Hasil untuk "Production capacity. Manufacturing capacity"

W. Hopp, M. Spearman

M. Green

RAMDHANY IHTIFAZHUDDIN, Nana Sutarna, Britantyo Wicakson

This research is motivated by problems at PT Sentral Kreasi Kencana in the immersion freezing process for jewelry production. This process is still carried out manually with a fixed time assumption of 45 minutes, which causes temperature uncertainty and difficulty in increasing production quantity. This has an impact on decreasing efficiency and increasing production costs per gram. The study aims to increase the efficiency of the immersion freezing process by implementing a sensor-based temperature monitoring system and production cost analysis. The methods used include developing a temperature monitoring system using the MLX90614 sensor, integration with Arduino Uno, and creating a graphical user interface (GUI) for real-time data analysis. The system developed consists of an MLX90614 infrared temperature sensor, an Arduino Uno microcontroller, a buzzer as an alarm, and a push-button for batch calculation. The GUI displays real-time temperature data, trend graphs, total batches, product weight, fixed costs, and cost/gram calculations. The results showed that the implementation of the temperature monitoring system increased the number of daily production batches by 50%, from 4 to 6 batches. The processing time was also reduced from 45 minutes to 30 minutes per batch. The optimum temperature of −7◦C was set as the reference point for the immersion freezing process. Production cost analysis showed a significant decrease in cost/gram from IDR 6,333.33 to IDR 2,638.89, far below the company’s standard cost of IDR 5,000.00 per gram. This system has proven effective for gold with a content of 34.0%, 67.1%, and 75.5%. The implementation of this technology has succeeded in increasing production efficiency, reducing the cost per gram, and increasing overall production capacity. This research provides a practical solution for optimizing the immersion freezing process in the jewelry industry, with the potential for wider application in other precision manufacturing sectors.

Yupeng Wu, Jiasheng Li, Zhaocheng Wei et al.

A unified viscoplastic constitutive model based on internal physical variables was proposed to predict the viscoplastic mechanical behavior and microstructure evolution of metals during hot forging. Based on the phase transformation theory of materials under the effect of temperature, the evolution mechanism of residual stress during the cooling process after hot forging and stamping was explored. The determined unified viscoplastic constitutive equation was written in the VUMAT subroutine and employed for the explicit FE analysis of the hot forging and stamping process of thin-walled spherical shells. In the data transfer process, the stress field, temperature field, and deformation characteristics calculated during the high-temperature transient of the hot forging and stamping process were inherited. Meanwhile, the thermoplastic constitutive equation considering the influence of phase transformation was written in the UMAT subroutine and utilized for the implicit FE analysis of the cooling process of thin-walled spherical shells. Through comparison with the measured stress results of the spherical shells after actual forging, it was shown that the proposed constitutive model can effectively predict the microstructural evolution and the final residual stress distribution pattern of medium-carbon steel during the hot forging process.

Hüseyin Cem Kulak, Hasan U. Akay

In aviation, weight is crucial for aircraft performance and payload capacity. Traditional design methods, which rely on trial and error, aim to create lightweight and strong structures but can be time-consuming. Topology optimization, a mathematical technique, speeds up finding the optimal material distribution within a given shape under specific loads and conditions. This study employed ANSYS's CFD and Structural Optimization modules for the topology optimization of a NACA 0012 airfoilwing section under various conditions. The CFD module provided aerodynamic loads to the structural topology optimization module. The study analyzed both 2D and 3D geometries under different conditions, including single-point and multi-point optimizations for 2D sections with varying angles of attack, which provided useful comparisons. For the 3D test case, CFD analysis and topology optimization were performed on a rectangular wing at an inflow speed of 0.5 Mach and a 0⁰ angle of attack. Such combined structural and flow analysis is rare in the literature. This research provides newinsights, highlighting the sensitivity of topology optimization to boundary conditions and computational meshes. Despite challenges from complex geometries, this approach is expected to grow in popularity, especially with advanced production methods like 3D printing and additive manufacturing

Michal Povolný, Michal Straka, Miroslav Gombár et al.

