Production Machines Research Articles

Implementasi Pengambilan Keputusan Taktis Melalui Analisis Biaya Relevan Untuk Meningkatkan Pendapatan (Studi Kasus: Pabrik Rumahan Roti Hangat Keliling di Kota Jambi)

The Mobile Warm Bread Home Factory is a Micro, Small and Medium Enterprise (MSME) that processes raw materials into finished products, namely warm bread. The factory produces 2,100 loaves of bread a day, with flavor variants such as chocolate, strawberry, coconut, green bean, srikaya, and messes. In the production process, the mobile hot bread home factory still uses gas oven production tools or machines, in the midst of the development of equipment or Machines that use electrical energy are electric ovens. The aim of this research is to analyze the application of relevant costs in decision making to continue or replace a gas oven with an electric oven. This research methodology uses descriptive qualitative, with data collection through in-depth interviews, observation and data analysis. Decision making will always be related to the future so business owners need to review these decisions. To help business owners make the right decisions, by choosing the best decision alternative among the available decision alternatives. The results of this analysis show that the most appropriate decision is to continue using a gas oven because it obtains the most relevant total results and benefits rather than replacing it with an electric oven.

Analysis of Reasons of Breakage in Bag Production Machine for Agricultural Materials

Analysis of Reasons of Breakage in Bag Production Machine for Agricultural Materials

Analysis of project work using earned value management and precedence diagram method in manufacturing system projects

PT. Weiss Tech has received a project order for the fabrication and installation of coffee packaging production machines from PT.XYZ, scheduled to start on June 19, 2023, with a deadline of June 24, 2024, spanning 54 weeks. The project has been ongoing for 16 weeks with an update indicating that only 20% of the planned progress has been achieved, which suggests that it may not be completed on time. To evaluate the project's performance, the EVM method will be used to identify critical tasks and reduce the project duration using the Precedence Diagram Method with the help of MS Project software. The evaluation shows negative values for CV and SV variances, as well as performance index SPI<1 and CPI<1, indicating poor project performance. The critical tasks identified include electrical & instrument installation, machine & utility installation, trial work, assembly, quality control, polishing, milling, turning, cutting, and design. Design work has been identified as the most influential on the critical path. By rescheduling the project plan, positive values for CV and SV have been achieved. With the SPI value of 1.13 and CPI value of 1, it can be concluded that future project performance will be completed faster and within budget.

Pengendalian Kualitas Produk dengan Metode Statistical Quality Control untuk Mengurangi Produk Rusak pada UMKM Gethuk Anyar di Ngawi

MSMEs Gethuk Anyar is an industry operating in the culinary sector, the problem faced is the company's difficulty in maintaining the quality of the gethuk products it produces. This research aims to find out how quality control is implemented using statistical tools in an effort to control the quality and level of product damage at Gethuk Anyar MSMEs in Ngawi. Quality control analysis uses tools in the form of check sheets, cause and effect diagrams, Pareto diagrams and control charts. The results of the control chart analysis show that production is in an uncontrolled state. This can be seen from the control graph where the points fluctuate very highly and irregularly. Based on the Pareto diagram, priority repairs need to be made for the dominant types of damage, namely hard defects (59%), soft defects (27%), burnt defects (10%). From the analysis of the cause and effect diagram, it can be seen that the factors that cause damage originate from human/worker factors, work methods, production machines, work environment and raw materials so that companies can take preventive and corrective action in order to reduce the level of damage and improve product quality.

The supporting structure strength analysis of indexing stamping machine for manufacturing thin-walled inverting steel tubes

Abstract Due to the occurrence of cracks in thin-walled inverting steel tubes during the inverting process leading to fractures, thereby failing to achieve the desired energy absorption effect, they were often manufactured by interstitial stamping process with the manual rotation of thin-walled tubes to reduce stress concentration points, which is not conducive to mass production. Therefore it proposes a new design scheme of the indexing stamping machine for mass production of thin-walled inverting tubes. And the tube supporting structure of the indexing stamping machine will undergo shear stress and axial stress during manufacturing operation. To ensure that the machine can operate properly, ANSYS software is employed to conduct strength and stability analysis for the supporting structure. The calculation results indicate that the main supporting structure can meet the strength requirements of the thin-walled inverting tube forming machine. These study findings provide some references for optimizing the structure design for indexing stamping machines.

