Demand For Parts Research Articles

Study of the conductive ink wash-out during overmolding

This study explores the effect of the wash-out of conductive inks used during the in-mold electronic process. The influence of processing conditions and design parameters on wash-out areas during overmolding is analyzed. Using a comprehensive design of experiments approach, factors like the gate type, cavity thickness, melt temperature, injection rate, and mold temperature were evaluated. Three gating types, namely, edge, fan, and submarine, were considered. From the statistical analysis, it was demonstrated that the choice of gating type is the most important factor. Cavity thickness is the second most important factor for all three gating types. Reducing cavity thickness and raising melt temperatures generally led to a decrease in wash-out (WO), whereas the effect of injection rate varied depending on the gating method used. Adjusting mold temperature also produced favorable results, emphasizing the importance of ink-to-substrate adhesion. Simulation studies revealed the relationship between shear rate and wash-out area, underscoring the need to control injection parameters. These findings stressed the importance of meticulous design and processing to minimize wash-out defects, thus advancing in-mold electronic technology and meeting market demands for complex electronic parts.

PREDICTIVE TELEMETRY IN THE MANAGEMENT OF BUSINESS PROCESSES IN THE AUTO TRANSPORT INDUSTRY

The automotive industry is undergoing a significant transformation driven by advances in technology, particularly in IoT, big data analytics, and predictive telemetry. This research explores the development and implementation of predictive telemetry platforms for business process management in the automotive industry. Leveraging IoT devices and big data, these platforms analyze telemetry data from vehicles, enabling informed decision-making and improved management processes. A key focus is on how predictive telemetry enhances the development, safety, and performance optimization of autonomous vehicles, which rely on accurate, timely data for decision-making. By analyzing sensor data, these platforms forecast potential issues, ensuring the safety and reliability of autonomous systems while reducing downtime and enhancing performance. The study also examines the impact of predictive telemetry on customer experience and service personalization. By analyzing data on driving habits, vehicle usage, and performance metrics, companies can create tailored services, such as personalized maintenance schedules based on actual usage. Predictive analytics further informs the development of features and services aligned with customer preferences. In aftermarket processes, telemetry data streamlines operations like inventory management, warranty services, and customer support. For instance, predictive insights help anticipate parts demand, optimize inventory, and reduce costs. Early defect detection allows for proactive recalls or repairs, enhancing customer satisfaction and protecting brand reputation. The research incorporates case studies of companies that have implemented predictive telemetry platforms, highlighting challenges such as data integration, scalability, and cybersecurity. Despite these hurdles, the findings demonstrate substantial benefits, including increased efficiency, cost savings, and customer loyalty. In conclusion, integrating predictive telemetry platforms transforms business process management in the automotive sector. By leveraging IoT and big data, companies can make informed decisions to optimize vehicle performance and elevate customer experiences. This research emphasizes the importance of investing in predictive telemetry technologies to remain competitive in a data-driven industry.

Effect of ultrasonic assisted EDM based on horizontal vibration on deep and small hole machining

With the development of science and technology, industrial production requires more and more precise parts processing, precision processing has become a trend. In this trend. The traditional processing methods have been difficult to meet the market demand for micro-hole parts. Therefore, combining the characteristics of EDM and Ultrasound-assisted Machining, a new horizontal Ultrasound-assisted EDM system for deep and small holes is designed and applied in physical machining. The experimental results show that the horizontal ultrasonic vibration of workpiece can promote the efficiency and depth of EDM, and improve the electrode wear and the inner surface roughness of small holes. By comparing the processing effects of different materials, it is found that the processing efficiency and depth of pig iron are better than those of stainless steel. In conclusion, the horizontal ultrasonic vibration system established in this paper not only improves the processing effect of conventional EDM to a certain extent, but also does not affect the spindle feed and working fluid circulation of EDM machine tools. It enriches the research direction of ultrasonic assisted EDM and has broad market prospects.