Additive and coating technologies, such as high-velocity oxy-fuel (HVOF) thermal spraying and direct metal laser sintering (DMLS), often require extensive post-processing to meet dimensional and surface quality requirements, which remains challenging for nickel-based superalloys such as Inconel 718. This study presents the design and topology optimisation of a cutting tool with a linear cutting edge, capable of operating in turn-milling or turning modes, offering a viable alternative to conventional grinding. A non-optimised tool served as a baseline for comparison with a topology-optimised variant improving cutting-force distribution and stiffness-to-mass ratio. Finite element analyses and experimental turn-milling trials were performed on DMLS and HVOF Inconel 718 using carbide and CBN inserts. The optimised tool achieved significantly reduced roughness values: for DMLS, Ra decreased from 0.514 ± 0.069 µm to 0.351 ± 0.047 µm, and for HVOF from 0.606 ± 0.069 µm to 0.407 ± 0.069 µm. Rz was similarly improved, decreasing from 4.234 ± 0.343 µm to 3.340 ± 0.439 µm (DMLS) and from 5.349 ± 0.552 µm to 4.521 ± 0.650 µm (HVOF). The lowest measured Ra, 0.146 ± 0.030 µm, was obtained using CBN inserts at the highest tested cutting speed. All improvements were statistically significant (<i>p</i> < 0.005). No measurable tool wear was observed due to the small engagement and the use of a fresh cutting edge for each pass. The resulting surface quality was comparable to grinding and clearly superior to conventional turning. These findings demonstrate that combining topology optimisation with a linear-edge tool provides a practical and efficient finishing approach for additively manufactured and thermally sprayed Inconel 718 components.

Chengkai Zhu, Renfeng Peng, Bin Gao et al.

Computing the classical capacity of a noisy quantum channel is crucial for understanding the limits of communication over quantum channels. However, its evaluation remains challenging due to the difficulty of computing the Holevo capacity and the even greater difficulty of regularization. In this work, we formulate the computation of the Holevo capacity as an optimization problem on a product manifold constructed from probability distributions and their corresponding pure input states for a quantum channel. A Riemannian gradient descent algorithm is proposed to solve the problem, providing lower bounds on the classical capacity of general quantum channels and outperforming existing methods in numerical experiments in both efficiency and scale.

A. Mohsenzadeh, A. Zamani, M. Taherzadeh

Manufacturing of bioethylene via dehydration of bioethanol is an alternative to the fossil-based ethylene production and decreases the environmental consequences for this chemical commodity. A few industrial plants that utilize 1st generation bioethanol for the bioethylene production already exist, although not functioning without subsidiaries. However, there is still no process producing ethylene from 2nd generation bioethanol. This study is divided into two parts. Different ethanol and ethylene production methods, the process specifications and current technologies are briefly discussed in the first part. In the second part, a techno-economic analysis of a bioethylene plant was performed using Aspen® plus and Aspen Process Economic Analyzer, where different qualities of ethanol were considered. The results show that impurities in the ethanol feed have no significant effect on the quality of the produced polymer-grade bioethylene. The capacity of the ethylene storage tank significantly affects the capital costs of the process.

Mahdi Saleh Mathkoor, Raad Jamal Jassim, Raheem Al-Sabur

The rapid spread of the use of high-density polyethylene (HDPE) pipes is due to the wide variety of methods for connecting them. This study keeps pace with the developments of butt fusion welding of HDPE pipes by exploring the relationship between the performance of the weld joints by studying ultimate tensile strength and exploring the joint welding profiles by studying the shape of the joint at the outer surface of the pipe (height and width of the joint cap) and the shape of the joint at the internal surface (height and width of the joint root). Welding pressure, heater temperature, stocking time, and cooling time were the parameters for the welding process. Regression was analyzed using ANOVA, and an ANN was used to analyze the experimental results and predict the outputs. Two optimization techniques (pattern search and genetic algorithm) were applied to obtain the ideal operating conditions and compare their performance. The results showed that pattern search and genetic algorithms can determine the optimal output results and corresponding welding parameters. In comparison between the two methods, pattern search has a limited relative advantage. The optimal values for the obtained outputs revolved around a tensile strength of 35 MPa (3.45 and 4.5 mm for the cap and root heights, and 8 and 6.98 mm for the cap and root widths, respectively). When comparing the effects of welding parameters on the results, welding pressure had the best effect on tensile strength, and plate surface temperature had the most significant effect on the welding profile geometries.

Gianmauro Fontana, Maurizio Calabrese, Leonardo Agnusdei et al.