COCOFUEL: A Microcontroller-Based Biodiesel Production Machine

In the global landscape, the coconut stands out, with over 61 million tons harvested annually, yielding approximately 75 coconuts per tree annually. Recognizing its multifaceted benefits, including its suitability as a biofuel, various countries have endeavored to develop biodiesel production machines, each employing distinct methodologies and chemical compositions. Central to these efforts has been the drive to optimize production efficiency while minimizing costs, encompassing chemical inputs, procedural complexity, and energy consumption. This project undertook the design and implementation of a microcontroller-based machine tailored exclusively for biodiesel production via the transesterification method, utilizing coconut oil, methanol, and a catalyst. Adopting the Rapid Application Development framework, the project unfolded through iterative cycles, encompassing analysis, design, prototyping, testing, and refinement. The machine’s core functionalities were orchestrated by the Arduino Mega microcontroller, orchestrating the operation of washer motors, solenoid valves, and the heating rod, complemented by a relay, Bluetooth module, and voltage regulator. Thorough testing and evaluation underscored the machine’s reliability and efficacy in producing biodiesel from coconut oil, marking a significant stride toward renewable en ergy advancement. This accomplishment not only heralds progress in green energy solutions but also offers a compelling response to the environmental imperatives associated with conventional diesel fuels. The potential for reduced carbon emissions and the promotion of environmental sustainability amplifies the significance of this development, charting a course toward a greener future through the synergy of natural resources and cutting -edge technology.

Smart Industrial Internet of Things Framework for Composites Manufacturing.

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits.

Penataan Penjadwalan Preventive Maintenance Semua Mesin Milling Wheat Menggunakan Metode TPM (Total Productive Maintenance)

PT X is a factory that processes wheat into flour with roll mill as the main equipment. The wheat used is a blend of Indian wheat and Australian wheat. Indian wheat has a hard texture that can cause the roll grinding to become damaged or dull, increased working amperes,bearing jams, broken belts and motor overload which causes downtime. When a machine or component breaks down, the production process in the company will be disrupted and may come to a halt, so the target is not achieved and endangers to the company. To keep production machines and equipment working, maintenance processes are carried out properly and regularly. Additionally, the company can implement preventive maintenance steps to prevent and reduce machine downtime. Preventive maintenance is a planned maintenance strategy to prevent potential breakdowns. To reduce the high machine downtime, preventive maintenance is solution . The method chosen in this research is Total productive Maintenance. Based on research results, the overall equipment effectiveness (OEE) values were 85.8% in January, 87.18% in May, 86.69% in June, 85.25% in November, and 85.51% in December. The OEE values that did not meet the standard occurred in February (81.92%), March (81.34%), April (84.73%), July (83.77%), August (79.03%), September (77.05%), and October (81.72%). The biggest factor that causing losses of overall equipment effectiveness is caused by the high value of the six big losses, particularly the reduced speed losses, which stand at 9%.

Preventive Maintenance on CNC Machines Using the OEE Method to Reduce Downtime at PT. MTAT

This study examines the performance of CNC machines at PT MTAT Indonesia from January to March 2023. Monthly production data, machine uptime, defect rates, and non-productive periods were collected to assess Overall Equipment Effectiveness (OEE). This study aims to analyze the effectiveness of preventive maintenance of CNC machines at PT MTAT Indonesia using the Overall Equipment Effectiveness (OEE) method to reduce downtime. This study uses monthly data from January to March 2023, including production uptime, defect rates, and non-productive periods, to calculate OEE. The analysis showed that the CNC machines achieved an average OEE of 86.52%, surpassing the global standard of 85%, indicating high efficiency and quality. The study used Pareto analysis to identify the main causes of downtime, finding technical and maintenance issues as the main contributors. By addressing these factors, PT MTAT Indonesia can further improve machine efficiency and productivity. This study contributes to this field by providing a comprehensive analysis of CNC machine maintenance and proposing strategies for continuous improvement.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

Sistem Otomasi Untuk Menyortir Barang Pada Ruang Produksi Menggunakan Scada dan PLC

The development of the industrial revolution is progressing. Especially on production performance or machine parts. Industrial revolution 4.0 uses the internet and digitalization. So there are lots of new innovations. In a large-scale company, of course there are many tools and machines that need to be regulated whether they are on or not with regular supervision. With regular supervision, it is impossible for human workers to check one by one, thereby wasting time and reducing production levels. Therefore, it is necessary to develop a control system using PLC and HMI (Human Machine Interface) as the brain or control center for the company. In the industrial sector, PLC (Programmable Logic Controller) is an important factor in the operation of automatic machines in factories replacing relay control systems. So that PLC as a control system in the industrial sector can move machines according to needs. One part of the industry is the production room. The production room is the part where there are manufacturing materials or goods up to packaging. This research aims to design and implement a PLC (Programmable Logic Controller) and SCADA by adding sensors and actuators as output. The method used is a rule base system, the system runs according to a predetermined sequence and flow. The results of this research are the PLC, the SHT 20 temperature and humidity sensor can produce temperature and humidity values, the capacitive photosensor can detect items with 35 trials accurately but the distance is limited to only 27cm, the proximity sensor can only detect metal objects by attaching to the sensor and The actuator in the form of a stepper motor can run according to the commands given and is well integrated into the HMI (Human Machine Interface).

