Production Cycle Time Research Articles

Using Bayesian network technology to predict the semiconductor manufacturing yield rate in IoT

In the era of the knowledge economy, one of the key issues is how to integrate and use intelligent information systems to collect data and make valuable predictions to support business decisions. Intelligent information systems use artificial intelligence to enhance system performance, giving the enterprise a competitive advantage. This paper uses ontology technology for user requirements analysis—defining the class, slot, and instance of the ontology—and then designs the system architecture based on that ontology. In accordance with the firm’s requirements, we build a data warehouse system that is integrated with different data sources within the enterprise and that supports a web-based interface in the Internet of Things (IoT). The system also supports the standard queries, reports, summary tables, and datasets required for data mining. The proposed data mining method for manufacturing industries that use Bayesian network incorporates Bayesian theory and graphical models and can predict causal and probabilistic relationships among a set of variables. Our results bring information system functionality closer to satisfying the real-world needs of business. The proposed system can reduce production cycle times, increase the speed and accuracy with which production information is analyzed, and offer predictions that can be used for better business decisions. Data mining technology can improve the efficiency of manufacturing processes by using feedback data to tune the manufacturing parameters and improve the accuracy of yield rate predictions, giving the firm a greater competitive advantage.

Effect of pouring temperature on hot tearing susceptibility of Al-Cu cast Alloy: Casting simulation

Al-Cu alloy more prone to hot tear in the foundry metals in casting process. It is widely using in structural, aerospace, marine and automotive industry. Defect-free casting is being challenge in the foundry. In the present investigation, the hot tearing of the molten Al-Cu cast alloy is reported by varying the pouring temperature at 700 °C, 750 °C and 780 °C by means of casting simulation. It has a direct impact on production cycle time, casting internal quality, material utilization, and gating design optimization. Simulation was performed by AutoCAST-X software; it enables directional solidification and feed metal paths. In the simulation set up have been considered green sand mould system with the wooden pattern, and the steel metallic cores are facilitated in the cavity to contrast the temperature while solidification process; as a result pouring temperature is subject to crack susceptibility. The results revealed that the dependence on pouring temperature is a key factor for the prediction of hot tearing susceptibility. The microscopic cracks were observed in casted part by simulation. The Casting simulation forecast the hot spot zone in casting solidification process to facilitate the riser for compensation of shrinkage allowance of molten metal. Simulation enhances the material utilization, quality of casting, productivity and optimization of gating design. The relation of pouring temperature was developed with solidification time.

The Role of Production Planning in Enhancing an Efficient Manufacturing System – An Overview

Every manufacturing system can only survive with a high level of system planning and budgeting. Production planning is a sequential ladder in the manufacturing setting to ensure that materials input (raw materials, men, money, and machine) is made available within a stipulated time frame, in the appropriate amount to produce the demanded output of goods and services based on the schedule specified. Production planning also helps in making available high quality of goods and services in the right quantities to the customer at the demanded delivery timing to attain higher degree of customer satisfaction. Through production planning, different departmental activities i.e., engineering, production, purchasing, sales, and marketing, as well as stock control department, and other units that may relate to production are coordinated to attain a regular, steady, and balanced flow of production thus, enhancing reduction in production cycle time. Production planning serves as an instrument which estimates to gear up the performance of different departments and individuals of an organization in such a manner that will enhance better service to customers, fewer rush orders, more efficient use of equipment, reduced idle time of both machine and personnel, improved plant morale, good public image, and lower capital requirement, it may also create alternative plans to meet any form of emergency or contingency during the cause of production. From the foregoing, a manufacturing system that incorporates production planning in its operations will be more efficient, effective, and economical as compared with those that did not.

Optimal Decision Tree for Cycle Time Prediction and Allowance Determination

The due-date quotation is a key performance indicator for managing customer orders which would influence customer acceptance and/or the future potential lateness penalty. The production cycle time and allowance time are added and used as the due date of order. The objective is to maximize the hit rate which is the percentage of the orders fulfilled within the time limit of quoted due date. Under the framework of supervised machine learning, we explore the new developments in feature selection and the optimal decision tree to predict cycle time by using mixed-integer optimization. Cycle time allowance could be added to the predicted cycle time or incorporated in an optimization problem as a managerial decision variable. Case studies are used to demonstrate the effectiveness of this approach, and their performances are comparable to the other popular ensemble tree approaches, such as random forests and gradient boosting.