The soldering process for aerospace applications follows stringent requirements and standards to ensure the reliability and safety of electronic connections in aerospace systems. For this reason, the quality control phase plays an important role to guarantee requirements compliance. This process often requires manual control since technicians’ knowledge is fundamental to obtain effective quality check results. In this context, the authors have developed a new open source dataset (SolDef_AI) to implement an innovative methodology for printed circuit board (PCB) defect detection exploiting the Mask R-CNN algorithm. The presented open source dataset aims to overcome the challenges associated with the availability of datasets for model training in this specific research and electronics industrial field. The dataset is open source and available online.

Safoora Meysamiazad, Ali Hijiha, Mohammad Ali Abdolvand et al.

AbstractThe purpose of this research is to identify the determining factors of green product branding, a model for Iran's food industry. The research method is applicable in terms of purpose, and mixed (qualitative-quantitative) in terms of implementation method, and survey-exploratory in terms of data collection method. The statistical population of the research in the qualitative part includes 15 managers of green brand food companies with master's and PhD educations in the field of management, agriculture and entrepreneurship, as well as professors of business management and environment at the university, who were selected for an interview by means of judgmental sampling. The statistical population in the quantitative part is the consumers of green products in the food industry; 384 people were selected using available sampling and answered the questions of the questionnaire. Interviews and questionnaires made by the researcher and taken from the qualitative section were used to collect information. In the qualitative section, the data obtained from the interviews were coded and analyzed in three main stages: open coding, axial coding, and selective coding. In the quantitative section, SPSS software was used for analysis and PLS was used for structural equations. The results in the qualitative section showed that 214 open codes, 85 concepts and 26 subcategories were identified and extracted from the conducted interviews. The results in the quantitative part showed that the model has a suitable fit and can be used for branding green products in the country's food industry.Extended AbstractIntroductionToday, in response to the increasing public interest in sustainable development, many companies have introduced green products. The characteristics of production and consumption of green products are in accordance with the concepts of economy, where waste reduction and environmental protection are the most important (Govindan & Hasanagic, 2018). In the economy, green products are increasingly popular with consumers and widely marketed. Selling green products creates domestic competition with non-green products. Green products usually have a higher quality level than non-green products. Due to the sustainable production method, green products have a higher production cost than non-green products (Shen et al, 2019). As a result, it is widely observed that green products are more expensive than non-green products (Basiri & Heydari, 2017). Consumers are also looking for a brand that has a strong planning strategy and methodology to achieve environmental sustainability in accordance with current and future regulatory guidelines and policies. Therefore, most business units have tried to incorporate sustainability into process and product or service design (Upadhyay & Kumar, 2020). Branding can be critical to a company's long-term success, especially for companies operating in markets with many clusters (many buyers and sellers) and few differentiated products. On the other hand, in recent years, climate changes along with increasing environmental awareness have changed consumers' purchasing decisions towards green and environmentally friendly products (Aivazidou et al, 2017). Green marketing is emerging as a popular advertising strategy due to increasing environmental concerns and awareness. In addition, in order to achieve a better understanding of the environmental movement of the target society, it is an important issue to test the attitude of the consumers of that country towards environmental issues and, as a result, their behavior (Mohammadi et al, 2022).Based on this, the current research is looking for an answer to this question: What are the determining factors of branding green products, a model for Iran's food industry?Theoretical FrameworkbrandA brand is not only a symbol that distinguishes a product from others, but also includes all the features that come to mind when a buyer thinks of that brand. These characteristics are the objective, abstract, psychological and social characteristics of that product (Xiangbo et al, 2021). Green brands, green labels and characteristics of green environmental products create positive feelings in certain groups and consumers who know that a product is green and when it is better to use it. Natural brands and proper labeling are successful from a marketing point of view because of the positive overall image they