Production Cycle Time Research Articles

An Optimization Method for Multi-Robot Automatic Welding Control Based on Particle Swarm Genetic Algorithm

This paper introduces an optimization method for multi-robot automated control welding based on a Particle Swarm Genetic Algorithm (PSGA), aiming to address issues such as high costs, large footprint, and excessive production cycles in multi-robot welding production lines. The method first constructs a multi-axis robotic kinematic model to provide constraint conditions. Then, the PSO (particle swarm optimization) algorithm, which integrates penalty functions into the fitness evaluation, is used to determine the optimal welding path by simulating collective behavior within a group. The GA (genetic algorithm) encodes the position of the welding robot bases into chromosomes to find the optimal layout for coordinated control of multiple robots. The entire process is optimized according to welding standards and requirements. Additionally, a comprehensive production line performance estimation model was used to quantitatively analyze the new scheme. The results show that the optimized production line’s balance rate increased by 10%, the balance loss rate decreased by 10%, the smoothness index increased by 37.8%, the space costs reduced by 44.4%, the equipment demand reduced by 41.1%, the labor demand reduced by 50%, the total costs reduced by 10%, and the average product cycle time was reduced by 5.07 s. Finally, we tested the algorithm in various complex scenarios and compared its performance against mainstream algorithms within the context of this study. The results demonstrated that the optimized production line significantly improved efficiency while maintaining safety standards.

Cooperative multi-agent reinforcement learning for multi-area integrated scheduling in wafer fabs

The existing scheduling methods of wafer fabs focus on single area, achieving local optimisation while failing to realise global optimisation due to neglecting the coordination of multi-area. Therefore, it is necessary to consider the complex opposing relationships between multi-area caused by constraints such as batch processing, re-entrance, and multiple residency times within and between areas to conduct integrated scheduling and shorten the production cycle time. For this issue, this paper proposes a cooperative multi-agent reinforcement learning for multi-area integrated scheduling. Aiming at the dynamic batching and scheduling considering the dynamic arrival lots in multi-area, a multi-agent reinforcement learning algorithm is presented to learn the optimal dynamic batching and scheduling policy firstly. Subsequently, a cooperative multi-agent framework is raised to achieve the global optimisation and coordination of multi-area. Furthermore, an adaptive exploration strategy is constructed to enhance the global exploration capability of the complex solution space caused by residency time constraints and re-entrant property. Moreover, a policy share enhanced Double DQN is employed to improve the generalisation and adaptability of the multi-agent. Finally, the experiments demonstrate that the proposed integrated scheduling method has better comprehensive performance compared to the previous area-separated scheduling methods.

Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

Optimizing Quality Control on Electric Vehicle Production Lines with AI and Machine Learning

The study explores the use of AI and ML technologies to optimize quality control in EV production lines. By applying neural networks and machine learning algorithms, the research achieved significant improvements: a 74.4% reduction in torque deviation, a 75.9% enhancement in speed consistency, and a 70.8% decrease in defect rates. These gains also resulted in a 16.0% reduction in production cycle time and a 50.0% decrease in downtime, leading to an 8.4% increase in Overall Equipment Effectiveness (OEE). The methods employed included AI-driven predictive maintenance, real-time monitoring, and statistical process control (SPC). Despite the clear benefits, challenges such as integrating these technologies with existing systems and ensuring robust data infrastructure remain. Future research should focus on refining these approaches and extending their application across the automotive industry.

Optimal Path Planning Algorithm with Built-In Velocity Profiling for Collaborative Robot.

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm.

Measuring process innovation outputs and understanding their implications for firms and workers: Evidence from Pakistan

New processes significantly affect firms and workers; however, due to a lack of quantitative metrics, our understanding of the measures, determinants, and impacts of new processes remains limited. Drawing on unique data from Pakistan, we analyze five different metrics of process innovation output: cost reduction, defect rate reduction, reduction in production cycle time, increase in production capacity, and improvement in product quality. We find that the breadth and depth of innovative capabilities, level of competition, and availability of market sources of knowledge are important inducers of process innovation, and that smaller firms are more likely to introduce new processes and are better able to transform them into higher output. All five process innovation outputs are associated with higher labor productivity and sales expansion; however, there is considerable heterogeneity in the size of these impacts. We did not find that adopting new processes led to lower employment.