Closed-form solution for lot-sizing problem in manufacturing systems with imperfect matching

Integrated single-vendor single-buyer problem has received a lot of attention as it is the building block for wider supply chains. This paper considers a manufacturing system in which a single manufacturer procures raw materials from a single supplier, processes them to produce finished products, and delivers to a just-in-time (JIT) customer. The manufacturer's production cycle is imperfect where the production uptime and cycle time are not exact integer multiples of the shipment interval to the customer. The closed-form solution for the optimal batch size is developed to minimize the total cost of procurement and production. It is found that the total cost under imperfect matching is always lower than or equal to that under perfect matching. Different from previous works under perfect matching, it is found that the optimal total cost is a piecewise concave function of the ordering and setup costs, and also a piecewise convex function of the shipment size to the customer.

Investigation of support structure parameters and their affects during additive manufacturing of Ti6Al4V alloy via electron beam melting

Electron beam melting technology offers various benefits like the reduced product cycle time, customization, flexibility, high energy density and less material wastage. However, electron beam melting still suffers from redundant usage of support structure material while fabricating overhang structures. The support structures not only consume additional material, but also require additional time for their design and removal. The optimized support structures have to be designed in such a way that they consume minimum material, are easy to remove and are free from defects. The aim of the current study is to study the effect of support design and process parameters on the performance of the support structures (cost and quality) during additive manufacturing of Ti6Al4V alloy via electron beam melting. The results show that the support structures parameters play a significant role in the cost of the applied support and the accuracy of the fabricated object. It was found that with appropriate selection of support design and process parameters, it is possible to reduce the support volume and hence the fabrication cost in metal additive manufacturing (AM). A tooth height of 3 mm, no support offset of 2 mm, and fragmentation separation width of 0.8 mm resulted in lower support volumes without having any effect on the quality of the overhang. This study systematically investigated the support structure design and their outcomes on overhang fabrication. Its conclusions could add value to the researchers working on additive manufacturing of Ti6Al4V alloy by electron beam melting.

AN IMPROVED DESIGN OF A SOYBEAN PEEL SEPARATOR FOR TEMPE PRODUCTION

Tempe is a typical food of Indonesia which is consumed by people throughout the world. Tempe is made of fermented soybean by Rhizopus sp. The production of tempe is labor intensive. Among the twelve core stages in tempe production. One of that is separating the peel of soybean which is performed by human energy. It is hard to make contamination-free tempe since manual operation is involved in the process. This research was conducted by reverse engineering method with the aim to redesign an existing soybean peeling separator base on the user needs. The cycle time of whole tempe production to detect the problem were calculated. The longest time is peel separating process which use 40% from the whole tempe production. This station will be examined deeply an calculated per work elements and classify these elements to be the criterion of the developed machine. These criterion generate various design concepts, then each design will be chosen using scoring concept, screening concept, and feasibility to be realized. The results revealed that, the newly designed peel separator decreased the processing time by 39%, which decrease the risk of contamination by manual operator. In addition to that, the reduction of cycle time was decreased the water and electricity use of tempe production.

Truck Assembly Line Reconfiguration to Reduce Cycle Time with Lean Manufacturing Approach in the Indonesian Automotive Industry

The automotive industry is one of the industries that plays a major role in Indonesia’s revenue sources, one of which is in meeting daily needs, especially for transportation and logistics. This causes the growth of truck production targets each year and has become one of Indonesia’s focus to target 1.29 million trucks produced in 2020. This study aims to minimize production time with the optimal number of workstations to minimize costs that will come out for increasing production capacity in truck assembly lines in Indonesia. The study was conducted with a lean manufacturing approach to analyze the waste contained in the assembly line. The study began by calculating time for each work using the time study method. The time obtained is then inputted to the mixed integer linear programming mathematical programming model developed in the LINGO 17.0 software. The result of data processing in the LINGO 17.0 software is the allocation of each job to the new workstation to get the production cycle time in accordance with the production takt time, and also the total idle time of the new assembly line.

