraints and variables such as individual scrap price, scrap availability, tap additives and ambient temperature are used in the models developed here. A comparison with an equivalent EAF model from the literature showed a 13% improvement using the mean absolute error in the EAF energy usage prediction as a comparative metric. The multi-objective optimisation resulted in reductions in the energy consumption costs that ranged from 1.87% up to 8.20% among different steel grades and scrap cost reductions ranging from 1.15% up to 5.2%. The machine learning models and the optimiser were ultimately deployed with a graphical user interface allowing the melt-shop staff members to make informed decisions while controlling the EAF operation.

Six Sigma application for quality improvement of the hot press in process

Six Sigma is a systematic improvement method, a management approach designed to continuously improve corporate business processes and achieve customer requirement. This paper presents a case study conducted at a cell phone manufacturing company that intended to use the DMAIC method to carry out a structured approach to Six Sigma projects with fewer defective units generated by the cell phone hot press in process. In this process, Boss cracking and torque force failure led to high scrap costs and line downtime, which ultimately affected the supply of the assembly line. To this end, the Six Sigma approach was applied starting with the problem being defined, measured, and exhaustively analyzed to determine the root cause: poor injection molding structure, metal insert not perpendicular to the hole, improperly set molding machine parameters, and core/cavity dimension variation. A series of improvement measures addressing these factors increased the quality level of the process by improving the mold design to increase venting, optimizing the product structure to add the guide chamfer, and developing a standardized molding parameter setup procedure. Methods to maintain process control were also identified and implemented. The use of several quality tools and the use of Six Sigma methods also made the process more consistent.