Liming Wang

Bio: Liming Wang is an academic researcher from Shandong University. The author has contributed to research in topics: Grinding & End mill. The author has an hindex of 10, co-authored 39 publications receiving 275 citations. Previous affiliations of Liming Wang include Chinese Ministry of Education & Concordia University.

An improved cutting power model of machine tools in milling process

Abstract: Establishing a rapid, accurate, and practical energy consumption evaluation model for machine tools is essential to save energy and increase profits in the manufacturing industry. Relationships between spindle rotation speed, cutting parameters, material removal rate, specific energy consumption, cutting power, and material removal power were experimentally analyzed, and the limitation and the limitation and differences between several machine tools’ cutting power evaluation models were discussed. Cutting parameters or material removal rate were regarded as independent variable in those models. By comparing the models’ fitting and predicted results, it draw a conclusion that the model treating cutting parameters as independent variable had greater accuracy but depends on a large quantity of experimental data. The model treating material removal rate as independent variable can also obtain a good fit and reduce the number of necessary experiments. Therefore, it is a rapid method to estimate the cutting energy consumption of machine tools for the latter model. In addition, the paper puts forward an improved cutting power model, which considers the influence of the spindle rotation speed on the material removal power during the milling process. The proposed model can predict a milling machine’s cutting power more accurately.

A novel approach to determination of wheel position and orientation for five-axis CNC flute grinding of end mills

Abstract: In practice, the flutes of end mills are ground using CNC grinding machines via controlling the grinding wheel’s position and orientation to guarantee the designed flute parameters including rake angle, flute angle, helix angle, and core radius. However, for the previous researches, the designed flute profile was ground via building a specific grinding wheel with a free-form profile using two-axis CNC grinder. And, the free-form grinding wheel will greatly increase the manufacturing cost, which is too complicated to implement in practice. In this research, the flute-grinding processes were developed with a standard grinding wheel via five-axis CNC grinding operations. The mathematical representation of machined flute parameters was deduced in terms of the grinding wheel’s position and orientation. The geometrical constraints to avoid interference and abnormal flute profile for the five-axis CNC fluting were first developed in this work. Finally, the difference between the designed flute parameters and the machined flute parameters were formulated as a constrained optimization problem so as to determine the wheel’s position and orientation. The set of effective initial points for this optimization model was found mainly distributed the first quadrant of the contact area. The fminsearch function in Matlab toolbox was recommended to solve the optimization model due to its capability of handling discontinuity problem. The solution obtained in optimization model and the corresponding machined flute parameter were verified and compared with Boolean simulation in CATIA to confirm the validity and efficiency of the proposed approach. The results showed that the accuracy of machined flute parameters could achieve 1e-3 mm and 1e-2°, which satisfied the machining tolerance. This study provides a general solution for the CNC fluting operations and could be extended to grind complex surface of end mills in the future study.

Identifying best existing practice for characterization modeling in life cycle impact assessment

Abstract: Life cycle impact assessment (LCIA) is a field of active development. The last decade has seen prolific publication of new impact assessment methods covering many different impact categories and providing characterization factors that often deviate from each other for the same substance and impact. The LCA standard ISO 14044 is rather general and unspecific in its requirements and offers little help to the LCA practitioner who needs to make a choice. With the aim to identify the best among existing characterization models and provide recommendations to the LCA practitioner, a study was performed for the Joint Research Centre of the European Commission (JRC). Existing LCIA methods were collected and their individual characterization models identified at both midpoint and endpoint levels and supplemented with other environmental models of potential use for LCIA. No new developments of characterization models or factors were done in the project. From a total of 156 models, 91 were short listed as possible candidates for a recommendation within their impact category. Criteria were developed for analyzing the models within each impact category. The criteria addressed both scientific qualities and stakeholder acceptance. The criteria were reviewed by external experts and stakeholders and applied in a comprehensive analysis of the short-listed characterization models (the total number of criteria varied between 35 and 50 per impact category). For each impact category, the analysis concluded with identification of the best among the existing characterization models. If the identified model was of sufficient quality, it was recommended by the JRC. Analysis and recommendation process involved hearing of both scientific experts and stakeholders. Recommendations were developed for 14 impact categories at midpoint level, and among these recommendations, three were classified as “satisfactory” while ten were “in need of some improvements” and one was so weak that it has “to be applied with caution.” For some of the impact categories, the classification of the recommended model varied with the type of substance. At endpoint level, recommendations were only found relevant for three impact categories. For the rest, the quality of the existing methods was too weak, and the methods that came out best in the analysis were classified as “interim,” i.e., not recommended by the JRC but suitable to provide an initial basis for further development. The level of characterization modeling at midpoint level has improved considerably over the last decade and now also considers important aspects like geographical differentiation and combination of midpoint and endpoint characterization, although the latter is in clear need for further development. With the realization of the potential importance of geographical differentiation comes the need for characterization models that are able to produce characterization factors that are representative for different continents and still support aggregation of impact scores over the whole life cycle. For the impact categories human toxicity and ecotoxicity, we are now able to recommend a model, but the number of chemical substances in common use is so high that there is a need to address the substance data shortage and calculate characterization factors for many new substances. Another unresolved issue is the need for quantitative information about the uncertainties that accompany the characterization factors. This is still only adequately addressed for one or two impact categories at midpoint, and this should be a focus point in future research. The dynamic character of LCIA research means that what is best practice will change quickly in time. The characterization methods presented in this paper represent what was best practice in 2008–2009.

Life cycle assessment of cellulose nanofibrils production by mechanical treatment and two different pretreatment processes.

Abstract: Nanocellulose is a bionanomaterial with many promising applications, but high energy use in production has been described as a potential obstacle for future use. In fact, life cycle assessment studies have indicated high life cycle energy use for nanocellulose. In this study, we assess the cradle-to-gate environmental impacts of three production routes for a particular type of nanocellulose called cellulose nanofibrils (CNF) made from wood pulp. The three production routes are (1) the enzymatic production route, which includes an enzymatic pretreatment, (2) the carboxymethylation route, which includes a carboxymethylation pretreatment, and (3) one route without pretreatment, here called the no pretreatment route. The results show that CNF produced via the carboxymethylation route clearly has the highest environmental impacts due to large use of solvents made from crude oil. The enzymatic and no pretreatment routes both have lower environmental impacts, of similar magnitude. A sensitivity analysis showed that the no pretreatment route was sensitive to the electricity mix, and the carboxymethylation route to solvent recovery. When comparing the results to those of other carbon nanomaterials, it was shown that in particular CNF produced via the enzymatic and no pretreatment routes had comparatively low environmental impacts.

Illustrating Anticipatory Life Cycle Assessment for Emerging Photovoltaic Technologies

Abstract: Current research policy and strategy documents recommend applying life cycle assessment (LCA) early in research and development (R&D) to guide emerging technologies toward decreased environmental burden. However, existing LCA practices are ill-suited to support these recommendations. Barriers related to data availability, rapid technology change, and isolation of environmental from technical research inhibit application of LCA to developing technologies. Overcoming these challenges requires methodological advances that help identify environmental opportunities prior to large R&D investments. Such an anticipatory approach to LCA requires synthesis of social, environmental, and technical knowledge beyond the capabilities of current practices. This paper introduces a novel framework for anticipatory LCA that incorporates technology forecasting, risk research, social engagement, and comparative impact assessment, then applies this framework to photovoltaic (PV) technologies. These examples illustrate the potential for anticipatory LCA to prioritize research questions and help guide environmentally responsible innovation of emerging technologies.