Efficient Optical Measurement of Welding Studs With Normal Maps and Convolutional Neural Network

Studs are small screws welded on a car's body. Measuring the key point positions of the studs within production cycle time, also known as online measurement, is crucial for the quality management of the cars. However, the existing stud measuring systems can only measure one stud in a camera position, which is not efficient enough for online measurement. Accurately measuring multiple studs in one camera position is still a challenge. In this article, we propose a stud online measuring system (SOMS), which can measure multiple studs with high accuracy based on photometric stereo and deep learning techniques. To achieve accurate measurements, the SOMS uses normal maps, which are obtained by photometric stereo. Conventional photometric-stereo-based inspection methods reconstruct the surface from the normal maps; however, the reconstruction is inaccurate when the object has a large depth variation range. Instead of reconstruction, the SMOS directly uses a convolutional neural network (CNN) to localize the key points of studs from the normal maps. To adapt CNN to normal map input, we proposed a fusion block which extracts the most significant features among three dimensions of the normal maps. A test based on a collection of different types of studs was conducted to evaluate the advantage of the SOMS. Our system outperformed benchmarks in a series of experiments. Because the SOMS was developed without any prior knowledge on the geometry of the stud, it also has the potential for other optical challenging geometric features' online measurements.

Comparative job production based life cycle assessment of conventional and additive manufacturing assisted investment casting of aluminium: A case study

Conventional investment casting (IC) process, despite being one of the highly precise manufacturing processes, is suffering from key intrinsic problems, including acquisitions of the enormous resources and specialized machine tools, long production runs, and dedicated workforce. However, additive manufacturing (AM) could be a viable solution to assist the IC process in dealing with the aforementioned issues as well as to refurbish its environmental sustainability. The present research work aims to compare the Life Cycle Assessment (LCA) of conventional and AM assisted IC processes for producing aluminium castings. Particularly, the emphases have been made on comparing the production cycle times, cost, specific energy consumption, and carbon footprints. Along with this, the quality characteristics of as-cast aluminium specimens have been compared for conceptual validity. The results of the study indicated that the use of AM technology has not only curbed the acquisition of the resources and eliminated the necessity of specialized machine tools, but also has reduced the production cycle time, processing cost, specific energy consumption, and carbon emissions by about 19%, 93%, 70%, and 71%, respectively. Overall, the AM assisted IC process has been observed as a rapid, cost-effective, environmentally sustainable process for the job production run.

Effect of Temperature-Dependent Bacterial Growth during Milk Protein Fractionation by Means of 0.1 µM Microfiltration on the Length of Possible Production Cycle Times.

This study determined the maximum possible filtration time per filtration cycle and the cumulated number of operational hours per year as a function of the processing temperature during milk protein fractionation by 0.1 µm microfiltration (MF) of pasteurized skim milk. The main stopping criteria were the microbial count (max. 105 cfu/mL) and the slope of the pH change as a function of filtration time. A membrane system in a feed and bleed configuration with partial recirculation of the retentate was installed, resembling an industrial plants’ operational mode. Filtration temperatures of 10, 14, 16, 20, and 55 °C were investigated to determine the flux, pH, and bacterial count. While the processing time was limited to 420 min at a 55 °C filtration temperature, it could exceed 1440 min at 10 °C. These data can help to minimize the use of cleaning agents or mixing phase losses by reducing the frequency of cleaning cycles, thus maximizing the active production time and reducing the environmental impact.

Reducing Cycle Time of Machining a Manifold on a CNC Machine

This report focuses on the different methods to improve productivity at the management level, at the machine shop, and of machining methods. The various parameters affecting machining productivity i.e., cycle time, set-up time, idle time, operator skill level, etc. including how CNC machining helps in optimizing these parameters are discussed and analyzed in this paper. The effect of different cutting parameters like cutting speed, feed rate, depth of cut, the effect of coolant, etc. is also examined. Various methods of machining for different processes which help to improve the productivity of the process are explored. A case study on machining of an aluminum manifold is done in which the existing process for machining is analyzed and various experiments are performed in order to find out the optimum cutting parameters and minimizing the cycle time of production of the manifold. The result helps to reduce the cycle time by a significant amount thereby reducing the total cost of production and increasing the production rate of the component with negligible capital investment.