A self-adaptive co-evolutionary algorithm for multi-objective flexible job-shop rescheduling problem with multi-phase processing speed selection, condition-based preventive maintenance and dynamic repairman assignment

Production scheduling and maintenance planning are two interactive factors in modern manufacturing system. However, at present, almost all studies ignore the impact of unpunctual maintenance activities on the integrated production and maintenance scheduling since the unavailabilities of repairmen are dynamically changed, e.g., repairmen increase, decrease and their unavailable intervals update. Under these contexts, this paper addresses a novel integrated optimization problem of condition-based preventive maintenance (CBPM) and production rescheduling with multi-phase processing speed selection and dynamic repairman assignment. More precisely, (1) a novel multi-phase-multi-threshold CBPM policy with remaining-useful-life-based inspection and multi-phase processing speed selection is proposed to obtain some selectable maintenance plans for each production machine; (2) a hybrid rescheduling strategy (HRS) that includes three rescheduling strategies is designed for responding to the dynamic changes of repairman; and (3) an adaptive clustering- and Meta-Lamarckian learning-based bi-population co-evolutionary algorithm (ACML-BCEA) is developed to deal with the concerned problem. In the numerical simulations, the effectiveness of designed operators and proposed ACML-BCEA algorithm is first verified. Next, the superiority and competitiveness of the proposed CBPM policy and HRS are separately demonstrated by comparing with other CBPM policies and rescheduling strategies. After that, a comprehensive sensitivity analysis is performed to analyze the effect of optional range of processing speed, skill level of selectable repairmen and total number of processing phases, and the analyzing results show that these factors all have a significant impact on the integrated optimization.

Penjadwalan Pemeliharaan Mesin Produksi Menggunakan Reliability Centered Maintenance

CV. ABC is a furniture business that produces home equipment which includes all items, namely chairs, tables, cupboards, beds, nightstands, wooden stairs, etc. The aim of this research was to determine maintenance scheduling for production machines, namely wood cutting machines and drilling machines, and provide suggestions for improving the maintenance scheduling of production machines, namely wood cutting machines and drilling machines, which are more effective. The advantage of the Reliability Centered Maintenance (RCM) method is that it prioritizes the main action of preventive maintenance, namely preventing and minimizing the consequences of failures that arise, so that it can increase the reliability and safety of the equipment used. Failure Modes and Effects Analysis (FMEA) is a technique for evaluating the level of reliability of a system to determine the effects of failure of the system. Based on the results of data processing and research discussion regarding production machine maintenance scheduling using RCM at CV. ABC, a conclusion can be drawn, namely, the Cutting Machine has a breakdown frequency of 26 times with the Bearing component having the most breakdown frequency, namely 8 times, the highest Risk Priority Number (RPN) calculation is Solenoid component with a value of 105, Mean Time to Failure (MTTF) calculation with a value of 111.5385 and Mean Time to Recovery (MTTR) with a value of 0.769231. The drilling machine has a breakdown frequency of 24 times with the Drill Bit component having the highest breakdown frequency, namely 6 times, the highest RPN calculation is the Drill Bit component with a value of 105, MTTF calculation with a value of 120.8333 and MTTR with a value of 0.8333.

Operation risk assessment of Flexible Manufacturing Networks subject to quality-reliability coupling

In flexible manufacturing network (FMN), the growth of product types increases the diversity of interacting behaviors between machine reliability and product quality, and the product diversity and machine flexibility promote the explosion of processing routes and processing sequences, increasing the gap between current risk evaluation of manufacturing systems and engineering practice. To fill this gap, a novel operation risk assessment framework is constructed for the complex FMN. In detail, to model the reliability-quality interactions, a mathematical framework describing a complete manufacturing process involving "feedstock quality (Q1) - machine degradation (R) - product processing quality (Q2) - quality inspection status (I)" is proposed for complex FMN, solving the measurement of interacting behaviors between machine reliability and product quality in an FMN that suffers from both multiple process routes and multi-type products. Then, an effective evaluation framework is proposed to evaluate the connectivity risk of multiple process routes and the quality risk of multiple product types in a complex FMN, providing a concurrent solution for risk evaluation under both complex system structures and complex quality-reliability interrelationships. At last, a simulation experiment verifies the effectiveness of our proposed methods and reveals differences in the operation risk of different product types in an FMN.