Line Balancing Study Using Value Stream Mapping Tool on Lean Manufacturing: A Case Study in an Electronic Industry

XY Electronics, a leading international company in electronic components manufacturing, is confronting significant production constraints that adversely affect output, lead times, and operational expenses. This study examines the manufacturing line for product A using Value Stream Mapping to analyze process times and identify bottlenecks where takt times are exceeded. It focuses on areas surpassing production cycle times and aims to enhance line utilization through better line balancing and waste reduction. The results reveal that the header assembly, along with coplanarity and pre-testing 3, are major bottlenecks, which significantly impact productivity. By optimizing task allocation, refining workforce distribution, and employing cross-training, the production line efficiency improved significantly. In addition, strategic workforce reallocation and station optimization were crucial in addressing resource underutilization and enhancing overall operational efficiency

Design of a Wind Tunnel Nozzle Section Trolley Control System Based on Backstepping Method

Abstract To accommodate different types of supersonic wind tunnel tests, frequent replacements of nozzle sections and test sections are required, involving multiple complex technical processes. To optimize operational procedures and improve efficiency, a control system for the wind tunnel nozzle section trolley based on the backstepping method was designed. The system uses radio frequency identification (RFID) signals for station positioning and intelligently adjusts the speed and direction of the control motor, thus driving the trolley to move back and forth automatically. Through the simulation of the motor motion control model, the system effectively reduces random disturbances in the motion system caused by load changes, achieves precise positioning of wind tunnel plugins and nozzle assemblies, enhances system operating accuracy, reduces production cycle times, and realizes a one-button positioning function for all components, playing a significant role in improving wind tunnel test efficiency and operational quality.

Robotics in Manufacturing: A Review of Advances in Automation and Workforce Implications

The integration of robotics in manufacturing processes has undergone significant advancements, reshaping the landscape of industrial production and introducing transformative changes. This review explores the latest developments in robotics within the manufacturing sector, shedding light on the technological breakthroughs and their implications on the workforce. The recent surge in robotics adoption in manufacturing has been driven by the pursuit of increased efficiency, precision, and cost-effectiveness. From traditional robotic arms to cutting-edge collaborative robots (cobots) that work alongside human operators, the manufacturing industry has witnessed a paradigm shift. Advanced robotic systems equipped with artificial intelligence (AI) and machine learning algorithms enable adaptive and autonomous decision-making, further enhancing their capabilities. Automation in manufacturing, fueled by robotics, has yielded numerous benefits such as improved product quality, reduced production cycle times, and increased overall productivity. The review delves into case studies and real-world applications where robotics have proven instrumental in optimizing manufacturing processes across diverse industries. However, as the manufacturing landscape evolves, the implications for the workforce cannot be overlooked. The integration of robotics poses challenges and opportunities for human workers. While automation has the potential to eliminate routine and hazardous tasks, it also necessitates upskilling and reskilling of the workforce to operate, program, and maintain robotic systems. The review examines the socio-economic impacts of increased automation, discussing potential shifts in employment patterns and the need for a strategic approach to workforce development. This review provides a comprehensive analysis of the advances in robotics within manufacturing and their profound implications on the workforce. Striking a balance between automation and human collaboration is crucial for the future of manufacturing, emphasizing the importance of proactive strategies to harness the benefits of robotics while ensuring a resilient and adaptable workforce.