Prediction of Power Equipment Emergency Repair Based on Adaptive Neural Network Fuzzy Inference Method

If the emergency repair prediction of power equipment is only made from the perspective of historical spare parts inventory data, it cannot reflect the impact of disaster-causing elements and disaster evolution on the demand for emergency repair spare parts in the future. Therefore, this paper aims to propose a reliable power equipment emergency repair prediction method, and constructs a demand prediction method for emergency repair spare parts of power equipment based on scenario analysis. Constructing a power equipment repair system through intelligent reasoning methods to improve the efficiency of power equipment repair. This article comprehensively uses methods such as literature analysis, model inference, and case simulation verification, this paper innovatively combines the adaptive neural network fuzzy inference system with expert experience. This paper validates the superiority of the prediction method constructed in this paper through comparative analysis. The results show that with the increase of the amount of data, the prediction accuracy of the method proposed in this paper will be improved, which can provide a reference for the subsequent emergency repair prediction of power equipment.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

ANALYSIS OF SPARE PART INVENTORY CONTROL USING ECONOMIC ORDER QUANTITY (EOQ) AND CONTINUOUS REVIEW METHODS

General Background: In the concrete manufacturing industry, the reliance on machines for production activities necessitates a robust spare parts inventory management system to ensure operational continuity. Specific Background: However, fluctuating demand for spare parts often leads to overstock or stockout situations, significantly impacting inventory costs and cash flow. Knowledge Gap: Existing studies primarily focus on static inventory management approaches, neglecting the dynamic nature of spare parts demand in manufacturing environments. Aims: This research aims to optimize total inventory costs while determining efficient order and reorder point quantities by integrating the Economic Order Quantity (EOQ) method with continuous review techniques. Results: The findings reveal an optimal order quantity of 131 units and a reorder point of 18 units, resulting in a total inventory cost of IDR 2,414,609,989—an efficiency improvement of IDR 293,152,400, equivalent to an 11% cost saving compared to previous inventory management practices. Novelty: This study innovatively employs a probabilistic approach to account for demand variability, enhancing the accuracy of inventory control measures. Implications: The outcomes suggest that implementing the proposed inventory management strategy can mitigate the risks of overstocking and stockouts, ultimately fostering improved financial performance in the company. Furthermore, the research highlights the necessity for regular inventory reviews and suggests future studies to develop more dynamic inventory control models that incorporate price fluctuations for spare parts, thereby addressing potential risks associated with cost variability.

Research and Application of Flexible Fracturing Manifold in Volumetric fracturing

Abstract In order to solve the problems of high pressure pipe valves in Volumetrictric fracturing, such as large demand of high pressure pipe and valve parts, high pressure manifold for fracturing complex connection, fracturing pipeline connections are complex, long fracturing construction preparation period. The economy and timeliness of pipeline connection seriously affect the speed and efficiency of Volumetrictric fracturing operations. This paper designed and developed the high pressure flexible composite pipe and carried out its performance evaluation test. Combined with Volumetrictric fracturing operation conditions, supporting functional modular manifold system, overcome flexible pipeline and rigid pipeline fast connection technology, flexible fracturing manifold system was formed. Through the large-scale field application in the unconventional reservoir Volumetric fracturing field, The flexible fracturing manifold is compared with the conventional high-pressure pipe valve connection and the single pipe universal connection, the flexible fracturing manifold can reduce the use of high-pressure pipe valves by 1/3, increase the efficiency of fracturing manifold connection by 50%, and reduce the labor intensity by 50%, enables efficient, reduce high pressure manifold connection points by 70%. The timeliness and safety of fracturing manifold connections have been greatly improved. Flexible fracturing manifold fundamentally change the connection mode of Volumetrictric fracturing high-pressure manifold, and realizes the speed and efficiency of reservoir transformation.

Methodology of Shipboard Spare Parts Requirements Based on Whole Part Repair Strategy

This paper introduces an assessment method for shipboard spare parts requirements based on a whole-part repair strategy, aimed at enhancing the availability and combat effectiveness of naval equipment. Addressing the shortcomings of traditional repair strategies, this study innovatively adopts a whole-part rotation repair approach to reduce repair times and improve the rapid response capability of equipment. An evaluation model for support probability and fill rate is established, and Monte Carlo simulation techniques are applied to simulate the impact of different maintenance strategies on spare parts demand and equipment availability. This study also conducts a sensitivity analysis of key parameters, including Mean Time Between Failures (MTBF), repair demand probability, and faulty part repair cycle, to assess their influence on spare parts requirements and equipment availability. The results indicate that the whole-part repair strategy can effectively reduce spare parts demand and enhance equipment availability. In conclusion, the whole-part repair strategy demonstrates a distinct advantage in shipboard spare parts management, optimizing inventory management while ensuring combat readiness. This research provides a novel analytical approach for naval logistics and maintenance planning.