Research on Logistics Simulation and Optimization of Die Forging Production Line Based on Flexsim

Long forging production cycle and low equipment utilization are the important problems currently facing domestic die forging ring production lines. To solve this problem, a simulation optimization method for die forging production line based on Flexsim was proposed. Firstly, the ECRS theory method was used to analyze the bottleneck process of the production line. Then, built a logistics simulation model of die forging production line based on Flexsim, The parallel and cycle processing steps was set reasonable parameters and simulated. Next, through logic analysis, methods such as adding processing equipment, improving equipment efficiency and balancing equipment handling tasks were used to optimize and improve the production line. Finally, compared and analyzed the original and optimized model. The simulation results show that the optimized production line equipment task allocation tends to be rationalized, the equipment utilization rate is increased by 4.34%, and the product cycle time is reduced by 27.65%, and optimized planning and design are achieved.

Computer Aided Modelling of the Impact of Irregular Power Supply on Machining Foundry Products

Computer aided models such as Dynamic, Quadratic, and Integer programming have been developed to solve production challenges relating to Manpower, Inventory and Maintenance assuming constant and regular power supply, which is a hard condition to meet in a developing country like Nigeria where power supply is irregular. This paper developed a Computer aided model to address this challenge using Integer programming (IP), by considering profit (Z) as a function of cost and revenue of a machined foundry product. Software was developed to serve as database and the same time analyzed complex mixed – model system where parallel workstation, shift or batch production is allowed. The software was designed to have graphic user interface (GUI), which enable the user to input data like values of product code (identification number for each product), capacity of machine, established unit cost of production, production cycle time, selling cost and the unit cost of production to determine the impact when electricity power from national grid is used, as opposed to fueling a diesel-engine generator, for machining foundry products. Costs and production capacity data were collected from Ruthade Foundry Engineering Services, Akure, Ondo State, to validate the model. The results revealed that output per unit time is higher when power from national grid is holistically utilized than when power from generator was used for machining foundry product. The results serve as genuine guiding tool for foundry operators in selecting a particular course of action on electricity utilization during production and enhance sustainability of production industry. Keywords— Constraint, cycle time, integer programming, Microsoft Visual Basic, model, optimal number

Developing a Hybrid Approach for Lean Six Sigma Project Management: A Case Application in the Reamer Manufacturing Industry

Lean six sigma (LSS) is a well-structured process improvement methodology aimed at removing wastes and defects to increase the operational efficiency of a process. In this article, the authors were tasked to identify the root causes of the reworks and rejections occurring in the polycrystalline diamond (PCD) reamer line of a tool manufacturing industry and provide solutions and implement methods to overcome the rejections. Therefore, the author constructed a hybrid approach comprising the select, define, measure, map, analyse, improve, control, sustain (SDMMAICS) model to enhance the define, measure, analyse, improve, control (DMAIC) approach. Further, decision-making trial and evaluation laboratory (DEMATEL) was incorporated in the improve phase of the LSS model to ensure effective decision making. The model is backed by a KAIZEN framework to ensure the sustenance of the improvements implemented. This hybrid LSS framework was successfully applied and resulted in an increased sigma level from 3.3 to 4.3, improving the cycle time of PCD reamer production by 12%. The hybrid SDMMAICS-DEMATEL approach used is easy to follow and can be adopted in any LSS project. Moreover, the relevance of the model in a part manufacturing setup has been effectively tested and will serve as a reference for researchers in process improvement studies.

Productivity improvement in manifold machining using CNC machines

This report focuses on the different methods to improve productivity at management level, at the machine shop and of machining methods. The various parameters effecting machining productivity i.e., cycle time, set-up time, idle time, operator skill level etc. including how CNC machining helps in optimizing these parameters are discussed and analysed in this paper. Effects of different cutting parameters like cutting speed, feed rate, depth of cut, effect of coolant etc. are also examined. Various methods of machining for different processes which help to improve the productivity of the process are explored. A case study on machining of an aluminium manifold is done in which the existing process for machining is analysed and various experiments are performed in order to find out the optimum cutting parameters and minimizing the cycle time of production of the manifold. The result helps to reduce the cycle time by a significant amount thereby reducing the total cost of production and increasing the production rate of the component with negligible capital investment.