An Internet-of-Things-Based Dynamic Scheduling Optimization Method for Unreliable Flexible Manufacturing Systems under Complex Operational Conditions

The dynamic scheduling problem (DSP) in unreliable flexible manufacturing systems (UFMSs) with concurrency, conflicts, resource sharing, and sequential operations is a complex optimization problem that requires the use of efficient solution methodologies. The effectiveness of scheduling UFMSs relies on the quality of equipment maintenance. Currently, UFMSs with consistently large queues of parts awaiting service employ a repair-after-failure approach as a standard maintenance procedure. This method may require unexpected resources, incur costs, consume time, and potentially disrupt the operations of other UFMSs, either partially or fully. This study suggests using a predictive maintenance (PdM) strategy that utilizes the Internet of Things (IoT) to predict and avoid early mechanical equipment failures before they happen in UFMSs, thereby reducing unplanned downtime and enhancing reliability. Therefore, the objective of this paper is to construct timed Petri net (TPN) models using the IoT for the PdM configuration of mechanical equipment in the dynamic scheduling problem of UFMSs. This necessitates that users represent the specific problem using TPNs. The process of PN modeling requires the utilization of domain knowledge pertaining to the target problems as well as to machine information. However, it is important to note that the modeling rules for PNs are straightforward and limited in number. Consequently, the TPN model is applied to generate and formulate mixed-integer linear programming (MILP) instances accurately. This is done to identify the optimal production cycle time, which may be implemented in real-life scenarios. Several UFMS instances are used to demonstrate the applications and effectiveness of the proposed method. The computational results demonstrate that the proposed method shows superior solution quality, effectively solves instances for a total of 10 parts and 6 machines, and achieves a solution in a reasonable CPU time.

Peningkatan Produktivitas Produksi Sandal Jepit UD. Darmaji Melalui Analisis Kebutuhan Pegawai

High demand for sandals at UD. Darmaji. However, production cycle time is still long. This study determines the number of employees needed to meet accurate production capacity demand. The study used a quantitative approach with a motion study approach. The study population was the entire activity at UD. Darmaji. The sample was 8 activities in the sandal production process. The research instruments were observation forms and stopwatches. Data were collected through observation and time measurement. The data were analyzed using the motion study method to calculate cycle time, allowance time, performance rating, standard time, employee prediction, and cost comparison. UD. Darmaji has enough production capacity to meet demand for the next 6 months. However, the production cycle time is still long, especially in the activity of attaching the strap. To meet demand for the next 1 month, UD. Darmaji can use alternative 2 with 14 regular time employees for 9 hours per day. This alternative is cheaper and can improve efficiency and production productivity

OPTIMASI PERSEDIAAN BAHAN BAKU DAN PRODUKSI USAHA GANEPO PUTRI YOSE DENGAN MENGGUNAKAN ALGORITMA GREY WOLF OPTIMIZER

Putri Yose's Ganepo business, located in Padang Kandi seven koto talago, is one of the Rubik Ganepo producers that has grown to be one of the main suppliers in the Bukittingi and Riau areas. Interviews with the business owner revealed problems in inventory management that affect the profitability of this business. This study aims to assess and improve inventory management with a focus on raw material procurement and production cycle time. The model used in this study is Integrated Procurement Production (IPP) with the Grey Wolf Optimizer (GWO) optimization algorithm to determine the decision variables, namely the frequency of raw material procurement and production cycle time. GWO algorithm is chosen because it is a metaheuristic optimization method that is able to overcome real-world problems, avoid stagnation in solutions of local, and has a wide search space. The results showed that the IPP model with GWO was able to provide a new inventory policy, including the optimal procurement quantities of salt, cooking oil, garlic, turmeric, and production cycle time. The implementation of the policy increased revenue by 0.309203%.