Feasibility study on the micro-forming of novel metal foil arrays based on submerged cavitating water-jet impingement

With the increasing demand for metal foil parts with micro-array structures in micro-electromechanical systems (MEMS), it is urgent to explore a novel array micro-forming technology with high efficiency, low cost, and high flexibility. The cavitation water jet uses the high-energy shock wave generated by the collapse of the cavitation bubble group as the loading force. It has the characteristics of large range of action, uniform pressure distribution, and can realize large-area array micro-forming processing. To verify the feasibility of cavitating water jet array micro-forming, this paper performs cavitating water jet array micro-forming on 304 stainless steel foil with a thickness of 80 μm, and analyzes the variation of cavitation bubbles collapse impact zone with target distance. The effects of target distance and impact time on the forming quality of metal foil array micro-holes were studied from the aspects of forming depth, surface roughness, and thickness thinning rate. The results show that the forming depth and uniformity of the array micro-holes located in the cavitation collapse area increase with time. However, with the increase of target distance, the forming depth increases first and then decreases. When the target distance is L = 120 mm, the forming depth reaches the maximum. The surface roughness ( Ra) of the array micro hole is Ra = 1.54 μm. The thickness thinning rate of the micro-forming parts is between 2% and 10%, and the maximum thickness thinning rate in the die fillet area is 13.5%. This paper provides a novel processing method for manufacturing metal foil parts with micro array structure.

A convolutional neural network–back propagation based three-layer combined forecasting method for spare part demand

Accurate spare part demand forecasting for the key components of ships is one of the key factors to ensure normal ship operation. Large errors in demand forecasting not only bring challenges to the normal operation of ships but also cause an inventory backlog of key components and thus increase the operation and maintenance costs. A three-level spare parts combination prediction method based on historical data has been proposed, aiming to solve the problem of insufficient data in existing prediction methods. First, three types of individual direct forecasting models are used for predictions. Secondly, we used convolutional neural networks to perform convolutional operations on the prediction results, and then constructed a three-layer combined prediction model using backpropagation neural networks (BP). Experimental results have shown that the predictive performance of this model is significantly better than that of single-layer models. This study used spare parts data from shipping companies to predict the three-layer combination model, and the results fully demonstrated its significant advantages over single-layer models.

A physical demands analysis to identify and delineate critical physical tasks for physically demanding US air force occupations.

Military physical fitness tests and standards often lack a scientific basis. Contrary to this traditional-historical precedent, US Air Force researchers use a "Two-Tier" health (Tier 1) and performance (Tier 2) approach to develop evidence-based criterion physical fitness tests, standards, and training. This study specifically and only addresses a physical demands analysis, the first step in a six-step process to develop Tier 2 occupationally specific, operationally relevant physical fitness tests and standards, and training guidance. This study conducted a physical demands analysis to identify and delineate occupationally specific, operationally relevant critical physical tasks for eight physically demanding US Air Force occupational specialties. A comprehensive five part physical demands analysis reviewed duty tasks, conducted focus groups, assessed incumbents, observed operational tasks, and interviewed senior leaders to detail critical physical tasks. The physical demands analysis delineated duty tasks to critical physical tasks for Special Tactics (556 to 40), Pararescue (981 to 50), Tactical Air Control (415 to 44), Special Operations Weather (586 to 50), Explosive Ordnance Disposal (1107 to 39), Security Forces (1286 to 25), Fire and Emergency Services (802 to 39), and Survival, Evasion, Resistance, and Escape (1099 to 25). The study approach proved efficacious for conducting a thorough physical demands analysis to identify and delineate occupationally specific, operationally relevant critical physical tasks for eight US Air Force physically demanding occupations. Critical physical tasks provide basis for the next overall research process step, developing physical task simulations to link to predictive physical fitness tests and training guidance.