Relayout Production Facility of PC. Spun Pile Using Systematic Layout Planning in ABC Factory

ABC Factory is one of the companies engaged in manufacturing precast concrete in Indonesia. This company was established in 1978 under the auspices of XYZ Group Since. The products produced by ABC Factory are Precast Concrete / Prefabricated Concrete or often abbreviated as "PC". Some of the products manufactured by ABC Factory are PC. Spun Pile, PC. Square Pile, PC. Spun Pole (Electric Pole), PC. I Girder, PC. Box Girder and several other products. ABC Factory uses the Make to Order production method, so it is demanded to produce orders maximally and as efficiently as possible. In this manufacturing company, the preparation of the layout of production facilities greatly affects the production cycle time which in turn will have a major effect on the company's production capacity. for that, the company needs to redesign the layout of the PC production facility of Spun Pile with the best production flow. Based on these problems, the method used is to use the SLP (Systematic Layout Planning) method which functions to produce a layout design for production facilities that minimizes material movement distance and produces a good production flow in ABC Factory. Based on the results of the study obtained a reduction in the distance of material flow facility layout by 72.71 meters and an increase in production capacity of 138 cigarettes or 13.44% of the initial layout.

Deployment and evaluation of a flexible human–robot collaboration model based on AND/OR graphs in a manufacturing environment

The Industry 4.0 paradigm promises shorter development times, increased ergonomy, higher flexibility, and resource efficiency in manufacturing environments. Collaborative robots are an important tangible technology for implementing such a paradigm. A major bottleneck to effectively deploy collaborative robots to manufacturing industries is developing task planning algorithms that enable them to recognize and naturally adapt to varying and even unpredictable human actions while simultaneously ensuring an overall efficiency in terms of production cycle time. In this context, an architecture encompassing task representation, task planning, sensing, and robot control has been designed, developed and evaluated in a real industrial environment. A pick-and-place palletization task, which requires the collaboration between humans and robots, is investigated. The architecture uses AND/OR graphs for representing and reasoning upon human-robot collaboration models online. Furthermore, objective measures of the overall computational performance and subjective measures of naturalness in human-robot collaboration have been evaluated by performing experiments with production-line operators. The results of this user study demonstrate how human-robot collaboration models like the one we propose can leverage the flexibility and the comfort of operators in the workplace. In this regard, an extensive comparison study among recent models has been carried out.

Integrated Procurement-Production Inventory Model with Two-Stage Production

The inventory-production system concerns the effective management of the goods flows from raw materials to finished products. The Integrated Procurement-Production (IPP) system consists of many elements that must be managed effectively. The problem will be more complex if it involves deciding on the number of delivery frequencies at the retailer level. In this case, the Integrated Procurement-Production's objective function depends on the frequency of raw material shipments, the frequency of delivery of finished products, and the production cycle time. This study aims to develop an IPP system to maximize total profit. The decision variables used are the frequency of raw material delivery, the frequency of delivery of finished products, and the production cycle time. This study proposes the Dragonfly Algorithm (DA) as an algorithm for problem-solving. Dragonfly Algorithm is used to find the best inventory decision variables. This study conducted experiments with various iteration parameters and DA population. The results showed that the greater the iteration and the population used, the greater the profit. A sensitivity analysis of decision variables is also presented in this investigation.

3D scanning of a carburetor body using COMET 3D scanner supported by COLIN 3D software: Issues and solutions

Nowadays, the industry uses 3D Scanners for reverse engineering, new product design, rapid manufacturing, multimedia, architecture, inspection, and quality control. The scanning process converts a real object into a digital format. This paper's essential purpose is to show the use of a 3D blue light Scanner/COMET 3D to redesign a carburetor body. The paper identifies different issues involved in the processes to help future users. COMET 3D does scanning of the carburetor body by which COLIN 3D software is used for measurements, editing, and analyzing of the acquired point clouds data. This paper also identifies the necessary steps to undertake 3D Scanning and part dimensioning for a carburetor body. It also discusses the error/problems that occurred during the process. The applications of non-contact blue light 3D Scanners are many as they can be innovatively used to redesign an existing part, architecture designing, and reducing production cycle time, biomedical and associated applications. This paper's contribution lies in achieving a step-by-step procedure of scanning any three-dimensional object as this helps in understanding the 3D scanning hardware and support software. It provides good knowledge of how to resolve the issues that can cause an error during the measurement of the surfaces and scan objects.