OPTIMAL PRICING AND PRODUCTION LOT SIZE FOR TWO RATES OF PRODUCTION WITH PRICE-SENSITIVE DEMAND, PRICE BREAK-EVEN POINT, AND PROFIT MAXIMISATION IN HIGHER ORDER EQUATION

In the present study, optimal pricing and optimal lot size production policy models with price-sensitive demand of deteriorating products are considered, taking into account two distinct production rates. It is possible to begin production at one rate and then switch to a different rate after a period of time. Such a scenario is appealing, in that a big initial stock of produced goods can be avoided by starting production at a modest pace, thus reducing the initial investment and the holding cost. Further, the fifth-order equation is obtained when the equation for optimal pricing is derived. Maximising the profit is calculated based on a fifth-order equation. Both optimal pricing and production lot size are decision variables, and optimal cycle time is also one of the decision variables for determining price break-even points. As far as information is concerned, no researcher has examined optimal pricing and production lot size policies in two-rates-of-production models for their study.The objective of the present study is to examine the optimal production, optimal pricing, and optimal cycle time to reduce the total cost and to maximise the total profit. Both price break-even point and profit maximisation are considered. An appropriate mathematical model is developed. An illustrative example is provided and numerically validated using a sensitivity analysis. Microsoft Visual Basic 6.0 was used to code the model’s outcome validation.

Surface quality prediction for FDM-printed parts using in-process material flow data

Efficient and sustainable manufacturing practices rely on the early detection and removal of faulty components in production processes. In the context of Fused Deposition Modeling, this means that identifying defective parts during the production cycle can help to minimize waste and optimize resource utilization. However, conventional quality control methods, which involve post-process inspection, can be time-consuming and inefficient, particularly if nonconforming parts are detected after the production cycle is complete. To address this issue, a real-time quality prediction system has been developed that utilizes in-process flow sensor data to detect and identify nonconforming parts as they are being produced. The system was tested on cuboid test specimens, which were deliberately modified to include defects on the surface of the part. By analyzing the sensor data in real-time, the system was able to identify the defective parts and provide corrective actions to minimize waste and optimize resource utilization. By implementing this approach, manufacturing processes can be streamlined and resource utilization can be optimized while minimizing the production cycle time. This approach represents a significant advance over traditional quality control methods, which rely on post-process inspection and human factors.

A single-vendor multi-buyer inventory model with multiple raw material and quality degradation: A case study on agri-food industry

Managing degraded-quality materials in a supply chain with a single-vendor and multi- buyers is prevalent and critical in modern industry. This research addresses the single-vendor multi-buyer problem by incorporating multiple raw materials and quality degradation to maximize joint total profit (JTP). The proposed model utilizes the Whale Optimization Algorithm (WOA) for optimization, with empirical data derived from an agri-food industry case study in Indonesia. Experimental findings demonstrate the superior effectiveness of WOA compared to Chim optimization algorithm (ChOA), Genetic algorithm (GA), (GWO), Particle Swarm Optimization (PSO), and heuristic algorithm in solving the single-vendor multi-buyer problem. Sensitivity analysis reveals noteworthy insights: increased demand boosts total profit and production cycle efficiency, while elevated shipping costs for finished products negatively impact JTP by reducing production efficiency. Moreover, escalating quality degradation rates decrease JTP and production cycle time. This study significantly contributes to optimizing single-vendor and multi-buyer operations by addressing multiple raw materials and quality degradation, offering valuable insights into sensitivity analysis for critical factors in the agri-food industry.

Effects of Welding Parameters on Droplet Transfer and Bead Geometry in Laser Welding with Aluminum and Stainless Steel Filler Wires

Laser welding is increasingly used in industries because it allows high production cycle time and single-sided welding, but is characterized by a high sensitivity to joint preparation. By inserting filler wires, the sensitivity to joint preparation can be suppressed. In this study, the effect of welding variables on the bead geometry and melted droplet transfer were investigated. Two wires with different thermal diffusivities and densities were selected: stainless steel (STS) and aluminum (Al) wires. According to the wire feeding angle, the melting area tended to differ between the STS and Al wires. This implied that the wire feeding angle had to consider the absorption rate of the material for the laser wavelength. With an increase in the distance between the wire tip and the substrate, the dominant force acting on the droplet was changed from surface tension to gravity. Therefore, the distance was considered as a major factor in determining the droplet transfer mode. In all cases, the droplet transfer period exhibited relatively small deviation when the wire feeding direction was leading. Furthermore, the focal length effects on droplet transfer were irrelevant.