Overview of AI-Models and Tools in Embedded IIoT Applications

The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings.

Fuzzy Logic in Risk Assessment of Production Machines Failure in Forming and Assembly Processes

Fuzzy Logic in Risk Assessment of Production Machines Failure in Forming and Assembly Processes

DESIGN A SINGLE SCREW EXTRUDER FOR POLYMER-BASED TISSUE ENGINEERING

In the area of tissue engineering, single screw extrusion (SSE) has gained attention due to its versatility and efficiency in fabricating polymer-based scaffolds. Furthermore, advancements such as the implementation of extrusion techniques and the integration of bioactive agents have significantly expanded the capabilities of SSE. This study aims to investigate the configuration of a custom-designed plastic extrusion for tissue engineering, highlighting its potential in fabricating suture technology for various regenerative biomedical applications. Furthermore, the challenges and future perspectives in SSE technology are discussed, with a focus on the need for additional research to optimize processing parameters, enhance structure bioactivity, and facilitate clinical usage. SSE provides precise regulation of structure morphology, mechanical properties, and porosity, which are critical factors that influence cell behavior and tissue regeneration. Overall, SSE holds great promise as a scalable and cost-effective manufacturing technique for producing polymer-based structures with tailored properties, advancing the field of tissue engineering towards effective clinical solutions. The paper provides a comprehensive overview of a filament extruder production machine that is capable of manufacturing high-quality filament sutures (FS) using thermoplastic materials, specifically bio-protein derived from human serum albumin. The main focus of the paper is to explain the design and operation principles of the filament extruder. The extruder is equipped with a die that can measure a range starting from 2.5 mm and going down to smaller scales. This allows for the extrusion of filaments with a diameter as small as 1.75 mm. Although the design of the extrusion apparatus closely resembles that of commercially available machines, the focus here is on its adaptability and cost-effectiveness for laboratory-scale production. Overall, the research contributes to advancing the understanding of extrusion processing technologies in the context of biomedical applications, with a specific focus on utilizing human serum albumin-derived thermoplastics for manufacturing FS.

Electrostatic charging of material webs in production machines and how to eliminate it

The elimination of electrostatic charges, especially on material webs is critical to preventing ignition hazards and personal injury hazards in industry. The use of discharge bars (called “ionizers”) is an important method for eliminating electrostatic charges.The possibility of electrostatic discharge in a production machine is real. The metrological detection of charges on material webs is also discussed and the special phenomenon of a super brush discharge is described. A useful arrangement for active ionizers is described and justified.

Surface optimization of trapezoidal thread spindles—tribological analysis and application

Trapezoidal threaded spindles are ideal for supporting heavy loads and are therefore frequently used in heavy-duty lifting systems. Both the forming thread rolling process and the machining thread whirling process can be used to manufacture these lead screws. In order to improve the low efficiency of trapezoidal screw drives, it is crucial to understand and minimize friction losses. As part of a cooperation project between the Institute of Production Engineering and Machine Tools (IFW) and Bornemann Gewindetechnik, research was carried out into adapting the whirling process to optimize the tribological properties of threaded spindles. The process developed makes it possible to reduce the coefficient of friction in trapezoidal screw drives and thus also the required torque. By using whirled microtextures, the friction loss can be reduced by 25.5% and adhesive wear can be significantly reduced.

Integrated operations planning model for the automotive wiring industry

An integrated operations planning model for automotive wiring companies is studied to improve synchronization between production activities and inventory flows. These combined factors are growing in significance as they drive the need to take proactive steps in manufacturing and distributing wiring materials within the supply chain. This involves anticipating the requirements of different automotive manufacturers and thereby guaranteeing a consistent, uninterrupted, and punctual provision of raw wiring materials. This support is vital for sustaining the ongoing manufacturing operations in the automotive sector. For this push flow system, the proposed operational model is based on integer linear programming, considering capacity and bill of materials constraints to determine production quantities, inventory levels, and machine sizing. Real-life data from the automotive wiring industry validates the effectiveness of coordinated production and inventory activities, resulting in significant lead time reductions of up to 60 %. These findings provide compelling reasons for automotive wiring partners to engage in joint operations planning.