Life cycle assessment and multi-criteria decision-making for sustainable building parts: criteria, methods, and application

PurposeSustainable building design relies heavily on building parts, with crucial consideration for climate and environmental impact. Due to numerous criteria and diverse alternatives, employing multi-criteria decision-making (MCDM) to choose the best alternative is essential. Yet, relevant criteria and suitable MCDM methods for life cycle-based building planning still need to be determined. This study highlights prevalent environmental criteria and offers guidance on MCDM approaches for sustainable building parts.MethodsThis study introduces an innovative approach by integrating life cycle assessment and MCDM. This provides comprehensive decision support for planners. A systematic literature review identifies environmental criteria for building parts and is validated in expert workshops. Thus, the relevance of criteria across the building life cycle is established. Furthermore, the study analyzes MCDM approaches in the built environment. From this, the study employs and evaluates the Analytical Network Process (ANP) and Analytical Hierarchy Process (AHP) in a case study. Thereby, it offers insights into effective decision-making methodologies for sustainable building practices.ResultsThis research categorizes environmental criteria for building parts and buildings into emissions, energy, resources, and circularity. Among 26 building part-related criteria, the global warming potential is highlighted. While the AHP is widely used in MCDM, a standardized method in planning processes is yet to emerge. Applying the ANP and AHP reveals similar rankings for the best and worst alternatives in a case study focused on selecting the optimal ceiling structure. Ribbed or box slab ceiling constructions are favored over reinforced concrete and composite timber-concrete constructions.ConclusionsThis study presents a novel method for life cycle-based MCDM challenges, identifying key environmental criteria. While material correlations exist, evaluating building parts demands simultaneous consideration of multiple criteria. Future research aims to compare further MCDM methods regarding their applicability, transparency, and ranking to enhance decision-making in sustainable construction. These investigations are essential for refining decision-making processes in the built environment, ensuring effective and transparent sustainability planning approaches.

Finite element method based modeling of cutting forces and cutting temperature in micro-milling LF21

The demands for aluminum alloy LF21 micro precision parts are increasing in the fields of aerospace, high-tech electronic products, and the other fields. Micro-milling is an effective technology for machining small LF21 precision parts. Cutting forces and temperature are crucial factors in micro-milling process, directly affect tool vibration, tool wear, and surface quality of the workpiece, and even result in large deformation of the tool and workpiece. Direct measurement of cutting forces during micro-milling requires high-precision and expensive instruments. Moreover, due to the small cutting area in micro-milling, it is challenging to achieve accurate measurements of cutting area temperature. Therefore, accurate prediction of cutting forces and temperature in micro-milling is urgent and challenging. Nowadays, there are few studies on prediction of cutting forces in micro-milling LF21. The study on prediction of temperature in micro-milling LF21 is still blank. To solve the above problems, this paper proposes a finite element method based on modeling for prediction of cutting forces and temperature in micro-milling LF21. ABAQUS software is adopted. First, the geometry models of the micro-milling tool and workpiece are established. Then, the assembly and mesh division of the established models are completed. Johnson-Cook constitutive model and Johnson-Cook damage criteria are used to describe the material constitutive relationship and chip separation criteria, respectively. The suitable tool-workpiece friction models are determined. Finally, the simulation of the micro-milling LF21 process is achieved. Experiments of micro-milling LF21 are conducted and the cutting forces are measured using the dynamometer. The validity of the built process simulation model and the correctness of the cutting force prediction results are verified by the comparison of experiment and simulation cutting forces. Then, the prediction of temperature is achieved based on the verified process simulation model of micro-milling LF21.

Optimal Pricing and Retailing Strategy for an Assembled Product Manufacturing–Remanufacturing Process under Carbon Emission Regulations and Autonomation