Effect of interface temperature on low‐velocity impact response of injection over‐molded short/continuous fiber reinforced polypropylene composites

AbstractInjection over‐molded short/continuous fiber reinforced composites are a unique class of materials for fabricating lightweight components having complex geometries in automotive manufacturing due to their higher specific mechanical properties, lower assembly cost, and short production cycle time. The influence of interface temperature on the low‐velocity impact response of short/continuous fiber‐reinforced polypropylene was experimentally investigated by varying the melt temperature. The over‐molded specimens fabricated at an interface temperature considerably higher than the melting point of polypropylene exhibited a 12% enhanced peak load with less impact damage compared with specimens over‐molded at a lower interface temperature. Optical analysis of impacted specimens over‐molded at lower interface temperature revealed interface debonding and insert delamination. Whereas insert delamination alone was observed for the specimens fabricated at higher interface temperature. The specimens over‐molded at high interface temperature exhibited less impact energy absorption and high compression strength after impact because of their strong interfacial adhesion.Highlights Impact energy absorption is less if melt temperature is high in injection over‐molding. Higher melt temperature enhances interface bonding by raising interface temperature. Interface debonding and insert delamination are the dominant impact failure modes. The compression strength after impact has increased with higher melt temperature. Impact and compression strength after impact is governed by the interfacial strength.

Evaluation method of filling difficulty for profile ring rolling cavity based on FEM and RSM

During the rolling process of profile ring, the design of the rolling cavity directly affects the forming quality of the final ring. In order to improve the cavity filling rate and reduce the production cycle time, this paper proposes a general evaluation method of the filling difficulty for profile ring rolling cavity by combining the response surface method(RSM) and the finite element method(FEM) with the step-type ring as the research object. Firstly, a universally applicable design method for profile ring rolling cavity is proposed, and based on the description of the rolling cavity, three design variables and a corresponding response objective are defined to achieve multi-objective optimization. Secondly, a finite element model of this step-type ring rolling process is established, and the response model is built and improved by significance analysis and analysis of variance(ANOVA) based on the forming simulation results. Then, the effects of different variables on the optimization objectives are comprehensively investigated by this response model. Finally, the evaluation method of the filling difficulty for profile ring rolling cavity is obtained by combining the contour plot of the response objective.

Optimizing Injection Molding Parameters to Cycle Time of Bioring Cone Cup Products with Taguchi Method

Optimizing Injection Molding Parameters to Cycle Time of Bioring Cone Cup Products with Taguchi Method

Magnetic non-destructive evaluation of microstructural and mechanical characteristics of hardened AISI H13 die steel upon sustainable grinding

This paper describes the potential of non-destructive techniques such as micro-magnetic Barkhausen noise (MBN) to assess the grinding burn flaws in terms of microstructural and mechanical indices of hardened AISI H13 die steel. To overcome this grinding burn issue, minimum quantity lubrication grinding with eco-friendly machining fluids like paraffin oil (PO) and castor oil (CO) was adopted and compared its performance with well-established conventional grinding like wet and dry. Ground surface integrity with castor oil (CO) was found to be significantly improvement in surface quality including lower surface roughness (Rα: 0.23 μm and Rz: 1.83 μm) with uniform surface topography, absence of oxidization and carbonization film, and negligible change in microhardness and microstructure. The reason is strong and uniform anti-friction and anti-wear tribo-film was produced by castor oil, which minimizes heat penetration through limited ploughing and rubbing action. The MBN outcome i.e., root mean square (RMS) has followed a linear relation with microhardness and temperature of the ground sample with a correlation coefficient of 0.993 and 0.981. Besides, lower amplitude and wide peak envelope get shifted towards a higher magnetic field in the MQL-CO grinding caused by poor thermal softening effect on the ground sample, which restricts magnetic domain wall movement and rotation. These outcomes could guide tooling industries for efficient sustainable grinding with faster thermal damage assessment, which helps to minimize production cycle time and back to clean environment.