create, and consumers tend to buy such products and therefore stick with them (Del Afruz et al, 2017).Green marketingThe concept of green marketing is a business process that takes into account consumers' concerns about protecting the natural environment. Previously primarily based on environmental status, green marketing is becoming more sustainable in marketing efforts, with a primary focus on environmental and socio-economic status. However, the green market is defined as part of the market segments related to green consumption (Yoo et al, 2019).Sandoughi et al, (2022) studied the modeling process of organic agricultural products market development in Iran with an interpretative structural approach. Based on the obtained results, the process model of organic agricultural products market development starts from the analysis of the current situation, setting goals and prospects, and ends with the stage of increasing consumption and capacity building in the market. This model can be used as a guide by policy makers and all organic field activists in various research, planning and implementation sectors. Sarkar et al, (2022) investigated environmental and economic sustainability through innovative green products with renewable production. The findings showed that highly innovative green products perform better than low innovative products when uncertainty in demand and supply is high. Furthermore, new green products should be introduced only when the expected benefits of the new products outweigh the losses of the existing products. New policy innovation with remanufacturing is cost-effective compared to traditional innovation policy.Research methodologyAccording to its purpose, the research method is applicable; in terms of execution method, it is mixed (qualitative-quantitative); and in terms of data collection method, it is survey-exploratory. The statistical population of this research in the qualitative part is the managers of green brand food companies with master's and doctorate educations in the field of management, agriculture and entrepreneurship, as well as university professors of business management and environment. The statistical population in the quantitative section is the consumers of green products in this industry, which were considered as the sample size of 384 people using Cochran's formula and available sampling method. Collecting information in the qualitative part by the interview; and in the quantitative part of the research using the concepts obtained in the qualitative part, a questionnaire of 85 questions was used.Research findingsIn the qualitative section, the data obtained from the interviews were coded and analyzed in three main stages: open coding, axial coding, and selective coding. In the quantitative section, SPSS software was used for analysis and PLS was used for structural equations. The results in the qualitative section showed that 214 open codes, 85 concepts and 26 subcategories were identified and extracted from the conducted interviews. The results in the quantitative part showed that the model has a suitable fit and can be used for branding green products in the country's food industry.ConclusionThe current research has been carried out with the aim of identifying the determining factors of green product branding, a model for Iran's food industry. The results of the present study are in agreement with the results of Sandoughi et al, (2022), Mohammadi et al, (2022), Sarkar et al, (2022), Jegatheesan et al, (2021), Mohammadi Far & Soleimani (2021), Pourjamshidi et al, (2020), Marvi et al, (2021), Pourjamshidi et al, (2021), and Tandon et al, (2016). Mohammadi Far & Soleimani (2021) investigated the design of a multi-level framework for the successful implementation of green marketing in food manufacturing companies. The findings of the model indicate that several factors influence the implementation of green marketing in a multidimensional and intertwined manner. These factors can be categorized in four levels. The fourth level factors form the most basic layer and include the penetration of belief in green marketing in the philosophy and vision of the company; the third level includes the support of senior managers and changes in the organization's internal procedures; the second level includes optimizing the organizational structure, improving the organizational culture, improving employees and managing the change process: and the first level, which was placed in the highest and most operational layer of the interpretive structural model hierarchy, includes changes in the marketing mix, understanding and implementing green marketing audits, and developing technology infrastructure of information.According to the results obtained from the research, it is suggested:Advertising programs should be developed to familiarize the general public with green products, features and benefits on the platform of social networks.Human resource development programs and attention to the training of people in this field should be developed.Selection of food industry experts and experts in the field of green products so that their experiences in the field of green products production can be used.To improve the quality and safety of programs related to the production of green products and achieving health and management standards.