Online-to-offline (O2O) retailing offers unique opportunities for customizable assembled products with spare parts. Customers can browse and configure their desired product online, selecting from various components. Imperfect production, where a certain percentage of products have defects, can be amplified in the manufacturing system. Stricter carbon emission regulations put pressure on manufacturers to minimize waste. This creates a tension between discarding imperfect products, generating emissions, and potentially offering them at a discount through the O2O channel, which could raise quality concerns for consumers. In this study, an imperfect single-stage production process is examined, incorporating manufacturing–remanufacturing within a single stage for assembled products containing various spare parts. The study explores an investment scenario aimed at enhancing the environmental sustainability of the product. Additionally, two carbon emissions regulation strategies, specifically carbon cap-and-trade regulation and carbon taxation, are evaluated for their effectiveness in mitigating carbon footprints. The identification of waste, particularly in the form of defective items, is achieved through automated inspection techniques. The demand for spare parts associated with the assembled products is intricately linked to the selling prices set across diverse channels. Finally, the total profit of the manufacturing system is maximized with the optimized value of the selling prices, order quantity, backorder quantity, and investments in autonomated inspection, setup cost, and green technology. Numerical illustrations show that system profit was optimized when the defective rate followed a triangular distribution under carbon cap-and-trade regulation and when green technology investment helped to enhance retailer profit by 18.12%, whereas autonomated inspection increased retailer profit by 10.27%.

Research Progress and Prospect on Profile Roll Forming Methods and Equipment

Abstract: In recent years, with the development of aerospace, automotive, rolling stock, shipbuilding, military weapons and other industries, the demand for bent and rolled parts is increasing, and the requirements for roll forming accuracy are growing. With the rapid development of coal power, hydropower, nuclear power, wind power, and petrochemical industries, the design and research on roll bending machine and the profile roll forming methods have come into focus. The purpose of this study is to summarize the classification and characteristics of roll bending equipment, analyze the reasons that affect the accuracy of profile bending, and propose specific solutions. This paper summarizes the research status and patents on the existing roll bending equipment, introduces its structural characteristics, differences, and molding effects, and focuses on the difficulties, optimization, and improvement methods of roll bending molding. In this paper, the mechanical structures of the existing roll forming equipment have been analyzed and compared, the effects of different roll forming equipment on the profile bending results have been determined, and the reasons affecting the bending accuracy have been pointed out. By summarizing the patents and research on profile roll forming methods and equipment, the current situation and future research prospects of roll forming equipment are discussed. The roll forming equipment is divided into a roll bending machine, a CNC roll bending machine, and a flexible roll bending machine. Each design can meet different profile processing needs. From the design point of view, the optimization under the given processing profile can achieve relatively high processing accuracy. However, the processing of different profiles will still produce geometric defects, and there is some space for improvement in the structure of roll forming equipment, the materials of processing rollers, and the NC interactive system. Thus, further research on profile roll forming is needed in the future.

Enhancing Spare Parts Management through Support Vector Regression: A Case Study in the Service and Maintenance Industry

This study investigates the effectiveness of Support Vector Regression (SVR) in accurately predicting spare part usage within the service and maintenance industry, with a specific focus on coolers and freezers. Leveraging historical data from Hutama spanning January 2019 to December 2023, the SVR model successfully forecasts future spare parts demand with remarkable precision. Through rigorous parameter tuning using the grid search with optimization method, the SVR model achieves optimal performance, yielding reliable predictions with MAPE 8.55% and RMSE 9.43. Despite its effectiveness, limitations include the study's narrow focus on coolers and freezers within the service and maintenance sector, potential influences of external factors on prediction accuracy, and assumptions regarding the linear or nonlinear patterns in spare parts usage data.

OPTIMIZATION OF THE PROCESSES OF PRODUCTION OF SPARE PARTS FOR MOTOR VEHICLES

The purpose of the research is to develop a software simulation multi-product model designed for effective inventory management of spare parts. In the course of the work, a mathematical model was used to optimize the production and storage of spare parts for vehicles. At the same time, such significant factors as the total number of spare parts in stock, the frequency of failure of certain parts, operating conditions, the intensity of their use, as well as the influence of the human factor were taken into account. The results show that penalties for shipping and dynamic spare parts consumption significantly affect the cost function, while penalties for missing necessary parts significantly affect the minimum inventory level. The results can be applied to the management of spare parts stocks at authorized service centers. The study concludes that seasonal fluctuations in demand for spare parts can be eliminated by creating a scientifically sound spare parts inventory management system.

Global wildlife trade and trafficking contribute to the world’s nonhuman primate conservation crisis

A growing global human population, habitat conversion, and the indiscriminate exploitation of natural resources have created unsustainable demands on nature, resulting in widespread biodiversity loss. Primates, which represent the third most specious Order of mammals, are facing an extinction crisis. Currently, 69% of primate species are listed by the IUCN as threatened (Vulnerable, Endangered, or Critically Endangered) and 94% have declining populations. Here, we examine two primary threats to primate population persistence, namely the commercialized hunting and capturing of wild primates and their body parts for food, traditional medicine, pets, and use in biomedical research. Both the legal wildlife trade and illegal wildlife trafficking represent multibillion-dollar industries that contribute to primate population decline, a reduction in genetic diversity, and local extirpation. Trade and trafficking also can lead to the emergence of infectious diseases, increasing biosecurity risks to humans. Between 2015 and 2021, CITES reported 337,511 live primates representing at least 99 species were legally traded, with 6.5% sourced directly from the wild. The recent indictment of Cambodian officials for allegedly laundering wild-caught long-tailed macaques into the U.S. by labelling them as captive-bred, highlights the need for greater transparency and accountability. Comprehensive data on the illegal trafficking of primates are extremely difficult to obtain. However, between 2009 and 2017, primates accounted for 20% of all seizures of illegally traded mammals in the air transport sector. International wildlife trafficking is dominated by criminal networks, corruption, and driven by the demands of wealthy consumers. In addition, the internet has expanded international opportunities to connect buyers and sellers of wild-caught primates and their body parts. Despite explicit bans on selling endangered primates, social media sites continue to do so. Moreover, data on the global food security index (GFSI) indicate that as the international demand for wild live primates, their meat, and other body parts has continued to increase, the majority of people in primate range nations have remained food insecure. Given that almost 70% of primate species are negatively impacted by hunting and trapping, we offer a set of recommendations to reduce the trade and trafficking of wild primates.

Implementation of the Fp-Growth Algorithm on Spare Parts Supply Requests

Manufacturing companies rely on machines for operational activities to produce finished goods. Common factors constraining the demand and supply of spare parts are the high number of spare parts managed and irregular patterns of demand for spare parts. These varying quantities also require investment in spare parts inventory and longer response times than predicted. The research aims to apply the FP-Growth algorithm approach to find association rules and produce patterns of demand and supply of spare parts in lightweight brick manufacturing companies based on transaction data on demand and supply of spare parts from January – March 2023. The approach used is associated with the applied algorithm. In this research, the primary process of the FP-Growth algorithm is to create a combination of each item until no more combinations are formed using minimum support and minimum confidence parameters. Based on the results of making association rules using spare parts demand data from the machine maintenance department, it is stated that the regulations formed from processing the RapidMiner application with a confidence value of 100% recommend FD Regular Bolt spare parts, then the next rating with a confidence value of 94% is Steel Nuts, seven rules recommend Nuts. Steel. Therefore, it is recommended that FD Regular Bolts and Steel Nuts carry out safety stock to maintain stock availability and place them on shelves included in the fast-moving inventory category.

Modeling of laser-assisted micro-milling Inconel718

Nickel-based superalloy Inconel718 is a typical difficult-to-machine material with the characteristic of high strength and hardness. Demands for micro parts of nickel-based superalloy Inconel718 are growing in aerospace, biomedical and other fields. Laser-assisted micro-milling (LAMM) can realize precision machining of the workpiece because of its lower cutting force. However, the machining mechanism and the change law of cutting force during LAMM of nickel-based superalloy Inconel718 are still unclear. There is a crucial need for modeling of LAMM Inconel718. This paper explores LAMM of nickel-based superalloy Inconel718 and realizes the simulation of LAMM process. A three-dimensional transient heat transfer finite element (FE) model is established to predict the temperature distribution of a Gaussian heat source in LAMM. Multiple sets of simulations of laser pre-heating with various laser power are conducted to analyze the temperature fields of workpiece and determine the appropriate laser parameters. A novel 3D model of the full machining process of LAMM Inconel 718 is constructed by ABAQUS with multi-subroutine, a fully coupled thermal-mechanical process is achieved. A modified Johnson-Cook constitutive model is used to reflect the size effect and flow stress of LAMM Inconel 718. Finally, the validity of the models is verified by experiments. Based on the verified model, the appropriate laser pre-heating parameters are obtained, and the prediction of cutting forces of LAMM Inconel718 is achieved.