Tanzila Nargis, S. M. Shahabaz, Subash Acharya et al.

Carbon fiber-reinforced polymer (CFRP) composites have gradually replaced metals due to their exceptional strength-to-weight ratio compared to metallic materials. However, the drilling process often reveals various defects, such as surface roughness, influenced by different drilling parameters. This study explores the drilling quality of uni-directional CFRP composites, as well as hybrid Al₂O₃ alumina and hybrid SiC silicon carbide nano-composites, through experimental exploration using step, core, and twist drills. Response surface methodology (RSM) and statistical tools, including main effect plots, ANOVA, contour plots, and optimization techniques, were used to analyze the surface roughness of the hole. Optimization plots were drawn for optimal conditions, suggesting a spindle speed of 1500 rpm, feed of 0.01 mm/rev, and a 4 mm drill diameter for achieving minimum surface roughness. Furthermore, two machine learning models, artificial neural network (ANN) and random forest (RF), were used for predictive analysis. The findings revealed the robust predictive capabilities of both models, with RF demonstrating superior performance over ANN and RSM. Through visual comparisons and error analyses, more insights were gained into model accuracy and potential avenues for improvement.

Nurhadi Siswanto, Ahmed Raecky Baihaqy, Mohd Shukor Salleh et al.

AbstractCustomer demand fulfilment is essential for production systems in the highly competitive market environment. Because of that fact, a reliable production line is necessary for timely demand fulfilment, and the system must maintain an appropriate spare parts inventory to run the production line smoothly. This research aims to study the impact of spare part inventory policy on production line availability and demand fulfilment. This paper considers a steel pipe manufacturing company that runs continuously. This company uses a continuous raw material flow and produces discrete finished items. The company’s production system is multi-state with unique characteristics in which the machines can be operated at partial capacity under certain failures or disturbances. Hence, three states of the production system, namely fail, working under failure, and success (denoted as 0, 0.5, and 1, respectively) are considered. In addition, the company follows s,S policy for its spare parts inventory system. To deal with this problem, a discrete-event simulation approach is developed as the problem environment is complex with interdependency and variability. A multi-level reliability block diagram is also used to build the production system model. The proposed simulation model is run for different scenarios with either cost or performance as the objective measure. Based on the analysis of results, the proposed approach can improve the current operation in terms of both production line availability and demand fulfilment.

Alexander Meiburg

The zero-error capacity of a channel (or Shannon capacity of a graph) quantifies how much information can be transmitted with no risk of error. In contrast to the Shannon capacity of a channel, the zero-error capacity has not even been shown to be computable: we have no convergent upper bounds. In this work, we present a new quantity, the zero-error {\em unitary} capacity, and show that it can be succinctly represented as the tensor product value of a quantum game. By studying the structure of finite automata, we show that the unitary capacity is within a controllable factor of the zero-error capacity. This allows new upper bounds through the sum-of-squares hierarchy, which converges to the commuting operator value of the game. Under the conjecture that the commuting operator and tensor product value of this game are equal, this would yield an algorithm for computing the zero-error capacity.

Ning Qi, Pierre Pinson, Mads R. Almassalkhi et al.

This paper proposes a novel capacity credit evaluation framework to accurately quantify the contribution of generalized energy storage (GES) to resource adequacy, considering both strategic capacity withholding and decision-dependent uncertainty (DDU). To this end, we establish a market-oriented risk-averse coordinated dispatch method to capture the cross-market reliable operation of GES. The proposed method is sequentially implemented along with the Monte Carlo simulation process, coordinating the pre-dispatched price arbitrage and capacity withholding in the energy market with adequacy-oriented re-dispatch during capacity market calls. In addition to decision-independent uncertainties in operational states and baseline behavior, we explicitly address the inherent DDU of GES (i.e., the uncertainty of available discharge capacity affected by the incentives and accumulated discomfort) during the re-dispatch stage using the proposed data-driven distributional robust chance-constrained approach. Furthermore, a capacity credit metric called equivalent storage capacity substitution is introduced to quantify the equivalent deterministic storage capacity of uncertain GES. Simulations on the modified IEEE RTS-79 benchmark system with 20 years real-world data from Elia demonstrate that the proposed method yields accurate capacity credit and improved economic performance. We show that the capacity credit of GES increases with more strategic capacity withholding but decreases with more DDU levels. Key factors, such as capacity withholding and DDU structure impacting GES's capacity credit are analyzed with insights into capacity market decision-making.

Md Rabiul Hasan, Zhichao Liu, Asif Rahman

The awareness of energy consumption is gaining much more attention in manufacturing due to its economic and sustainability benefits. An energy consumption model is needed for quantifying the consumption and predicting the impact of various process parameters in manufacturing. This paper aims to develop an energy consumption model for Direct Energy Deposition (DED) based Hybrid Additive Manufacturing (HAM) for an Inconel 718 part. The Specific Energy Consumption (SEC) is used while developing the energy consumption of the product manufacturing lifecycle. This study focuses on the analysis to investigate three significant factors (scanning speed, laser power, and feed rate), their interactions' effects, and whether they have a significant effect.in energy consumption. The results suggest that all the factors have a strong influence, but their interaction effects have a weak influence on the energy consumption for HAM. Among the three process parameters, it is found that laser power has the most significant effect on energy consumption. Again, based on the regression analysis, this study also recommends high scanning speed while the laser power and feed rate should be low. Also, idle time has significant energy consumption during the whole HAM process.

J. Schnell, C. Nentwich, F. Endres et al.

Abstract Data mining methods are used to analyze and improve production processes in a lithium-ion cell manufacturing line. The CRISP-DM methodology is applied to the data captured during the manufacturing processes. Key goals include the identification of process dependencies and key quality drivers as well as the prediction of the product quality before the cumbersome and costly formation and aging procedure. Several Data mining methods, such as Generalized Linear Model (GLM), Artificial Neural Networks (ANN), Support Vector Regression (SVR), Decision Trees (DT), Random Forest (RF), and Gradient Boosted Trees (GBT) are compared and evaluated. Best results are yielded by an application of GLM, RF, and GBT for prediction of battery cell capacity before the expensive formation process. Key quality drivers identified are the electrode fabrication processes, as well as the electrolyte filling process during cell assembly. This is, to our knowledge, the first time data from a real battery production line has been systematically processed and analyzed along the whole process chain. The results of this paper can assist to manufacture better batteries and to reduce costs of lithium-ion cells by providing a systematic procedure for data acquisition and by lowering scrap rates during production.

Franciele Stoffel Viña, Liane Mahlmann Kipper, Jorge André Ribas Moraes

Os Resíduos Sólidos Urbanos (RSU) necessitam de uma gestão adequada, pois envolvem o cuidado com o meio ambiente e a saúde da população. O objetivo da pesquisa é mapear a evolução do conceito de Ponto de Entrega Voluntária (PEV) e suas relações com o processo de reciclagem dos RSU; avaliar como a Política Nacional de Resíduos Sólidos (PNRS) contribuiu para a disseminação das Cooperativas de Materiais Recicláveis e como a gestão municipal pode utilizar os PEVs como instrumentos para o fortalecimento das práticas da coleta seletiva no Brasil. A gestão municipal, utilizando os 16 indicadores selecionados, poderá estruturar PEVs, atendendo a PNRS, e qualificar a gestão dos RSU. Essa ação contribuirá também para melhorar a qualidade de vida dos catadores de materiais recicláveis, elevará a educação ambiental de toda a população afetada e reduzirá desperdícios de materiais que podem ser reintroduzidos na cadeia de produção e consumo.

Ahmed Sayem, Pronob Kumar Biswas, Mohammad Muhshin Aziz Khan et al.

The fourth industrial revolution, fueled by automation and digital technology advancements, enables us to manage manufacturing systems effectively. Its deployment in enterprises has now become increasingly important in developed and emerging economies. Many experts believe that barriers associated with Industry 4.0 implementation are critical to its success. Therefore, this study aimed to identify the major hurdles to Industry 4.0 adoption and reveal their interrelationships. Initially, the literature was thoroughly studied to determine the sixteen barriers impeding I4.0 adoption. Then, based on experts’ opinions, an integrated fuzzy-DEMATEL approach was utilized to examine the most significant challenges to I4.0 deployment. The results demonstrated the distribution of barriers in which the economic dimension played a decisive role, affecting technological, regulatory, and organizational dimensions. As observed in the barrier mapping, the lack of qualified workforce was a typical adoption barrier. Finally, the mitigation strategies developed would help managers to overcome the identified critical obstacles.

Arturo Realyvásquez-Vargas, K. Arredondo-Soto, Teresa Carrillo-Gutierrez et al.

Defects are considered as one of the wastes in manufacturing systems that negatively affect the delivery times, cost and quality of products leading to manufacturing companies facing a critical situation with the customers and to not comply with the IPC-A-610E standard for the acceptability of electronic components. This is the case is a manufacturing company located in Tijuana, Mexico. Due to an increasing demand on the products manufactured by this company, several defects have been detected in the welding process of electronic boards, as well as in the components named Thru-Holes. It is for this reason that this paper presents a lean manufacturing application case study. The objective of this research is to reduce at least 20% the defects that are generated during the welding process. In addition, it is intended to increase 20% the capacity of three double production lines where electronic boards are processed. As method, the Plan-Do-Check-Act (PDCA) cycle, is applied. The Pareto charts and the flowchart are used as support tools. As results, defects decreased 65%, 79%, and 77% in three analyzed product models. As conclusion, the PDCA cycle, the Pareto charts, and the flowchart are excellent quality tools that help to decrease the number of defective components.